MXPA06006231A - Serum biomarkers for chagas disease - Google Patents

Abstract

The present invention provides protein-based biomarkers and biomarker combinations that are useful in qualifying Chagas disease status in a patient. In particular, the biomarkers of this invention are useful to classify a subject sample as infected with Chagas disease or non-infected. The biomarkers can be detected by SELDI mass spectrometry.

Description

SERUM BIOMARKERS FOR CHAGAS DISEASE BACKGROUND OF THE INVENTION American trypanosomiasis (Chagas disease) is a protozoan infection caused by the flagellate Trypanosoma (Schizotrypanum) cruzi, widespread in America and endemic in Central and South America. Chagas disease can be rapidly fatal, especially in children or can be transported asymptomatically for decades. Between 10-30% of infected people inevitably develop severe cardiac complications or chronic digestive complications as the last manifestations of Chagas disease. These complications are usually fatal. In the American continent, it is estimated that approximately 16-18 million people will be infected by the parasite. This estimate does not include Mexico or Nicaragua for which accurate public health data are not available. Due to recent patterns of urbanization and immigration, Chagas disease is not a unique problem for Latin American countries. Estimates from a decade ago suggested that approximately 300,000 infected individuals were living in the city of Sao Paulo and more than 200,000 in Rio de Janeiro and Buenos Aires. In addition, patients with Chagas disease with chronic and asymptomatic forms of the disease are migrating north to the United States of America and Canada and even east to Europe. Several years ago it was estimated that approximately 100,000 infected individuals were already living in the United States of America, most of them having immigrated from Mexico and Central America. Many of these immigrants are unaware that they have contracted Chagas disease and continue to donate infected blood. Therefore, the control of Chagas disease "transfusion" is of vital importance to prevent infection in the United States of America and Canada. Chagas disease can also be transmitted congenitally. Several American families never exposed by trip to endemic areas were infected congenitally by parents or grandparents of Central or South America. The programs in Central and South America are currently related to trying to select pregnant women and newborns to reduce the proportion of Chagas disease. Currently, no optimal test is available for the diagnosis of chronic stage Chagas disease. The most direct available method of excluding potentially infected donors from the blood pool is to ask questions about immigration and travel involving Central and South America. These geographical exclusions are somewhat insensitive and subject to the reliability of the potential donor. As a result, a large number of willing and healthy donors are inappropriately excluded, thus contributing to a decrease in blood donors in Canada and the United States of America. Therefore, a rapid, accurate and non-expensive screening test is needed to provide rapid diagnosis of Chagas disease and to ensure the safety of blood supplies.

BRIEF DESCRIPTION OF THE INVENTION The present invention provides polypeptide-based biomarkers that are differentially present in subjects with Chagas disease and in particular, who are differentially present in chronically infected subjects against healthy uninfected individuals. In addition, the present invention provides methods for using polypeptide-based biomarkers to qualify for Chagas disease in a subject or in a biological sample taken from a subject, including a sample of serum, blood or other donated tissue. As such, the invention provides markers that represent new protein fragments expressed in individuals infected with T. cruz i, the pathogen responsible for Chagas disease. One such protein, referred to herein as MllO, is homologous to portions of a Leishmania major protein of unknown function (LM15-1.32). Many and portions of it provide useful biomarkers for Chagas disease. In one aspect, the invention provides a method for scoring the status of Chagas disease in a subject, the method comprising: (a) measuring at least one biomarker in a biological sample of the subject, wherein in at least one biomarker it is selected from the group consisting of the biomarkers of Table 1 and Table 2 (that is, Tables 2A-2X) as well as those summarized in the Figures and (b) correlating the measurement with the status of Chagas disease. In one embodiment, the biological sample is a serum sample. In one embodiment, at least one biomarker is selected from the group consisting of the biomarkers of Tables 3 and 4. In another embodiment, at least one biomarker is selected from the following biomarkers: MIP-la, ApoIA, Fibronectin , anaphylatoxin C3 and MllO. In another embodiment, the method comprises measuring each of the following biomarkers: MIP-la, ApoIA, Fibronectin, anaphylatoxin C3 and MllO. In still another embodiment, the method further comprises additionally measuring one or more of any of the biomarkers listed in Table 1, Table 2 and in the Figures. In a preferred embodiment, highly sensitive biomarkers of molecular masses 4.4, 4.8, 7.8, 8.9, 13.6, 16.3, 28.7 and 54.04 are used. In one embodiment, at least one biomarker is measured by capturing the biomarker on an absorber of a SELDI probe and detecting biomarkers captured by laser desorption-ionization mass spectrometry. In certain embodiments, the adsorbent is a cation exchange adsorbent, while in other embodiments, the adsorbent is a metal chelation adsorbent. In another embodiment, in at least one biomarker is measured by immunological test. In another embodiment, the correlation is carried out by means of a programming element classification algorithm. In an additional modality, the Chagas disease status is selected from chronically infected against uninfected. In still other modalities, the Chagas disease status is selected from chronically infected status against acutely infected disease status, chronically infected asymptomatic status against chronically affected with symptoms or acutely infected status against uninfected healthy status. In yet another modality, the status of Chagas disease is selected from Chagas against healthy. In a preferred embodiment, at least one biomarker is selected from the biomarkers of Table 3. In yet another embodiment, Chagas disease status is selected from Chagas versus Chagas disease. In a preferred embodiment, in at least one biomarker is selected from the biomarkers of Table 4. In another preferred embodiment, at least one biomarker is selected from the biomarkers of molecular weight 8.351 kDa, 9.3 kDa, 7.3 kDa, 6.04 kDa, 4.4 kDa, 4.07 kDa and 5.1 kDa, as illustrated in Figures 7-9. In yet another embodiment, the method further comprises managing the treatment of the subject based on status. If the measurement correlates with Chagas disease, then the treatment management of the subject comprises administering to a patient drugs selected from the group consisting of, but not necessarily limited to, drugs such as nifurtimox, benznidazole or allopurinol. In a further embodiment, the method further comprises measuring at least one biomarker after handling the subject. In another aspect, the present invention provides a method comprising measuring at least one biomarker of a sample from a subject, wherein in at least one biomarker is selected from the group consisting of biomarkers summarized in Table 1 and Table 2., also as in the Figures. In one embodiment, the sample is a serum sample. In one embodiment, at least one biomarker is selected from the group consisting of the biomarkers of Tables 3 and 4. In another embodiment, at least one biomarker is selected from the following biomarkers: MIP-la, ApoIA, Fibronectin , anaphylatoxin C3 and MllO. In still another embodiment, the method comprises measuring each of the following markers: MlP-la, Apo 1A, Fibronectin, anaphylatoxin C3 and MllO. In still another embodiment, the method further comprises additionally measuring one or more of any of the biomarkers listed in Table 1, Table 2 and in the Figures. In one embodiment, at least one biomarker is measured by capturing the biomarker on an adsorbent of a SELDI probe and detecting biomarkers captured by laser desorption-ionization mass spectrometry. In certain embodiments, the adsorbent is a cation exchange adsorbent, while in other embodiments, the adsorbent is a metal chelation. In another embodiment, in at least one biomarker is measured by immunological test. In yet another aspect, the present invention provides a kit comprising-, (a) a solid support comprising at least one capture reagent appended thereto, wherein the capture reagent is linked to at least one biomarker of a first group consisting of the biomarkers summarized in Table 1, Table 2 and in Figures and (b) instructions for using the solid support to detect in at least one biomarker summarized in Table 1, Table 2 and in the Figures. In one embodiment, the kit provides instructions for using the solid support to detect a biomarker selected from the biomarkers of Tables 3 and 4. In another embodiment, the kit provides instructions for using the solid support to detect a biomarker selected from the following biomarkers : MlP-la, ApoA, Fibronectin, anaphylatoxin C3 and MllO. in another embodiment, the team provides instructions for using the solid support to detect each of the following markers: MlP-la, Apo 1A, Fibronectin, anaphylatoxin C3 and MllO. In still another embodiment, the kit provides instructions for additionally measuring one or more of any of the biomarkers listed in Table 1, Table 2 and the figures, which preferably include one or more of the highly sensitive biomarkers of molecular masses 4.4, 4.8 , 7.8, 8.9, 13.6, 16.3, 28.7 and 54.04. In another embodiment, the solid support comprising the capture reagent is a SELDI probe. In some embodiments, the capture reagent is a cation exchange adsorbent. In other embodiments, the kit further comprises (c) an anion exchange chromatography adsorbent. In other embodiments, the kit further comprises (c) a container that contains at least one of the biomarkers of Table 1, Table 2 and in the Figures, which preferably include one or more of the highly sensitive biomarkers of molecular masses 4.4, 4.8 , 7.8, 8.9, 13.6, 16.3, 28.7 and 54.04. In a further aspect, the present invention provides a kit comprising: (a) a solid support comprising at least one capture kit appended thereto, wherein the capture reagent is linked to at least one biomarker of a first group which consists of the biomarkers summarized in Table 1, Table 2 and in Figures and (b) a container comprising at least one of the biomarkers summarized in Table 1, Table 2 and in the Figures. In one embodiment, the kit provides instructions for using the solid support to detect a biomarker selected from the biomarkers of Tables 3 and 4. In one embodiment, the kit provides instructions for using the solid support to detect a biomarker selected from the following biomarkers: MlP-la, ApoA, Fibronectin, anaphylatoxin C3 and MllO. In yet another modality, the team provides instructions for using the solid equipment to detect each of the following biomarkers: MlP-la, Apo 1A, Fibronectin, anaphylatoxin C3 and MllO. In yet another embodiment, the kit provides instructions for additionally measuring one or more of any of the biomarkers listed in Table 1, Table 2 and in the Figures. In another embodiment, the solid support comprising the capture reagent is a SELDI probe. In some embodiments, the capture reagent is a cation exchange adsorbent or metal chelation adsorbent. In other embodiments, the kit further comprises (c) an anion exchange chromatography adsorbent. In yet a further aspect, the present invention provides a product of programming elements, the product of programming elements comprising: (a) a code that accesses data attributed to a sample, the data comprise measuring at least one biomarker in the shows, the biomarker selected from the group consisting of the biomarkers of Table 1, Table 2 and in the Figures; and (b) a code that executes a classification algorithm that classifies the Chagas disease status of the sample as a function of the measurement. In one embodiment, the classification algorithm classifies the status of Chagas disease in the sample as a function of the measurement of a biomarker selected from the biomarkers of Tables 3 and 4. In one embodiment, the classification algorithm classifies the status of Chagas disease of the sample as a function of the measurement of a biomarker selected from the group consisting of MlP-la, Apo 1A, Fibronectin, anaphylatoxin C3 and MllO. In still another modality, the classification algorithm classifies the status of Chagas disease in the sample as a function of the measurement of each of the following biomarkers: MIP-la, ApoA, Fibronectin, anaphylatoxin C3 and MllO. In still another embodiment, the classification algorithm classifies the status of Chagas disease in the sample as a function of the additional measurement of one or more of any of the biomarkers listed in Table 1, Table 2, and Figures. In yet another embodiment, the algorithm for classifying programming elements classifies the Chagas disease status of the sample as a function of the measurement of biomarkers in which biomarkers F1WH_2, F4IH_4, F3WL_8 and F1LL_3 of Table 1 are included. aspects, the present invention provides purified biomolecules selected from the biomarkers summarized in Table 1, Table 2 and in the Figures and additionally, methods comprising detecting a biomarker summarized in Table 1, Table 2 and in the Figures by mass spectrometry or immunological test. In preferred embodiments of both of the above aspects, the biomarker is selected from the biomarkers of Tables 3 and 4. In yet another embodiment, the method further comprises testing and rating blood inventories based on the status of the blood which has been tested in accordance with the methods described herein. If the measurements taken from the blood samples correlate with Chagas disease, then the management of the blood inventories comprises the decontamination of the infected blood by treatment of the infected blood with purification agents available to the one skilled in the art in the art. which include but are not limited to agents such as gentian violet, ascorbic acid and aminoloquinolone WR6026. Alternatively, infected blood can be discarded or destroyed and only blood inventories which have not been positively tested for Chagas disease are retained. In another aspect, the present invention provides a method for measuring at least three biomarkers in a biological sample, wherein the at least three biomarkers are selected from the group consisting of the biomarkers of Table 1 and Tables 2A-2X . In a preferred embodiment, the at least three biomarkers are selected from the group consisting of the biomarkers of Tables 3 and 4. In yet another preferred embodiment, the at least three biomarkers are selected from the group consisting of MlP-la, Apo ÍA, Fibronectin, anaphylatoxin C3 and MllO. In yet another preferred embodiment, the at least three biomarkers are MlP-la, ApoIA, Fibronectin, anaphylatoxin C3 and MllO. even in another preferred embodiment, the at least three biomarkers are selected from the group including biomarkers F1WH_2, F4IH_4, F3WL_8 and F1LL_3 of Table 1. In one aspect, the present invention provides a method to qualify the status of Chagas disease in a subject compared to the status of a different parasitic disease (ie, parasitic disease that is not Chagas disease), the method comprises (a) measuring at least one biomarker in a biological sample of the subject, wherein in minus one biomarker specifically indicates the presence of Chagas disease and does not indicate the presence of a different parasitic infection and (b) correlate the measurement with the status of Chagas disease compared to the status of a different parasitic infection. In a modality, the biological sample is a serum sample. In a preferred embodiment of this method, at least one biomarker is selected from the group of biomarkers of Table 4. In another preferred embodiment, at least one biomarker is selected from the group of biomarkers of molecular masses 8,351 kDa, 9.3 kDa , 7.3 kDa, 6.04 kDa, 4.4 kDa, 4.07 kDa and 5.1 kDa, as illustrated in Figures 7-9. In another preferred embodiment of this method, the parasitic infection comprises an infection by kinetoplastidae. In yet another preferred embodiment, parasitic infection includes but is not limited to Leishmaniasis, African trypanosomiasis (soil disease), malaria and babesiosis. In another aspect, the present invention provides a method for verifying the progression course of Chagas disease in a patient, comprising: (a) measuring at least one biomarker in a first biological sample of the patient, wherein in minus one biomarker specifically indicates the presence of Chagas disease; (b) measuring in at least one biomarker in a second biological sample of the subject, wherein the second biological sample was obtained from the subject after the first biological sample and (c) correlating the measurements with the progression or regression of Chagas disease in the subject. In one embodiment, in at least one biomarker is selected from the group consisting of the biomarkers of Tables 1 and 2 and preferably Tables 3 and 4. In other preferred embodiments, in at least one biomarker is selected from the group consisting of 8,127 kDa (Apo-1) and 8,937 kDa. Other elements, objects and advantages of the invention and their preferred embodiments will become apparent from the detailed description, examples and claims that follow.

BRIEF DESCRIPTION OF THE FIGURES Figures 1A-W show representative mass spectra exhibiting various biomarkers of the invention and providing their mass to charge ratio. Figure 2 shows the analysis of trypsin digests of the Chagas disease biomarker of 110 kDa. Figures 3A-B show the results of multivariate analysis using five biomarkers to determine Chagas disease status. The detection of two or more biomarkers expressed independently from each other provides higher degrees of sensitivity and specificity for Chagas disease than can be provided by the detection of any individual biomarker. The biomarkers used for the analysis shown in Figures 3A-B are named in the figure by their marker IDs, with reference to Table 1. Figure 4 shows a mass spectrophotometric analysis confirming the identity of the 8.1 kDa protein which was detected in fraction 1 in IMAC-Cu and WCX arrangements using SPA and EAM. Panels 1 and 2 show the spectrum of proteins bound by anti-C3a antibodies and control mouse IgG antibodies, respectively. The antibodies were coupled to Hyper D beads of Protein A. Panel 3 shows the spectrum of the discovery phase of the study. the blood sample was fractionated using anion exchange chromatography and fraction 1 was profiled using a WCX arrangement (low laser energy). The proteins used in the model are indicated by their marker IDs, with reference to Table 1, as follows: F1WH_2 C-terminal fragment of Apo-1 (amino acids 124-243); F3WL_8 C-terminal truncation of anaphylatoxin C3 (amino acids 1-68); F4IH_4 N-terminal fragment of Apo 1 (amino acids 1-214); and F1IL_3 peak with double load of anaphylatoxin C3 of des Arg (amino acids 1-76). Figure 5 shows a graphical representation of the differential signal intensity of the 8,127 kDa Apo-1 peptide in asymptomatic Venezuelan patients chronically infected with Chagas against pediatric Guatemalan patients acutely infected with signs of Chagas disease indicating EKG. In this case, the difference in intensity of the biomarker is significant (p = 0.001), but the signal is present at a certain level in both Venezuelan patients infected or in Guatemalan patients acutely infected with or without EKG signs indicative of Chagas disease. This biomarker is useful for qualifying chronic Chagas disease and for distinguishing between chronically infected and acutely infected individuals. Figure 6 shows a graphical representation of the differential signal intensity of a 8,937 kDa peptide in chronically asymptomatic, Chagas infected Venezuelan patients against acutely infected Guatemalan patients with high EKG readings. In this case, the difference in intensity of the biomarker is significant (p = 0.002), but the signal is present at some level in both infected Venezuelan patients and Guatemalan patients acutely infected with or without EKG signs indicative of Chagas disease. This biomarker is useful for qualifying chronic Chagas disease and for distinguishing between chronically or acutely infected individuals. Figure 7 shows the signal intensity of a 8,351 kDa peptide in Venezuelan patients infected with Chagas compared to healthy uninfected individuals and individuals infected with different parasitic diseases, here African trypanosomiasis (sleeping sickness), malaria and babesiosis. Figure 8 shows the differential signal intensity of a 9.3 kDa peptide in Venezuelan patients infected with Chagas compared with individuals infected with different parasitic diseases, here African trypanosomiasis (sleeping sickness) and malaria. Figures 9A-E demonstrate the application of the methods of the present patent application to the identification of biomarkers indicating the status of other parasitic diseases, for example, helminthic infections, in which biomarkers are included indicating the infection with such organisms such as Fasciola hepatica, Schistosoma mansoni, Strongyloides stercolaris, Echinococcus granulosis, Tr chinella native, Filaria, Cysticercosis and Toxocara.

DETAILED DESCRIPTION OF THE INVENTION INTRODUCTION A biomarker is an organic biomolecule that is differentially present in a sample taken from a subject of a phenotypic status (eg, having a disease) as compared to another phenotypic status (eg, that does not have the same phenotypic status). disease) . A biomarker is differentially present between different phenotypic status and the mean or average expression level of the biomarker in the different groups is estimated to be statistically significant. Common tests for statistical significance include, among others, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and proportion of nones. Biomarkers, alone or in combination, provide measures of relative risk to which a subject belongs to one phenotypic or other status. Accordingly, they are useful as markers for disease (diagnosis), therapeutic effectiveness of a drug (thera- pies) and drug toxicity.

II. BIOMARKERS FOR CHAGAS DISEASE A. Biomarkers This invention provides, among other useful elements, polypeptide-based biomarkers that are differentially present in subjects who have Chagas disease against healthy uninfected individuals. The biomarkers are characterized by a mass-to-charge ratio as determined by mass spectrometry, by the shape of their spectral peak in mass time-of-flight spectrometry and by their binding characteristics to adsorbent surfaces. These features provide a method for determining whether a biomolecule detected in particular in a biomarker of this invention. These characteristics represent inherent characteristics of biomolecules and not process limitations in the way in which biomolecules are discriminated. In one aspect, this invention provides these biomarkers in isolated form. Biomarkers were discovered using SELDI technology using ProteinChip arrays from Ciphergen Biosystems, Inc. (Fermont, CA) ("Ciphergen"). Serum samples were collected from subjects diagnosed with Chagas disease and subjects diagnosed as normal (not demented). The samples were fractionated by anion exchange chromatography. Fractional samples were applied to SELDI biochips and spectra of the polypeptides in the samples were generated by mass time-of-flight spectrometry on a Ciphergen PBSII mass spectrometer. The spectra thus obtained were analyzed by Ciphergen Express ™ Data Manager Software programming elements with the Biomarker Wizard and Biomarker Pattern Software from Ciphergen Biosystems, Inc. The mass spectra for each group were subjected to scatter plot analysis. A Mann-Whitney test analysis was used to compare the Chagas disease groups and control groups for each group of proteins on the scatter plot and proteins were selected that differed significantly (P <0.006, but preferably less than 0.0001 ) between the two groups. This Method is described in more detail in the Examples section. The biomarkers thus discovered are presented in Tables 1-4 and additionally in Figures 5-9. With respect to Table 1, the "ProteinChip analysis" column refers to the chromatographic fraction in which the biomarker is found, the type of biochip to which the biomarker is linked and the washing conditions, according to the Example. For example, Fl, F2, etc., refer to "Fraction 1", "Fraction 2", etc. "I" refers to the use of commercially available ProteinChip IMAC-3 ("Ciphergen Biosystems, Inc."). "W" refers to the use of the commercially available ProteinChip WCX2 ("Ciphergen Biosystems, Inc."). "H" and "L" refer to the reading of SELDI-MS data at high and low intensities, respectively. This code, along with a unique number, is used to determine a Marker ID. further, all biomarkers disclosed herein were first discovered using ProteinChip analysis which employs the use of sinapinic acid (SPA) and an Energy Absorbing Molecule (EAM) as described in greater detail below and in the Examples. The "S" that appears in the Marker ID for the biomarkers of Tables 2-4 refers to the use of SPA. Biomarkers can also be referred to as M ### where ### represents the proportion of measured mass of the biomarker (M / Z) or they can be named by their molecular weights (for example, "the 100 kDa biomarker") . Where a particular biomarker has been subjected to additional identification protocols, as described herein, and the identity of the marker is indicated in the Table together with a description of its length with reference to the full length protein (e.g. the 13.6 kDa peak, labeled F1IH_1 in Table 1, corresponds to the C-terminal fragment of Apolipoprotein Al, amino acids 124-243).

TABLE 1 M / Z Value p Fraction, Marker ID (kDa) ProteinChip and beam intensity 13.6 < 0.006 F1IH F1IH .1 C-terminal Fragment of Apo Al 8124-243) 16. 3 < 0.006 Fi F1IH_2 Truncated C3 Anaphylatoxin Dimer (1-68) 8. 335027 < 0.006 FUL FUL 1 8. 349768 < 0.006 FUL FUL 2 4. 476274 < 0.006 FUL F1IL_3 Double load of anaphylatoxin C3 of des Arg (1-76) 8. 950683 < 0.006 FUL F1IL_4 Anafilatocin C3 from des Arg (1-76) 7. 190033 < 0.006 FUL FUL 5 9. 155242 < 0.006 FUL FIEL 6 8. 14304 < 0.006 FUL F1IL 7 C-terminal truncation of anaphylatoxin C3 (1-68) 9. 254734 < 0.006 FUL FUL 8 8. 935389 < 0.006 FUL FUL 9 8. 4567 < 0.006 FUL FUL 10 4. 066244 < 0.006 FUL FUL 11 8. 130521 < 0.006 FUL FUL 12 8. 43914 < 0.006 FUL FUL 13 4. 808781 < 0.006 FUL FUL 14 8. 642292 < 0.006 FUL FUL 15 9. 299242 < 0.006 FUL FUL 16 4. 21875 < 0.006 FUL FUL 17 2. 491823 < 0.006 FUL FUL 18 1. 079363 < 0.006 FUL FUL 19 4. 174495 < 0.006 FUL FUL 20 3. 29103 < 0.006 FUL FUL 21 . 070 < 0.006 F1WH F1WH_1 C-terminal Fragment of Apo Al (154-243) M / Z Value p Fraction, Marker ID (kDa) ProteinChip and beam intensity 13.6 < 0.006 F1WH F1 H_2 C-terminal Fragment of Apo Al (124-243) 13. 85 < 0.0036 F1 H F1WH 3 16.3 < 0.006 F1WH F1WH_4 Truncated C3 Anaphylatoxin Dimer (1-68) 16. 5 < 0.006 F1 H F1 H 5 28.957 < 0.006 F1 H F1WH_6 Fragment N-terminal fibronectin (1-258) 12. 952 < 0.006 F1WH F1WH 7 28. 79 < 0.006 F1WH F1WH 8 . 67 < 0.006 F1WH F1 H 9 16. 7 < 0.006 F1WH F1WH 10 12. 75 < 0.006 F1WH F1 H 11 31. 78 < 0.006 F1 H F1WH 12 8. 935 < 0.006 F1WL F1WL 1 4. 480 < 0.006 F1WL F1WL 2 9. 308 < 0.006 F1WL F1WL_3 C-terminal Fragment of Apo Al (161-243) 8. 351 < 0.006 F1WL F1 L_4 8,129 < 0.006 F1WL F1WL_5 C-terminal fragment of anaphylatoxin C3 (1-68) 7. 078 < 0.006 F1WL F1 L 6 8.335 < 0.006 F1WL F1WL 7 8.142 < 0.006 F1WL F1WL 8 7.483 < 0.006 F1 L F1 L 9 7.178 < 0.006 F1WL F1WL 10 6.454 < 0.006 F1WL F1WL_11 Apolipoprotein Cl (lost 2N-terminal amino acids) M / Z Value p Fraction, Marker ID (kDa) ProteinChip and beam intensity 6.636 < 0.006 F1WL F1WL_12 Apolipoprotein Cl 89.6 < 0.006 F2IH F2IH 1 88.3 < 0.006 F2IH F2IH 2 37.7 < 0.006 F2IH F2IH 3 54.04 < 0.006 F2IH F2IH 4 91.16 < 0.006 F2IH F2IH 5 8.350 < 0.006 F2BL F2IL 1 8.156 < 0.006 F2IL F2IL 2 4,079 < 0.006 F2IL F2IL 3 28.7 < 0.006 F2WH F2WH 1 33.8 < 0.006 F2WH F2WH 2 4.812 < 0.006 F2WL F2WL 1 5.458 < 0.006 F2WL F2WL 2 4,072 < 0.006 F2WL F2WL 3 10.3 < 0.006 F3IH F3IH 1 10.46 < 0.006 F3IH F3IH 2 4.819 < 0.006 F3IL F3IL 1 10 4.157 < 0.006 F3IL F3IL 2 8.966 < 0.006 F3IL F3IL 3 5.995 < 0.006 F3IL F3IL 4 4.145 < 0.006 F3IL F3BL 5,495 < 0.006 F3IL F3IL 6 8.148 < 0.006 F3IL F3IL 7 13.6 < 0.006 F3WH F3WH_1 C-terminal Fragment of Apo Al (124-243) 14.2 < 0.006 F3WH F3WH 2 14.09 < 0.006 F3WH F3 H 3 28.2 < 0.006 F3WH F3WH 4 28,393 < 0.006 F3WH F3WH 5 15 10.1 < 0.006 F3WH F3 H 6 17.47 < 0.006 F3WH F3WH 7 37.4 < 0.006 F3WH F3WH 8 8.943 < 0.006 F3 L F3WH 1 3,400 < 0.006 F3WL F3WL 2 3.384 < 0.006 F3 L F3WL 3 4.156 < 0.006 F3WL F3WL 4 5,993 < 0.006 F3WL F3WL 5 4.234 < 0.006 F3WL F3 L-6 4,219 < 0.006 F3WL F3WL 7 8.133 < 0.006 - F3WL F3WL_8 C-terminal truncation of anaphylatoxin C3 twenty M / Z Value p Fraction, Marker ID (kDa) ProteinChip and beam intensity 8.147 < 0.006 F3WL F3WL 9 6.452 < 0.006 F3WL F3WL 10 13.6 < 0.006 F4IH F4IH_1 C-tepninal Fragment of Apo Al (124-243) 10,046 < 0.006 F4IH F4IH 2 10,243 < 0.006 F4IH F4IH 3 24.77 < 0.006 F4IH F4IH_4 N-terminal Fragment of Apo Al (1-214) 8.943 < 0.006 F4IL F4IL 1 8.146 < 0.006 F4IL F4IL 2 13.6 < 0.006 F4WH F4WH 1 10,039 < 0.006 F4WH F4 H 2 24J <; 0.006 F4WH F4 H 3 9.348 < 0.006 F4WH F4 H 4 6.457 < 0.006 F4WL F4WL 1 8.132 < 0.006 F4WL F4WL 2 10 8.945 < 0.006 F4WL F4WL 3 3.383 < 0.006 F4WL F4WL 4 8.150 < 0.006 F4WL F4WL 5 9.305 < 0.006 F4WL F4 L 6 3.968 < 0.006 F4WL F4WL 7 5,017 < 0.006 F4WL F4WL 8 51.6 < 0.006 F5IH F5IH 1 8.142 < 0.006 F5IL F5IL 1 7.933 < 0.006 F5IL F5IL 2 4.627 < 0.006 F5IL F5IL 3 13,544 < 0.006 F5WH F5WH 1 14.36 < 0.006 F5 H F5WH 2 14.54 < 0.006 F5WH F5WH 3 15 17.89 < 0.006 F5 H F5WH 4 18.7 < 0.006 F5 H F5 H 5 33.5 < 0.006 F5WH F5WH 6 11.86 < 0.006 F5 H F5WH 7 6.453 < 0.006 F5WL F5WL 1 8.128 < 0.006 F5WL F5 L 2 8.948 < 0.006 F5WL F5WL 3 6.231 < 0.006 F5WL F5WL 4 6.335 < 0.006 F5 L F5WL 5 6.843 < 0.006 F5WL F5WL 6 5.990 < 0.006 F5 L F5WL 7 28,324 < 0.006 F6IH F6IH 1 84.3 < 0.006 F6IH F6IH 2 20 28.123 < 0.006 F6IH F6IH 3 M Z Value p Fraction, Marker ID (kDa) ProteinChip and beam intensity 56.4 < 0.006 F6IH F6IH 4 28.5 < 0.006 F6IH F6IH 5 8.951 < 0.006 F6DL F6BL 1 6.648 < 0.006 F6EL F6IL 2 8.145 < 0.006 F6IL F6IL 3 14,394 < 0.006 F6WH F6WH 1 14,579 < 0.006 F6WH F6WH 2 18.6 < 0.006 F6 H F6WH 3 8.939 < 0.06 F6WL F6WL 1 6.844 < 0.06 F6WL F6 L 2 3.322 < 0.06 F6WL F6 L 3 2.013 < 0.06 F6WL F6 L 4 6.639 < 0.06 F6WL F6WL 5 The biomarkers of this invention are characterized by their mass to charge ratio as determined by mass spectrometry. The mass to charge ratio of each biomarker is provided in Table 1 under the "M / Z" column. The mass to charge ratios were determined from spectra generated in a PBS II mass spectrometer from Ciphergen Biosystems, Inc. This instrument has a mass accuracy of approximately +/- 0.15% for markers with molecular weights of approximately 20 kDa or less and approximately 2.0% for molecular weight markers greater than about 20 kDa. Additionally, the instrument has a mass resolution of approximately 400 to 1000 m / dm, where m is the mass and dm is the mass spectral peak width at a peak height of 0.5. The mass to charge ratio of the biomarkers was determined using the Biomarker Wizard ™ programming elements (Ciphergen Biosystems, Inc.). Biomarker Wizard ™ assigns a mass to load ratio to a biomarker by grouping the mass to load ratios of the same peaks of all the analyzed spectra, as determined by PSBII, taking the ratio of mass to maximum and minimum load in the group and dividing by two. Thus, the masses provided reflect these specifications. The biomarkers of this invention are further characterized by the shape of their spectral peak in flight time mass spectrometry. Mass spectra show the peak shapes corresponding to representative biomarkers are presented in Figure 1. The biomarkers of this invention are characterized in isolation by their binding properties on chromatographic surfaces, that is, their ability to bind to ProteinChips IMAC-2 against its ability to link to WXC2 Cation Exchange ProteinChips. The identities of certain biomarkers of this invention have been determined. The method by which this determination was made is described later herein. For biomarkers whose identity has been determined, the presence of the biomarker (or nucleic acid encoding the biomarker) in a sample or subject can be determined by other methods known in the art which include, but are not limited to methods such as Western blotting , Southern blot or PCR. Because the biomarkers of this invention are characterized by mass to charge ratio, binding properties and spectral shape, they can be detected by mass spectrometry without knowing their specific identity. However, if desired, biomarkers whose identity is not determined can be identified, for example, by determining the amino acid sequence of the polypeptides. For example, a biomarker can be mapped by peptides with a number of enzymes, such as trypsin or V8 protease and the molecular weights of the digestion fragments can be used to search databases for sequences that match the molecular weights of the fragments of digestion generated by the various enzymes. Alternatively, protein biomarkers can be sequenced using MS technology in tandem. In this method, the protein is isolated, for example, by gel electrophoresis. A band containing the biomarker is cut and the protein is subjected to protease digestion. The individual protein fragments are separated by a first mass spectrometer. Then, the fragment is subjected to collision-induced cooling, which fragments the peptide and produces a ladder of polypeptides. Then the polypeptide ladder is analyzed by the second MS mass spectrometer in tandem. The difference in mass of the elements of the polypeptide ladder identifies the amino acids in the sequence. A whole protein can be sequenced in this way or a sequence fragment can be subjected to a database scan to find the identity of the candidates. The preferred biological source for the detection of biomarkers is serum. However, in other modalities, biomarkers can be detected in any tissue of interest where infectious material can be found. In the case of blood samples, blood inventories can be tested by isolating the blood serum by techniques well known in the art. Then the serum can be analyzed according to the techniques described herein. If the measurements taken from the blood samples indicate that the individual from whom the blood was taken was infected with Chagas disease, then the blood can be treated with purification agents available to one skilled in the art in which it is included, but are not limited to, agents such as gentian violet, ascorbic acid and aminoloquinolone WR6026. Alternatively, infected blood can be discarded or destroyed and only blood inventories which have not been positively tested for Chagas disease are retained. The biomarkers of this invention are biomolecules. Thus, this invention provides these biomolecules in isolated form. Biomarkers can be isolated from biological fluids such as serum. They can be isolated by any method known in the art, based on both their mass and their binding characteristics. For example, a sample comprising the biomolecules may be subjected to chromatographic fractionation, as described herein and subjected to further separation, for example by acrylamide gel electrophoresis. The knowledge of the identity of the biomarker also allows its isolation by means of immunoaffinity chromatography.

B. Use of modified forms of biomarkers for Chagas disease It has been found that proteins frequently exist in a sample in a plurality of different forms characterized by a detectably different mass. These forms can result from either one or both of pre- and post-translation modification. Modified pretranslation forms include allelic variants, sliced variants and edited forms of RNA. Post-translationally modified forms include forms resulting from proteolytic cleavage (eg, fragments of an original protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cystinylation, sulfonation and acetylation. The collection of proteins that include a specific protein and all modified forms thereof is referred to herein as a "protein group". The collection of all modified forms of specific protein, excluding the specific protein, by itself is referred to herein as a "modified protein group". Modified forms of any biomarker of this invention (in which any of the biomarkers listed in Tables 1 and in the Figures herein) are included may also be used, by themselves as biomarkers. In certain cases, the modified forms may exhibit better discriminatory power in diagnosis than in specific forms summarized herein. Modified forms of a biomarker, in which any of the markers listed in Tables 1-4 and in the Figures herein are included, can be detected initially by any methodology that can detect and distinguish the modified form of the biomarker. A preferred method for initial detection involves first capturing the biomarker and modified forms thereof, for example, with biospecific capture reagents and then detecting captured proteins by mass spectrometry. More specifically, the proteins are captured using biospecific capture reagents, such as antibodies, aptamers or afficibodies that recognize the biomarker and modified forms thereof. This method will also result in the capture of protein interactors that are linked to proteins or that are otherwise recognized by antibodies and that by themselves can be biomarkers. Preferably, the biospecific capture reagents are linked to a solid phase. Then, the captured proteins can be detected by SXLDI mass spectrometry or by elution of the capture and detection reagent proteins from the proteins eluted by conventional MALDl or by SELDI. The use of mass spectrometry is especially attractive because it can distinguish and quantify modified forms of a protein based on mass and without the need for labeling. Preferably, the biospecific capture reagent is bound to a solid phase, such as a plate, a membrane or a chip. Methods of coupling biomolecules such as antibodies to a solid phase are well known in the art. They may employ, for example, bifunctional linking agents or the solid phase may be derivatized with a reactive group, such as an epoxide or an imidazole, which will bind to the molecule upon contact. Biospecific capture reagents against different target proteins can be mixed in the same place or can be attached to solid phases in different physical sites or addressable sites. For example, you can load multiple columns with derived beads, each column, able to capture a single group of proteins. Alternatively, a single column can be packed with different beads derived with capture reagents against a variety of protein groups, thereby capturing all the analytes in one place. Thus, technologies based on antibody-derived beads, such as Luminex xMAP technology (Austin, TX) can be used to detect protein groups. However, biospecific capture reagents can be targeted specifically to the membranes of a group in order to differentiate them. In yet another embodiment, the biochip surfaces can be derived with the capture reagents directed against groups of proteins either at the same site or at physically different addressable sites. An advantage of capturing different groups in different addressable sites is that the analysis becomes simpler. After identification of the modified forms of a protein and correlation with the clinical parameter of interest, the modified form can be used as a biomarker in any of the methods of this invention. At this point, the detection of the modified form can be effected by any specific detection methodology including affinity capture followed by mass spectrometry or traditional immunological test directed specifically to the modified form. The immunological test requires biospecific capture reagents, such as antibodies, to capture the analytes. Also, if the analysis should be designed to specifically distinguish protein and modified forms of protein. This can be done, for example by employing a sandwich analysis in which an antibody captures more than one form and second, distinctly labeled forms, which specifically bind and provide different detection of the various forms. Antibodies can be produced by immunizing animals with biomolecules. This invention contemplates traditional immunological tests which include, for example, immunological sandwich assays in which ELISA or fluorescence-based immunological tests are included, as well as other immunoassays or immunological enzyme tests.

III. DETECTION OF BIOMARKERS REGARDING CHAGAS DISEASE The biomarkers of this invention can be detected by any appropriate method. Detection paradigms that can be employed for this purpose include optical methods, electrochemical methods (voltammetric and amperometric techniques), atomic force microscopy and radiofrequency methods, for example multipolar resonance spectrometry. Illustrative of the optical methods, in addition to both confocal and non-confocal microscopy, are detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a mirror method resonant, a grid-coupling waveguide method or interferometry). In one modality, a sample is analyzed by means of a biochip. Biochips generally comprise solid substrates having a generally flat surface to which a capture reagent (also called an adsorbent reagent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable sites, each of which has the capture reagent bonded thereto. "Protein biochip" refers to a biochip adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems, Inc. (Fremont, CA), Packard BioScience Company (Meriden CT), Zyomyx (Hayward, CA), Phylos (Lexington, MA) and Biacore (Uppsala, Sweden). . Examples of such protein biochips are described in the following published patents or patent applications: U.S. Patent No. 6,225,047; PCT International Publication No. WO 99/51773; U.S. Patent No. 6,329,209, PCT International Publication No. WO 00/56934 and U.S. Patent No. 5,242,828.

A. Detection by mass spectrometry In a preferred embodiment, the biomarkers of this invention are detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions. Examples of spectrometers are flight time, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and their hybrids. In a further preferred method, the mass spectrometer is a laser desorption / ionization mass spectrometer. In laser desorption / ionization mass spectrometry, the analytes are placed on the surface of a mass spectrometer probe, a device adapted to be coupled with a probe interface of the mass spectrometer and to present an analyte at ionization energy for the ionization and introduction to a mass spectrometer. A laser desorption mass spectrometer uses laser energy, commonly from an ultraviolet laser, but also from an infrared laser, to desorb analytes from a surface, to volatilize and ionize them and make them available to the ionic optical components of the mass spectrometer. 1. SELDI A preferred mass spectrometric technique for use in the invention is "Surface Enhanced Laser Desorption and Ionization" or "SELDI", as described for example in U.S. Patent Nos. 5,709,060 and 6,225,047, both issued to Hutches and Jeep. This refers to a desorption / ionization gas phase ion spectrometry method (eg, mass spectrometry) in which an analyte (here, one or more biomarkers) is captured on the surface of a mass spectrometry probe. SELDI There are several versions of SELDI. A version of SELDI is called "affinity capture mass spectrometry". It is also called "Enhanced Surface Affinity Capture" or "SEAC." This version involves the use of probes that have a material on the probe surface that captures analytes through a non-covalent affinity interaction (adsorption) between the material and the analyte. The material is called stranded "adsorbent", "capture reagent", "affinity reagent" or "binding portion". Such probes can be referred to as "affinity capture probes" and since they have an "adsorbent surface". The capture reagent may be any material capable of binding an analyte. The capture reagent may be attached directly to the substrate of the selective surface or the substrate may have a reactive surface which provides a reactive portion which is capable of binding the capture reagent, for example by means of a reaction forming a covalent bond or coordinated covalent bond. Epoxide and carbodiimidazole are useful reactive portions for covalently linking polypeptide capture reagents such as antibodies or cellular receptors. Nitrileacetic acid and iminodiacetic acid are useful reactive portions that function as chelating agents for binding metal ions that interact non-covalently with histidine-containing peptides. Adsorbents are generally classified as chromatographic adsorbents and biospecific adsorbents. "Chromatographic Adsorbent" refers to an adsorbent material commonly used in chromatography. Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (nitriloacetic acid or inimodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules (eg, nucleotides, amino acids, simple sugars and fatty acids) and mixed-mode adsorbents (for example, hydrophobic attraction / electrostatic repulsion adsorbents). "Biospecific adsorbent" refers to comprising a biomolecule, eg, a nucleic acid molecule (eg, an aptamer), a polypeptide, a polysaccharide, a lipid, a steroid or a conjugate thereof (eg, a glycoprotein, a lipoprotein, a glycolipid or a nucleic acid 8 eg, DNA-protein conjugate)) . In certain instances, the biospecific adsorbent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific adsorbents are antibodies, receptor proteins and nucleic acids. Biospecific adsorbents commonly have higher specificity for an optional analyte than chromatographic adsorbents. Additional examples of adsorbents for use in SELDI can be found in U.S. Patent No. 6,225,047. A "bioselective adsorbent" refers to an adsorbent that binds to an analyte with an affinity of at least 10"8 M. Protein biochips produced by Ciphergem Biosystems, Inc. comprise surfaces that have chromatographic or biospecific adsorbents attached to them at addressable sites.Ciphergen ProteinChip® arrays include NP20 (hydrophilic), H4 and H50 (hydrophobic); SAX-2, Q- 10 and LSAX-30 (anion exchange), WXC2, CM-10 and LWCX-30 (cation exchange), IMAC-3, IMAC-30 and IMAC 40 (metal chelate) and PS-10, PS-20 (reactive surface) with carboimidazole epoxide) and PG-20 (G protein coupled via carboimidazole) Hydrophobic ProteinChip arrays have isopropyl or nonylphenoxy-poly (ethylene glycol) methacrylate functionalities The anion exchange ProteinChip arrays have amino acid functionalities. Quaternary onium. Cation exchange ProteinChip arrays have carboxylate functionalities. The immobilized metal chelate ProteinChip arrays have nitriloacetic acid functionalities that adsorb transition metal ions such as copper, nickel, zinc and gallium by chelation. The preactivated ProteinChip arrays have carboimidizole or epoxide functional groups that can react with groups on proteins for covalent attachment. Such biochips are further described in. U.S. Patent No. 5,779,719 (Hutchens and Yip.

"Rethink Chromatography, June 17, 2003); PCT International Publication No. WO 00/66265 (Rich et al.," Probes for a Gas Phase Ion Spectrometer ", November 9, 2000) U.S. Patent No. 6,555,813 (Beecher et al., "Sample Holder with Hydrophobic Coating for Gas Phase Mass Spectrometer", April 29, 2003); U.S. Patent Application No. US 2003 0032043 (Pohl and Papanu, "Latex Based Adsorbent Chip", July 16, 2002) ), and PCT International Publication No. WO 03/040700 (Um et al., "Hydrophobic Surface Chip", May 15, 2003), U.S. Provisional Application No. 60 / 367,837 (Boschetti et al., "Biochips with Surfaces Coated with Polysaccharide-based Hydrogels ", May 5, 2002) and the US Patent Application entitled" Photocrosslinked Hydrogel Surface Coatings "(Huang et al., Filed February 21, 2003) .In general, a probe with a adsorbent surface is contacted with the sample for a period of sufficient time to allow the biomarker or biomarkers that may be present in a sample to bind to the solvent. After an incubation period, the substrate is washed to separate the unbound material. Appropriate washing solutions can be used; preferably, aqueous solutions are employed. The extent to which the molecules remain bound can be manipulated by adjusting the severity of the wash. The elution characteristics of a washing solution may depend for example on the pH, ionic strength, hydrophobicity, degree of caotropism, detergent strength and temperature. Unless the probe has SEAC and SEND properties (as described herein), an energy adsorbing molecule is then applied to the substrate with the linked biomarkers. The biomarkers linked to the substrates are detected in a gas-phase ion spectrometer such as a time-of-flight mass spectrometer. Biomarkers are ionized by an ionization source such as a laser, the ions generated are collected by an ionic optical assemblage, and then a dispersed mass analyzer and analyzes the ions that pass. Then, the detector translates the information of the detected ions to proportions of mass to load. The detection of a biomarker will commonly involve detection of signal intensity. Thus, both the quantity and the mass of the biomarker can be determined. Another variation of SELDI is enhanced net desorption on the surface (SEND), which involves the use of probes comprising energy adsorbing molecules that are chemically bonded to the probe surface ("SEND probe"). The phrase "energy adsorbing molecules" (EAM) denotes molecules that are capable of adsorbing energy from a source of laser desorption / ionization and after that, they contribute to the desorption and ionization of analyte molecules in contact therewith. The EAM category includes molecules used in MALDl, often referred to as "matrix" and is exemplified by derivatives of cinnamic acid, sinapinic acid (SPA), cyano-hydroxy cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid and hydroxyacetic acid derivatives -fenone. In certain embodiments, the energy absorbing molecule is incorporated into a linear or crosslinked polymer, for example a polymethacrylate. For example, the composition can be a copolymer of -cyano-4-methacryloyloxycinnamic acid and acrylate. In another embodiment, the composition is a copolymer of a-cyano-4-methacryloyloxycinnamic acid, acrylate and 3- (tri-ethoxy) silylpropyl methacrylate. In another embodiment, the composition is a copolymer of a-cyano-4-methacryloyloxycinnamic acid and octadecylmethacrylate ("C18 END"). SEND is further described in U.S. Patent No. 6,124,137 and PCT International Publication No. WO 03/64594 (Kitagawa, "Monomers and Polymers Having Energy Moieties of Use in Desorption / ionization of Analytes" August 7, 2003). SEAC / SEND is a version of SELDI in which a capture reagent and an energy adsorbing molecule are attached to the surface of the sample. Therefore, the SEAC / SEND probe allows the capture of analytes by means of affinity capture and ionization / desorption without the need to apply external matrix. The SEND C18 biochip is a version of SEAC / SEND that comprises a portion of C18 that functions as a capture reagent and a CHCA portion that functions as an energy absorbing portion. Another version of SELDI called Anexion and Photowalling Improving Surface Enhancement (SPAR), involves the use of probes that have portions appended to the surface that can covalently link to an analyte and then release the analyte by breaking a photolabile link into the analyte. the portion after exposure to light, for example, laser light (see, U.S. Patent No. 5,719,060). SEP7ΔR and other forms of SELDI are readily adapted to detect a biomarker or biomarker profile, in accordance with the present invention. 2. Other methods of mass spectrometry In another method of mass spectrometry, biomarkers can be captured first on a chromatographic resin that has chromatographic properties that bind biomarkers. In the present example, this could include a variety of methods. For example, biomarkers could be captured on a cation exchange resin, such as CM Ceramic Hyper D F resin, wash the resin, elute the biomarkers and detect by MALDl. Alternatively, this method could be preferred by fractionating the sample on an anion exchange resin before application to the cation exchange resin. In another alternative, it could be fractionated on an anion exchange resin and detected by MALDl directly. In yet another method, biomarkers could be captured on an immunochromatographic resin comprising antibodies that bind to the biomarkers, wash the resin to separate the unbound material, elute the biomarkers of the resin and detect the biomarkers eluted by MALDl or through SELDI. 3. Data analysis The analysis of analytes of time-of-flight mass spectrometry generates a spectrum of time of flight. The analyzed flight time spectrum does not usually represent the signal of a single pulse of ionization energy against a sample, but rather the sum of signals of a diversity of pulses. This reduces the noise and increases the dynamic range. Then, these flight time data are subjected to data processing. In the Ciphergen ProteinChip® programming elements, data processing commonly includes TOF to M / Z transformation to generate a mass spectrum, reference subtraction to eliminate increment shift and high frequency noise filtering to reduce high noise. frequency. The data generated by the desorption and detection of biomarkers can be analyzed with the use of a programmable digital computer. The computer program analyzes the data to indicate the number of biomarkers detected and optionally the intensity of the signal and mass molecules determined for each biomarker detected. The data analysis can include the steps of detecting the signal intensity of a biomarker and separating the data that deviates from a predetermined statistical distribution. For example, the observed types can be normalized, when calculating the height of each peak in relation to some reference. The reference may be background noise generated by the instrument and chemical compounds such as the energy absorbing molecule that is adjusted to zero on the scale. The computer can transform the resulting data into various formats for display. The standard spectrum can be displayed, but in a useful format, only the peak height and mass information are required from the spectrum view, producing a cleaner image and allowing biomarkers with almost identical molecular weights to be seen more easily. In another useful format, two or more spectra are compared, which conveniently highlight unique biomarkers and biomarkers that are up-regulated or down-regulated between samples. By using any of these formats, one can easily determine if a particular biomarker is present in a sample. The analysis involves in general the identification of peaks in the spectrum that represent an analyte signal. Peak selection can be made visually, but programming elements are available, as part of Ciphergen's ProteinChip® programming element package that can automate peak detection. In general, these training elements work by identifying signals having the signal-to-noise ratio greater than a selected threshold and marking the peak mass at the centroid of the peak signal. In a useful application, many spectra are identified to compare identical peaks present in a selected percentage of the mass spectrum. A version of these programming elements groups all the peaks that appear in the various spectra within a defined mass interval and assigns a mass (M / Z) to all peaks that are close to the midpoint of mass (M / Z). The programming elements used to analyze the data may include a code that applies an algorithm to the analysis of the signal to determine whether the signal represents a peak in a signal corresponding to a biomarker according to the present invention. The programming elements can also submit the data with respect to the peaks of the biomarker observed throughout ANN classification or analysis, to determine whether a biomarker peak or combination of biomarker peaks is present and indicates the status of the particular clinical parameter in exam. The analysis of the data can be "encrypted" to a variety of parameters that are obtained, either directly or indirectly, from the mass spectrometric analysis of the sample. These parameters include but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the logarithm or the height of one or more peaks and other manipulations. arithmetic of peak height data. 4. General Protocol for Detection by SELDI of Biomarkers in Chagas Disease A preferred protocol for detecting biomarkers of this invention is as follows. The biological sample to be tested, for example serum, is previously subjected to prefracement before SELDI analysis. This simplifies the sample and improves sensitivity. Prior to pre-fractionation, the serum (20 μL) is denatured a buffer solution of 9M Urea / 2% Chaps / 50 M Tris pH to 9.0 (pH U9 buffer). 30 μL of U9 are added to 20 μL of serum and then this diluted serum is subjected to anion exchange fractionation. A preferred pre-fractionation method involves contacting the sample with an anion exchange chromatographic material, such as Q HyperD (BioSepra, SA, a division of Ciphergen Biosystems, Inc.). Then, the bound materials are subjected to gradual pH elution using pH buffer solutions at pH 9, pH 7, pH 5, pH 4 and pH 3. (See Example 1 - List of pH regulating solutions) also as an elution of organic solvent (the sections in which the biomarkers are eluted are also indicated in Tables 1-4 and in the Figures by reference to the marker IDs, for example F1IH_ #, F2WSL_ #, etc.). Several fractions containing the biomarker are collected. Then, the sample to be tested (preferably pre-fractionated) is contacted with an affinity capture probe comprising a cation exchange adsorbent (preferably an array of ProteinChip WCX (Ciperghen Biosystems, Inc.)) or an IMAC adsorbent (preferably an IMAC3 ProteinChip array) (Ciphergen Biosystems, Inc.)), again as indicated in the marker ID listed in Tables 1-4 and Figures.

The probe is washed with a pH-regulating solution that will retain the biomarker while discarding the unbound molecules. The appropriate washes for each chip are described in the Example. Biomarkers are detected by laser desorption / ionization mass spectrometry. Alternatively, if antibodies recognizing the biomarker are available, for example in the case of data, these can be attached to the surface of a probe, such as a pre-activated ProteinChip PS10 or PS20 array (Ciphergen Biosystems, Inc.)). These antibodies can capture the biomarkers of a sample on the probe surface. Then, biomarkers can be detected, for example, by laser desorption / ionization mass spectrometry.

B. Detection by Immunological Test In another embodiment, the biomarkers of this invention can be measured by immunological test. The immunological test prefers biospecific capture reagents, such as antibodies, to capture the biomarkers. Antibodies can be produced by methods well known in the art, for example when immunizing animals with biomarkers, biomarkers that can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be simplified and used to generate antibodies by methods well known in the art. This invention contemplates traditional immunological tests including, for example, sandwich immunological tests including ELISA or fluorescence-based immunological tests, as well as other immunological tests of enzymes. In the SELDI-based immunological test, a biospecific capture reagent for the biomarker is attached to the surface of an MS probe such as a pre-activated ProteinChip array. Then, the biomarker is captured specifically on the biochip by means of this reagent and the captured biomarker is detected by mass spectrometry.

IV. DETERMINATION OF STATUS OF CHAGAS DISEASE SUBJECT A. Individual markers The biomarkers of the invention can be used in diagnostic tests to determine the status of Chagas disease in a subject for example, to diagnose Chagas. The phrase "Chagas disease status" includes distinguishing inter alia, chronic Chagas disease from non-Chagas disease and in particular, chronic asymptomatic Chagas disease against nonacute infection or status of acute Chagas disease versus non-infection. Based on this status, additional procedures may be indicated, which include additional diagnostic tests or procedures or therapeutic regimens. The power of a diagnostic test to correctly predict the status is commonly measured as the sensitivity of the analysis, the specificity of the analysis or the area under a receiver operating characteristic curve.

("ROC"). Sensitivity is the percentage of true positives that are predicted in a test as positive, whereas specificity is the percentage of true negatives that are predicted by a test to be negative. A ROC curve provides the sensitivity of a test as a 1-specificity function. The larger the area under the ROC curve, the more powerful the predictive value of the test. Other useful measures of the utility of a test are a positive prediction value and a negative predictive value. The positive predictive value is the percentage of real positives that prove positive. The negative predictive value is the percentage of real negatives that are tested as negative. The biomarkers of this invention show a statistical difference in the status of Chagas disease different from at least p < 0.05, - p < _ 10 ~ 2, p < _ 10"3, p <10" 4 or p < ^ 10"5. Diagnostic tests using these biomarkers alone or in combination show a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and even absolutely 100% Each biomarker listed in Tables 1-4 and in the Figures is differentially present in Chagas disease and therefore, each is individually useful to assist in the determination of Chagas disease status The method involves, first, quantifying the selected biomarker in a sample from a subject using the methods described herein, for example, capturing a biochip on SELDI followed by detection by mass spectrometry and secondly , compare the quantification with a diagnosed amount or cut that distinguishes a positive Chagas disease status from a negative Chagas disease status. quantified d of an upper or lower biomarker where a subject is classified as having a Chagas disease status in particular. For example, if the biomarker is regulated above normal during Chagas disease, then a quantification above the cutoff diagnosis provides a diagnosis of Chagas disease.

Alternatively, if the biomarker is regulated below normal during Chagas disease, then a quantification below the cutoff diagnosis provides a diagnosis of Chagas disease. As understood in the art, by adjusting the cutoff diagnosis used in an assessment, one can increase the sensitivity or specificity of the diagnosis assessment depending on the preference of the person diagnosing. The particular cutoff diagnosis can be determined, for example, by quantifying the amount of the biomarker in a statistically significant number of samples from subjects with various Chagas disease status, as carried out here, and plot the cuts to levels Desired specificity and sensitivity provided by the person diagnosing.

B. Combination of markers When individual biomarkers are used as diagnostic biomarkers, it has been found that a combination of biomarkers can provide high prediction values of a particular status compared to a single biomarker. Specifically, the detection of a plurality of biomarkers in a sample may increase the sensitivity and / or specificity of the test.

For example, in the protocols described in Example 1 below, 73 samples from Venezuelan patients were used to generate a mass spectrum. Of these 73 samples, 39 of the samples were obtained from patients chronically infected with Chagas disease and 34 were taken from healthy individuals living in the same endemic region. The peak of masses and heights was extracted from a series of data discovery. This data series was used to carry out a collection algorithm that uses regression tree analysis and classification (CART) (Ciphergen Biomarker Patterns Software ™). In particular, CART selects many subseries of random peaks. For each subject, CART generated improved or nearby decision trees to classify a sample for Chagas disease or without Chagas disease. Among the various tree decisions generated by CART, some have excellent sensitivity and specificity in distinguishing samples that have Chagas disease from those that do not. An exemplary decision tree for qualifying the Chagas disease status of a sample taken from a subject is presented in Figure 3. The identity of biomarkers used is indicated in Figure 3, with reference to Table 1. For example, the The biomarker in "Node 1" of Figure 3 is F1WH_2, corresponds to the biomarker of Chagas disease with an estimated mass of 13.6 kD. The specificity and sensitivity of the analysis for multiple biomarkers increases according to the number of biomarkers used in the decision tree that also increases. Figure 4B shows that 100% specificity and sensitivity greater than 94% can be achieved using the 5 biomarkers selected in this example. The sensitivity of the decision analysis tree is shown in an Income Table in the Figure under the column "Percentage correct" and in the line of Current class = "0". The specificity is shown in the same column in the Current Class row = "1". It is noted that the specification of decision trees, in particular the cut-off values used in making branched decisions, depends on the details of the valuation used to generate the discovery data series. The parameters for the acquisition of data from the valuation that produces the data used in the present analysis is provided in the Example. When developing a classification algorithm from, for example, a new series of samples or a different valuation protocol, the operator uses a protocol that detects these biomarkers and encodes the collection algorithm upon entering them.

C. Specific biomarkers for Chagas disease The methods are also provided to specifically qualify the status of Chagas disease in a subject as compared to the status of a different parasitic disease (that is, a non-Chagas disease), the The method comprises: (a) quantifying at least one biomarker in a biological sample from a subject, wherein at least one biomarker specifically indicates the presence of Chagas disease and does not indicate the presence of a different parasitic infection; and (b) correlate the quantification with the status of Chagas disease in comparison with the status of a different parasitic infection. In one embodiment, the biological sample is a serum sample. In one embodiment, biomarkers that specifically identify the presence or absence of Chagas disease when distinguished from a different parasitic infection, including infection by protozoa, helminths or malaria. In one embodiment, biomarkers that specifically identify the presence or absence of Chagas disease are distinguished from other infections caused by protozoa, including Leishmaniasis, African trypanosomiasis (sleeping sickness), and babesiosis. In one embodiment, biomarkers that specifically identify the presence or absence of Chagas disease are distinguished from other infections caused by kinetoplastidae or trypanosomal infections, including Leishmaniasis and African trypanosomiasis. In one embodiment, the biomarkers that specifically identify the presence or absence of an infection with T. cruzi are distinguished from an infection with T. bruce !, including T. brucei rhodesi and T. brucei gambi. In . In one embodiment, biomarkers that specifically identify the presence or absence of an infection due to Chagas disease are distinguished from other parasitic diseases and are selected from the group consisting of biomarkers with molecular weight of 8,351 kDa, 9.3 kDa, 7.3 kDa, 6.04 kDa, 4.4 kDa, 4.07 kDa and 5.1 kDa, as illustrated in Figures 7-9. The presence of a specific biomarker for Chagas disease, or the presence of a specific biomarker for Chagas disease above (or below) the cut-off level (that is, a comparatively greater or lesser presence of biomarkers) , is indicative of T. cruzi infection and is indicative of Chagas disease in an individual.

D. Determination of the risk for developing the disease In one embodiment, this invention provides methods for determining the risk of developing Chagas disease in a subject. Biomarker quantities or patterns are characteristic of several risk states, for example, high, medium or low. The risk of developing Chagas disease is determined by quantifying the relevant biomarker or biomarkers and either by submitting them to a classification algorithm or by comparing them with a reference quantity and / or biomarker pattern that is associated with the particular risk level.

E. Determination of disease status In one embodiment, this invention provides methods for determining the status of Chagas disease in a subject. Each state of Chagas disease has a characteristic amount of a biomarker or relative amounts of a series of biomarkers (a standard). The status of Chagas disease is determined by quantifying the relevant biomarker or biomarkers and then subjecting them to a classification algorithm or comparing them with a reference amount and / or biomarker pattern that is associated with the particular state.

F. Course of disease determination (progression / regression) In one embodiment, this invention provides methods for determining the course of the disease in a subject. The course of the disease refers to changes in the status of the disease over time including progression of the disease (worsening) or regression of the disease (improvement). Over time, the quantities or relative amounts (eg, the pattern) of the changes in the biomarkers. For example, certain biomarkers increase with the progression or regression of Chagas disease. Therefore, the trend of these markers either when it increases or decreases with the passage of time towards disease or not disease, indicates the course of the disease. Thus, this method involves quantifying one or more biomarkers in a subject in at least two different time points, for example, a first time and a second time and comparing this change in quantities, if any. The course of Chagas disease is determined based on these comparisons. Similarly, this method is used to determine the response to treatment. If the treatment is effective, then the biomarkers tend towards a normal state. If the treatment is not effective, biomarkers tend to indicate disease.

G. Administration in the subject In certain modalities of the methods to qualify the status of Chagas disease, the methods also include administering a treatment to the subject according to the status. This administration includes the actions of the subsequent physician or clinician to determine the status of Chagas disease. For example, if a doctor makes a diagnosis of Chagas disease, then a certain treatment regimen may be administered, just as the drugs described are effective in the treatment of Chagas disease such as nifutrimox, benznidazole and allopurinol. Alternatively, a negative diagnosis of Chagas disease in an individual showing symptoms associated with Chagas disease may continue with additional tests to determine whether the patient suffers from a T. cruzi-related parasite infection or is a disease unrelated to infection. caused by a trypanosome. If the diagnostic test provides an inconclusive data on the status of Chagas disease, further tests should be carried out. The additional embodiments of the invention are related to the communication of analysis results, for example for technicians, doctors or patients. In certain modalities, computers are used to communicate the results of analysis or diagnosis to both interested parties, for example doctors and their patients. In some modalities, the analyzes carried out or the evaluation is analyzed in a country or jurisdiction different to the country or jurisdiction where the results or diagnosis are communicated. In a preferred embodiment of the invention, a diagnosis based on the presence or absence in a test subject of any of the biomarkers included in Tables 1-4 or in the Figures is communicated to the subject as soon as possible after having obtained the diagnosis. The diagnosis can be communicated to the subject through the attending physician. Alternatively, the diagnosis can be sent to the test subject by e-mail or communicated to the subject by telephone. A computer can be used to communicate the diagnosis by email or telephone. In certain embodiments, the message contains results of a diagnostic test that can be automatically generated and released to the subject using a combination of equipment and computer programming elements that is familiar to those skilled in the art of telecommunications. An example of a health care-oriented communication system is described in U.S. Patent Number 6,283,761; however, the present invention is not limited to the methods where this particular communication system is used. In certain embodiments of the methods of the invention, all or some steps of the method, including the analysis of samples, diagnosis of diseases and communication of results of analysis of results or diagnoses, can be carried out in various jurisdictions (ie, external) .

V. GENERATION OF CLASSIFICATION ALGORITHMS FOR QUALIFYING THE STATUS OF CHAGAS DISEASE In some embodiments, the data derived from the spectrum (ie, mass spectrum or flight time spectrum) that are generated by using samples such as "known samples. "can then be used for a" determination "classification model. A "known sample" is a sample that has been pre-sorted. The data that have been derived from the spectrum and used to form the classification model are referred to as the "determination data series". Once determined, the classification model can recognize patterns of data derived from the generated spectrum using unknown samples. The classification model can then be used to classify unknown samples into classes. This can be used, for example, by predicting when or not a biological sample is associated with a certain biological condition (ie, Chagas disease against uninfected, or asymptomatic Chagas disease against acute Chagas disease). The determination of the data series that is used to form the classification model may comprise random data or reprocessed data. In some embodiments, random data can be obtained directly from the flight time spectrum or the mass spectrum and these can optionally be "reprocessed" as described above. Classification models can be formed using any suitable (or "determined") statistical classification method that attempts to segregate data bodies into classes based on objective parameters present in the data. Classification methods can be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2 No. 1, January 2000, whose teachings are incorporated by reference. In the supervised classification, the determined data contain examples of known categories are presented for a determination mechanism, which determines one or more series of relations that define each of the known classes. New data can then be applied to the determination mechanism, where the new data is classified using determination relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares regression (PLS) and major regression components (PCR)), binary decision trees (e.g. recurrent partition such as CART - classification and regression trees), artificial neural networks such as regression propagation networks, discriminant analyzes (eg, Bayesian classifier or Fischer analysis), logistic classifiers and vector support classifiers (support machines for vectors). A supervised classification method is a recurring partitioning process. The recurring partition processes use recurring partition trees to classify spectra derived from unknown samples. Additional approximate details of the recurring partitioning processes are provided in U.S. Patent Application No. 2002 0138208 Al to Paulse et al. , "Method for analyzing mass spectra". In other modalities, the classification models that are created can be formed using unsupervised determination methods. The unsupervised classification attempts to determine classifications based on similarities in the given data series without pre-sorting the spectrum from which the given data series was derived. Methods of unsupervised determination include group analysis. A group analysis tries to divide the data into "groups" or groups that ideally can have members that are very similar to each other very different from members of other groups. Similarly, this is quantified using certain metric distances, which quantifies the distance between the data items and the groups linked to the data items that are close to each other. Clustering techniques include the average of MacQueen's K algorithm and Kohonen's self-organizing algorithm map. Confirmed determination algorithms for use in classifying biological information are described, for example, in PCT International Publication No. WO 01/31580 (Barhill et al., "Methods and devices for identifying patterns in biological systems and methods of use thereof. "), U.S. Patent Application No. 2002 0193950 Al (Gavin et al.," Method or analyzing mass spectra "), U.S. Patent Application No. 2003 0004402 Al (Hitt et al.," Process for discriminating between biological states based on hidden patterns from biological data ") and U.S. Patent Application No. 2003 0055615 Al (Zhang and Zhang," Systems and methods for processing biological expression data "). Classification models can be formed on and used on any suitable digital computer. Suitable digital computers include micro, mino or large computers using any conventional or specialized operating system, such as an operating system based on Unix, Windows ™ or Linux ™. The digital computer used can be physically separated from the mass spectrometer that is used to create the spectrum of interest, or it can be coupled to the mass spectrometer. The determination of data series and classification models in accordance with the embodiments of the invention can be incorporated by computer codes that are executed or used by a digital computer. The computer code may be stored in any compatible computer media including optical or magnetic disks, devices, recordings, etc., and may be read in any computer programming language including C, C ++, visual basic, etc. The determination algorithms described above are useful both for developing classification algorithms of the biomarkers already described and for finding new biomarkers of Chagas disease. The classification algorithms in turn, form the basis for diagnostic tests by providing diagnostic values (for example, cut-off points) for biomarkers used alone or in combination.

SAW. EQUIPMENT FOR THE DETECTION OF BIOMARKERS OF THE CHAGAS ENFEMENSE In another aspect, the present invention provides equipment to qualify the status of Chagas disease, wherein the equipment is used to detect biomarkers according to the invention. In one embodiment, the kit comprises a solid support, such as a chip, a microtiter plate or a bed or resin having a capture reagent adapted thereto, wherein the capture reagent is linked to the biomarker of the invention. Thus, for example, the kits of the present invention may comprise probes for mass spectrometry of SELDI, such as ProteinChip® arrays. In the case of specific capture reagents, the equipment may comprise a solid support with a reactive surface and a container comprising the specific capture reagent. The kit may further comprise a washing solution or instructions for making a washing solution wherein the combination of the capture reagent and the washing solution allows the capture of the biomarker or biomarkers on the solid support for subsequent detection, for example, by mass spectrometry. The equipment may comprise more than one type of adsorbent, each present on a different solid support. In a further embodiment, such equipment may comprise instructions for appropriate operating parameters in the form of a label or a separate insert. For example, instructions can inform a consumer about how to collect the sample, how to wash the sheath or the particular biomarkers to be detected. In still another embodiment, the equipment may comprise one or more containers with biomarker samples to be used as a standard (s) for calibration.

VII. USE OF BIOMARKERS FOR CHAGAS DISEASE IN DETECTION ANALYSIS The methods of the present invention have other applications as well. For example, biomarkers can be used to select compounds that modulate the expression of biomarkers in vi tro or in vivo, such compounds in turn can be useful in the treatment or prevention of Chagas disease in patients. In another example, biomarkers can be used to verify the response to treatments for Chagas disease. In yet another example, biomarkers can be used in inheritance studies to determine if the subject is at risk of developing Chagas disease. Thus, for example, the kits of this invention could include a solid substrate having a cation substrate function, such as a protein biochip (e.g., an array of ProteinChip WCX2 Ciphergen) and a pH buffer solution of acetate. sodium for washing the substrate, also as instructions providing a protocol for measuring the biomarkers of this invention on the chip and for using these measurements to diagnose Chagas disease. Appropriate compounds for therapeutic tests may be selected initially by identifying compounds that interact with one or more biomarkers listed in Tables 1-4 or in the Figures herein. By way of example, the selection could include recombinantly expressing a biomarker listed in Table 1-4 or the Figures, purifying the biomarker and fixing the biomarker to a substrate. Test compounds would then be contacted with the substrate, commonly under aqueous conditions and the interactions between the test compound and the biomarker are measured, for example, by measuring elution rates as a function of the salt concentration. Certain proteins can recognize and cleave one or more biomarkers from Tables 1-4, in which case the proteins can be detected by verifying the division of one or more biomarkers in a standard assay, for example by gel electrophoresis of the proteins. In a related embodiment, the ability of a test compound to inhibit activity (ie,, non-enzymatic or enzymatic) of one or more of the biomarkers of Tables 1-4 or of the Figures can be measured. The skilled artisan will recognize that the techniques used to measure the activity of a particular biomarker will vary depending on the function and properties of the biomarker. For example, the enzymatic activity of a biomarker can be analyzed provided that an appropriate substrate is available and provided that the concentration of the substrate and the appearance of the reaction product are easily measurable. The ability of potentially therapeutic test compounds to inhibit or enhance the activity of a given biomarker can be determined by measuring the rates of catalysis in the presence or absence of the test compounds. The ability of a test compound to interfere with a non-enzymatic (eg, structural) function or activity of one of the biomarkers of Tables 1-4 or of the Figures can also be measured. For example, self-assembly of a multi-protein complex that includes one of the biomarkers described herein can be verified by spectroscopy in the presence or absence of a test compound. Test compounds capable of modulating the activity or expression of any of the markers described in Tables 1-4 or the Figures can be administered to patients suffering from or at risk of developing Chagas disease. For example, administration of a test compound that increases the activity of a particular biomarker may decrease the symptoms of Chagas disease in a patient if the activity of the particular biomarker in vivo prevents the accumulation of dangerous metabolites associated with Chagas disease. Conversely, administration of the test compound that decreases the activity or expression of a particular biomarker can decrease or alleviate the symptoms of Chagas disease in a patient whose increased activity or expression is responsible, at least in part, for the onset of Chagas disease or the ability of the parasite to spread and effect the disease state in a patient. In a further aspect, the invention provides a method for identifying compounds useful for the treatment of disorders such as Chagas disease that are associated with increased levels of modified forms of the biomarkers listed in the Tables herein or the full biomarker proteins. length. For example, in one embodiment, similar extracts or expression libraries may be selected for compounds that inhibit the cleavage of full length proteins associated with the biomarkers listed in the Tables, which include MlP-la, MllO, ApoIA , Fibronectin and C3 anaphylatoxin to form truncated forms of these biomarker proteins. In one embodiment of such screening analysis, cleavage of biomarker proteins, in which MlP-la, MllO, ApoA, Fibronectin and anaphylatoxin C3 are included, can be detected by attaching a fluorophore to the biomarker protein that remains cooled when the biomarker protein is unscreened but fluoresces when the protein is cleaved. Alternatively, a full-length version of a biomarker protein, wherein MlP-la, MllO, Apo 1A, Fibronectin and anaphylatoxin C3 or any other biomarker described herein can be modified to revert to the amide bond between the Non-cleavable X and Y amino acids can be used to selectively bind or "trap" the cellular protease that cleaves the full-length biomarker protein at that site in vivo. Methods to select and identify proteases and their objectives are well documented in the scientific literature, for example in Lopez-Ottin et al. (Nature Reviews, 3: 509-519 (2002)). In still another embodiment, the invention provides a method for treating or reducing the advance or probability of a disease, for example Chagas disease, which is associated with increased levels of proteins truncated by T. cruzi enzymes (i.e., cruzipain). For example, combination libraries can be selected by compounds that inhibit the ability of a T. cruzi protease (i.e., cross-link) to cleave one or more of the biomarker proteins described herein. Methods of selecting chemical libraries by such compounds are well known in the art. See, for example, Lopez-Ottin et al. (2002). Alternatively, inhibitory compounds can be intelligently designed based on the structure of T. cruzi enzymes, in which they include cruzipain. At the clinical level, the selection of a test compound includes obtaining samples from the test subject before and after the subjects have been exposed to a test compound. The levels in the samples of one or more of the biomarkers described in Tables 1-4 and in the Figures can be measured and analyzed to determine if the levels of the biomarkers change after exposure to a test compound. The samples can be analyzed by mass spectrometry, as described herein or the samples can be analyzed by appropriate means known to those skilled in the art. For example, the levels of one or more of the biomarkers described herein can be measured directly by Western blotting using radiolabelled or fluorescently labeled antibodies that bind specifically to biomarkers. Alternatively, changes in mRNA levels encoding one or more biomarkers can be measured and correlated with the administration of a given test compound to a subject. In a further embodiment, changes in the level of expression of one or more of the liberators can be measured using in vi tro methods and in vitro materials. For example, cultured human tissue cells that express or are capable of expressing one or more of the biomarkers described herein can be contacted with test compounds. Subjects who have been treated with test compounds will be systematically examined for any physiological effects that may result in the treatment. In particular, test compounds will be evaluated for their ability to decrease the likelihood of disease in a subject. Alternatively, if the test compounds have been administered to subjects who have been previously diagnosed with Chagas disease, the test compounds will be selected as to their ability to stop or slow the progression of the disease. The invention will be described in more detail by means of specific examples. The following examples are offered for illustrative purposes and are not intended to limit the invention in any way. Those of skill in the art will readily recognize a variety of non-critical parameters that can be changed or modified to produce essentially the same results.

VIII EXAMPLES A. EXAMPLE 1. DISCOVERY OF BIOMARKERS FOR CHAGAS DISEASE For the study that led to the discovery of Chagas disease, the biomarkers listed in Table 1, a study set consisting of 73 samples, were used. Of these 73 samples, 39 of the samples were obtained from patients chronically infected with Chagas disease and 34 were taken from healthy individuals living in the same endemic region. The samples were fractionated and evaluated according to the following protocol. 1. FRACTIONING OF ANIONIC EXCHANGE Before the pre-fractionation of anion exchange, the serum (20 μl) is denatured using a pH regulating solution of 9M urea / 2% Chaps / 50 mM Tris pH 9.0 pH 9.0 (pH U9 buffer). 30 μl of U9 are added to 20 μl of serum and then this diluted serum is subjected to anion exchange fractionation. List of pH regulating solutions for anion exchange fractionation -.

Ul (1 M urea, 0.22% CHAPS, 50 mM Tris-HCl pH 9) 50 mM Tris-HCl with 1% OGP pH 9 (wash buffer 1) 50 mM Hepes with 0.1% OGP pH 7 ( washing pH buffer solution 2) 100 mM Na Acetate with 0.1% OGP pH 5 (wash buffer 3) 100 mM Na Acetate with 0.1% OGP pH 4 (washing buffer 4) 100 mM Na Acetate with 0.1% OGP pH 3 (washing buffer pH 5) 33.3% isopropanol 3% / 16.7% acetonitrile / 0.01% trifluoroacetic acid (washing buffer pH 6) Note : Do not take aliquots of the wash pH regulating solution 6 to the pH buffer tray until wash buffer 5 is applied to the resin. This ensures that the evaporation of volatile organic solvents will not be a problem. List of materials: Filter plate 6-cavity boxes in v-96 cavities, marked F1-F6 a. Washing resin Resin is prepared by washing Hyper Q DF resin (BioSepra, Cergy, France) 3 times with 5 bed volumes with 50 mM Tris-HCl pH 9. It is then stored in 50 mM TrisHCl pH 9 in a suspension at room temperature. fifty%. b. Balancing resin 125 μL of Hyper Q DF is added to each cavity in the filter plate The pH-regulating solution is filtered 150 μL of Ul is added to each cavity The pH-regulating solution is filtered 150 μL of Ul is added to each cavity The pH-regulating solution is filtered 150 μL of Ul is added to each cavity pH-regulating solution is filtered c. Serum is bound with resin 150 μL of sample from each tube is pipetted into the appropriate cavity in the vortex filter plate 30 seconds to 4o d. Fractions are collected. The 96-well cavity V box Fl is placed under the filter plate. The flow through the plate Fl is collected. 100 μL of washing buffer pH 1 is added to each well of the filter plate. vortex 10 minutes at room temperature (RT) The pH 9 eluent is collected on plate Fl Fraction 1 contains the flow through and the eluent of pH 9 100 μL of Wash pH 2 buffer is added to each plate. filter cavity Vortex 10 minutes at room temperature (RT) The 96-well F2 cavity plate V is placed under the filter plate. Fraction 2 of the F2 plate is collected. 100 μL of buffer pH wash 2 to each filter cavity plate Vortex 10 minutes at room temperature (RT) The rest of fraction 2 is collected in plate F2 Fraction 2 contains the eluent of pH 7 100 μL of standard solution is added from the washing pH 3 to each filter cavity plate Vortexed 10 minutes at room temperature (RT) The 96-well F3 cavity plate v is placed under the filter plate Fraction 3 of the F3 plate is collected 100 μL of buffer is added from washing pH 3 to each cavity of the filter plate Vortexed for 10 minutes at room temperature (RT) The remainder of fraction 3 is collected in plate F3 Fraction 3 contains the eluent of pH 5 100 μL is added of washing buffer pH solution 4 to each filter cavity plate Vortexed for 10 minutes at room temperature (RT) The 96-well, 96-well cavity plate F4 is placed under the filter plate. Fraction 4 of the plate F4 100 μL of Wash pH 4 buffer is added to each well of the filter plate Vortexed for 10 minutes at room temperature (RT) The remainder of fraction 4 is collected on plate F4 Fraction 4 contains the eluent of pH 4 100 μL of Wash pH 4 buffer is added to each filter cavity plate Vortexed for 10 minutes at room temperature (RT) The 96-well F5 cavity plate v is placed under the plate of the filter Collect fraction 5 from plate F4 Add 100 μL of washing buffer pH 5 to each well of the filter plate. Vortex 10 minutes at room temperature (RT). Collect the remainder of fraction 5 in the well. plate F5 Fraction 5 contains the eluent of pH 3 100 μL of washing buffer pH 6 is added to each filter cavity plate Vortexed for 10 minutes at room temperature (RT) Cavity plate v 96 cavities F6 under the filter plate Fraction 6 of the F6 plate is collected. 100 μL of wash buffer pH 6 is added to each cavity of the filter plate. Vortexed for 10 minutes at room temperature (RT). collect the remainder of fraction 6 on plate F6 Fraction 6 contains the eluent of organic solvent Freeze up to the procedure with the chip link protocol 2. Chip link protocol Samples are processed using a ProteinChip IMAC-3. Material: Bioprocessors IMAC-3 Chips Pap Vortex Pen (VWR VX-2500 multi-tube vortex device) PH regulating solution of the IMAC3 chip: A) Linkage buffer: 100 mM sodium phosphate + 0.5 M NaCl pH 7.0 + 0.1% Triton X 20% B) Copper: 100 mM CuS04 + 0.1% Triton X 20% ) 100 mM sodium acetate pH 4.0 + 0.1% Triton X 20 1. The chip is placed in the bioprocessor 2. The IMAC chip is loaded with copper: 50 μl / 100 mM CuS04 cavity is applied 3. Vortexed 5 minutes (speed 100 rpm) at room temperature 4. Remove CuS04 5. Wash with water 120 μl / cavity 6. Vortex 5 minutes (speed 100 rpm) 7. Neutralize chips: Add 50 μ / 100 mM Na acetate cavity pH 4.0 8. Solution 9 is removed. 120 μl / cavity 10 is washed with water. Vortexed 5 minutes (speed 100 rpm) 11. Steps 9 and 10 are repeated twice additional 12. Chips are equilibrated: 120 μl of binding buffer is added (PBS / 5M NaCl, pH 7.5) 13. Vortexed 5 minutes (100 rpm) 14. Fraction an a link the chips: The waste is discharged and 180 μl of binding pH buffer and 20 μl of fractions (containing samples) are added . Vortex is submitted 45-60 minutes (100 rpm) 16. Discharge and wash (PBS / 0.5M NaCl, 150 μl / well) 17. Vortex 5 minutes (100 rpm) 18. Repeat steps 16 and 17 two additional times 19. Rinse the chip with dH20 (150 μl / well) 20. Add to matrix: Remove the top of the bioprocessor and gasket 21. Rinse the chips quickly with dH20 22. Dry the chips 23 The points with pen of PAP 24 are enclosed in a circle. 0.5 μl of SPA is added to the chips twice (the points between the addition are air-dried) Ciphergen normally supplies EAM as 5 mg of dry powder in a tube. 100 μl of 100% acetonitrile is added (final concentration 50% ACN) + 50 μl of 2% trifluoroacetic acid (final concentration 0.5% TFA) + 50 μl of dH20. It is vortexed one minute (high speed) and left in the bale for 5 minutes. It is centrifuged 2 minutes at high speed to agglomerate any particles It is dried It is read in the interval of one hour ProteinChip® arrangement of weak cation exchanger (WCX2) The chip is placed in a bioprocessor and 150 μl of binding buffer is added to each cavity.

Incubate for 5 minutes at room temperature in the vortex (shaker setting). The buffer solution is removed. Step 1 is repeated. The pH-regulating solution is removed and immediately 90 μl of binding buffer + 10 μl of column fraction Q are added immediately. It is incubated in the shaker for 30 minutes The sample is aspirated from the well and washed each cavity with 150 μl / cavity of binding buffer, 5 minutes at room temperature with shaking. The sample is removed. Stage 4 is repeated twice for a total of three washes. The chip is removed from the processor and the chip is rinsed briefly with H20 in a tube. The chip arrangement is air dried, the points with PAP pen are enclosed in a circle. It dries by air. 0.5 μl EAM / dot is added twice. Denaturing agents such as urea are compatible with this chip surface and will alter the protein level. GITC is a salt and will inhibit the binding like other salts. Recommended binding pH regulatory solutions: 50 mM Tris, HEPES or acetate (pH 4.0-9.5) Recommended sample dilution: 50-2000 μg / ml total protein Severity modifiers: the addition of salts or changes in pH will alter the severity of the link stage (see notes of the chip user =. 3. Data acquisition adjustment: Energy absorbing molecule: 50% of SPA High mass is adjusted to 100000 Daltons, optimized for 2000 Daltons at 100000 Daltons The starting laser intensity is adjusted to 200. The sensitivity of the initial detector is adjusted to 8. The mass is focused at 8000 Daltons. The mass derailleur is adjusted to 1000 Daltons. It adapts data acquisition method to quantification Seldi. Adjustment parameters of Seldi delta 20. delta to 4, transients according to position 10 to 80. Warm up positions are adjusted with two shots to intensity 225. The sample is processed. The methods used to analyze the data (shown in Table 1) are described above. The representative spectra appear in Figure 1. 4. Identity determination of the biomarker Identification of the 100 kDa protein The proteins were separated on an acrylamide gel and a band containing the biomarker was cut from the gel. The protein in the band was faded. The gel was dried using acetonitrile and then subjected to fixation in a trypsin solution. The elements of the digest were analyzed on a PBSII Ciphergen mass spectrometer. The determined masses were used to interrogate the NCBInr protein database (using ProFound programming elements) that identified the protein having the same tryptic digest pattern. Using the techniques mentioned above, a protein of approximately 110 kDa was identified as a highly significant biomarker for Chagas disease. This protein, designated as MllO, is a new protein with homology to a predicted protein (LM15-1.32) encoded by Leishmania major. A summary of the search results and sequenced fragments of MllO are shown in Figure 2.

Identification of the 7,861 kDa protein Another biomarker with a 7.8 kD mass, M7.861, was identified as human MlP. The identity of the protein was confirmed using an ELISA analysis and an antibody specific to MlP-la. The measurement of levels in MlP-la in subjects infected with Chagas disease against non-infected subjects demonstrated that the mean level of MlP-la in the serum of infected subjects (20.73 pg / ml) was significantly higher than the average level against subjects uninfected (12.22 pg / ml).

Identification of the purified 13.6 kDa protein The 13.6 kDa protein was detected in fraction one in IMAC-Cu and WCX arrays using SPA as the EAM. A tryptic digest of the 13.6 kDa protein was analyzed by mass spectrometry in a single MS mode. The single largest peaks were further analyzed with MS in tandem, and the resulting CID data were submitted to the Mascot database for identification. The following ions were identified as triptych fragments of Apoliprotein A-I: m / z Position Sequence 1012.57 207-215 AKPALEDLR 1157.62 178-188 LEALKENGGAR 1230.71 216-226 QGLLPVLESFK 1301.64 161-171 THLAPYSDELR 1318.64 227-238 LSPLGEEMRDR 1386.71 227-238 VSFLSALEEYTK The amino acid sequence of ApoA-I is shown below. Peptides identified by CID fragmentation are highlighted in italics. 1 11 21 31 41 51 l i l i l í 1 UEP &QS5SD mnit VtVD VLKDSG? FfiYV SQEESSA SK QMpESiia »? © SVTSTFS L 61 SEQ SPVTQ3 &T8MJI1BKE? E G RQEMSKDIt EEVKM Qt X ISDFQKKWQE EHEIÍ? RQKVE 121 P ?? BAEüQEG & BQK SB? IQE í? B & SEMBiJ JÜRH? HVDñ B. TS? PVSDBL HQRUÍ & RI? Í 181 LE eßASOO EYH &Kñ! EEHI? BThSEK &KPA LED? R? ^ S ?? P If? ÉSFKffBFX, SdlkEE? KKI » 241 HTQ all six triptych fragments were from the C-terminal half of the protein. The systematic fragmentation of amino acids from the N terminus ended in Argl23 (cut of trypsin). It was calculated that the molecular weight of the remaining C-terminal half was 13.571.40 Da (the proposed N-terminal Alal24 is underlined). Thus, the 13.6 kDa fragment corresponds to amino acids 124-243 of the full length protein.

Identification of the 8.1 kDa protein The 8.1 kDa protein was detected in fraction 1 in WCX arrays using SPA as the EAM. A triptych digest of the 8.1 kDa protein was analyzed by mass spectrometry in a single MS mode. The single largest peaks were further analyzed with MS in tandem and the resulting CID data were submitted to the Mascot database for identification. The following ions were identified as tryptic fragments of Anaphylatoxin Complement C3: m / z Position Sequence 1095.58 42-51 FISLGEACKK 1339.59 18-28 ELRKCCEDGMR 1588.74 52-64 VFLDCCNYITELR 1716.84 51-64 KVFLDCCNYITELR The amino acid sequence of C3 is shown below. The peptides identified by CID are highlighted in italics. 1 11 21 31 41 53. 1 S? OHEEKBMIJ and iS? GKSPKE R K iCCE2XSMBW P iffiS'SCQRET R iFTSKEñCK K; VF DCCNYI YES? E &fíRQHJSUL SSL € ñR Confirmation of the 8.1 kDa protein identified by immunological test Monoclonal anti-C3a antibody and control mouse IgG were coupled to Protein A HyperD beads. The 506N serum sample contained approximately equal amounts of protein according to the profiling study. proteins from 8. lkDa to 8.9 kDa was incubated with beads. The unbound proteins were removed by washing with PBS and the bound proteins were eluted with 0.1M acetic acid. The profiling of the eluted fractions showed that the 8.1 kDa and 8936 Da proteins bind specifically to the anti-C3a antibody, but not to the control mouse IgG, thus confirming that both proteins are derivatives of human C3 anaphylatoxin (Figure 5) . C3 convertase activates Complement C3 by cleaving the alpha chain, releasing anaphylatoxin C3a. In the blood, the activity of C3a is under the control of carboxypeptidase N. The enzyme rapidly cleaves the C-terminal arginine, thereby generating C4ades-Arg (molecular weight 8938.46). It appears that the 8.1 kDa protein is the additional degradation / cleavage product of C3ades-Arg. The systematic removal of N-terminal amino acids would result in the 8152.56 Da peptide. In contrast, removal of the C-terminal amino acid would result in the 8132.52 Da peptide. Accordingly, the 8.13 kDa biomarker corresponds to the C-terminal truncation of C3a, specifically amino acids 1-68 of a full-length protein. Most probably, this polypeptide is generated by cleavage of trypsin on Arg69 followed by removal of Arg by carboxypeptidase N. The proposed C-terminal Ala68 is underlined. Table 1 indicates the representative fraction and chip conditions in which the truncated C3 anaphylatoxin biomarker was observed.

Identification of the 28.7 kDa protein As shown in Table 1, the 28.7 kDa protein was detected in fraction 1 in the WCX array using the SPA as the EAM. A tryptic digest of the 28.7 kDa protein was analyzed by mass spectrometry in a single MS mode. The single largest peaks were further analyzed with MS in tandem and the resulting CID data were submitted to the Mascot database for identification. The following ions were identified as tryptic fragments of fibronectin: m / z Position Sequence 1348.64 222-234 GNLLQCICTGNGR 1401.66 27-36 HYQINQQWER 1527.63 86-99 DSMIWDCTCIGAGR 1677.77 37-52 TYLGN7? LVCTCYGGSR 1707.78 242-258 HTSVQTTSSGSGPFTDV 1866.85 102-118 ISCTIANRCHEGGQSYK 2789.18 53 -76 GFNCESKPEAEETCFDKYTGNTYR The N-terminal amino acid sequence of fluoronectin is shown below. The peptides identified by CID fragmentation are highlighted in italics. 11 21 31 41 51 i I j I I 1 QAQQMVQPQS wmsQap & ß C ?? HGKHYQ? IJQQHEKTEDG flasveirc? GG ssesmesaíS '61 E ETCFDK? xeara-SHrour E &KBß? fir aCTcxeaßRG -Riscrimmc H? rßsQS sx 121 BTffiRPHE? G GSMBSCTCLG GKesWTCKS? íESKClDH & ft. GTSYW6BTÍ \ T BK YQ ^ WMMV 1É1 DCPCSiGEagS ET? CTSHHRG BDQD? R? B ITD? 6ISKKDM? TGNL QCSC T? BfiTS XiaS 241 s SVQTFSS GSGSF333VRA avyQpQ &HPQ 23? J ?? GHCV D SGWXSVGMQ íJI¿X3EEííJQQ All seven triptych fragments correspond to N-terminal fibronectin. Importantly, a fragment with an M / Z of 107 did not have a tryptic cut in the C-terminal truncation, strongly suggesting that this is the C-terminal termination of the 28.7 kDa protein. Actually, the calculated molecular weight of the N-terminal sequence for Val258 is 28, 765.95 Da. This fragment contains 19 Cys, which is known to be involved in 9 bridges (-18 Da). In addition, the N-terminal Gln is modified in the pyrrolidonecarboxylic acid. (-17 Da). The molecular weight is then 28,731 Da. More preferably, this polypeptide is generated by the cleavage of trypsin on Arg259 followed by removal of Arg by carboxypeptidase N. The C-terminal Val258 amino acid is underlined in the above sequence. Thus, this 28.7 kDa protein corresponds to amino acids 1-258 of full length fibronectin (see, for example, marker F1WH_6 in Table 1). The 28.7 kDa fragment may represent the fibronectin digestion product by cruzipain, a trypanosomal protein that binds the fibronectin network present in cardiac tissue.

Identification of the 24.7 kDa protein The 24.7 kDa protein was detected in fraction 4 in the IMAC-Cu and WCX arrangement using SPA as the EAM. A tryptic digest of the 24.7 kDa protein was analyzed by mass spectroscopy in a simple MS mode. The single largest peak was further analyzed with MS in tandem and the resulting CID data were submitted to the Mascot database for identification. The following ions were identified as tryptic fragments of Apolipoprotein AI: m / z Position Sequence 1031.51 141-149 LSPLGEEMR 1226.54 1-10 DEPPQSPWDR 1301.64 161-171 THLAPYSDELR 1400.67 28-40 DYVSQFEGSALGK 1612.78 46-59 LLDNWDSVTSTFSK 1723.94 117-131 QKVEPLRAELQEGAR 1815. 85 24-40 DRSGDYVSQFEGSALGK The amino acid sequence of ApoA-I is shown below. The peptides identified by CID fragmentation are highlighted in italics. 1 13. 21 31. 4-1 53 l i l i l a De? ^? & S & s • s? & e &e < M j &BSGEBv? SQFSSSZISSK is &HtKüiiE ?? r SSVZS? FSEÍ, Sx H = G £ / SFVTQ¡S HD5 The £ gírß QÜBOSStSKDL EEVTCFílWQ &? IQDFQIQGÍÍQ EMEli-OÍQíraE- I3a sXJSS ?? J QBSA BqKESEajQ ?? ASEKI = &RAHÚ S feSLEHrajMiS gS rUgaaSES ßÜKi ?? lEIES. 1SI? J? ESAGlRH? A. í? í? OiKñ.TBE £ rj Sl? iHEtíaKP ?. IiEDl &QS P WKESP VS Ii SñIEE? EKL 2íi KTQ ~ The full length molecular weight of ApoA-I is 28,078.62. several fragments correspond to the N-terminal part of ApoA-I. in contrast, the C-terminal fragments of the 13.6 kDa protein are not detected in the 24.7 kDa protein digest. Thus, the 24.7 kDa protein is the C-terminal truncation of ApoA-I, corresponding to amino acids 1-214 of the full length protein (see for example, the biomarker F41H_4 in Table 1). The systematic removal of C-terminal amino acids resulted in the theoretical but molecular sequence of 24,756 kDa. This polypeptide, then, is similarly generated by the cleavage of trypsin on Arg215 followed by removal of Arg by carboxypeptidase N. C-terminal Leu214 is underlined in the sequence of Apo A-I above.

Identification of the 16.3 kDa protein The 16.3 kDa marker replicated the appearance of the 8133 Da marker identified as truncated C3a. However, the mass of the 16.3 kDa marker strongly suggests that this was a marker dimer of 8133 kDa. The protein content of 8133 Da of two samples was very high (Chagas disease positive) and another very low (negative to Chagas disease) and were analyzed by using a bed-based immunological assay with monoclonal Ab against C3a. As expected, the protein decrease of 8133 Da was very specific. Similarly, the 16.3 kDa protein was specifically decreasing from the positive sample by C3a Ab, but not for the control of the mouse IgG antibody, indicating that the 16.3 kDa protein is a truncated C3 dimer corresponding to amino acids 1-68 of the full length protein (see, for example, F1IH_2 and the corresponding monomer biomarker, F1IL_7, in Table 1). This dimer appeared to be resistant to DDT, therefore it is not a Cys bridge dimer.

Identification of 9.3 and 10.1 kDa proteins The 9.3 kDa and 10.1 kDa markers (eg, F1WL_3 and F1WL_1, respectively) were co-purified by reverse anion exchange chromatography. These two markers migrated together by reduced and unreduced SDS-PAGE. The digestion of trypsin from the bands extracted from the gel showed the same Apolipoprotein A-1 fragments identified for the 13.6 kDa marker, except for a peptide present in the 13.6 kDa digest, but absent in the 9.3 / 10.1 kDa digest. The last tryptic fragment is the most N-terminal in the 13.6 kDa sequence, indicating that both 9.3 kDa and 10.1 kDa polypeptides represent an additional degradation of ApoAl in the N- to C-terminal direction. The sequential removal of the amino acid in the N-terminus of ApoAl resulted in theoretical molecular weights of 9306.59 Da (observed molecular weight of 9307 Da) and 10069.46 Da (observed molecular weight 10070 Da) for the candidate biomarkers. Thus, the 9.3 kDa biomarker corresponds to a fragment consisting of amino acids 161-243 full-length ApoAl, while the biomarker of 10.1 kDa corresponds to a fragment consisting of amino acids 154-243 full-length ApoAl.

B. DISCOVERY OF ADDITIONAL BIOMARKERS Using protocols similar to those described in Example A above, additional series of samples were analyzed. The studies included samples taken from patients with parasitic diseases, such as the following: Table IB For example, Table 2 in the column "Chagas versus health" shows the results of a biomarker in a study discovered by analyzing samples taken from approximately 40 patients infected with Chagas disease against an equal number of geographically approximate non-infected controls. The infected group included 11 Guatemalan patients with acute Chagas disease, 12 patients from Cuba, 10 patients from Venezuela infected with chronic Chagas disease and 3 Canadian patients infected with Chagas disease. To obtain the group of biomarkers shown in the column "Chagas disease against Chagas disease", we used a sample that included 11 Guatemalan patients with acute Chagas disease, 12 patients from Cuba and 10 patients from Venezuela infected with Chagas disease. chronic as well as 3 Canadian patients infected with Chagas disease. This sample was analyzed against the group that included the group of uninfected patients as well as 42 patients infected with other parasitic diseases (Babesia, Chagas disease, Leishmania, Malaria, Toxoplasma). The p-values and specificities of some preferred biomarkers were highlighted with bold letters in Tables 2A-2X. Tables 3 and 4 collect a preferred group of biomarkers from each study with high degrees of sensitivity and specificity, as indicated in Table 2 (typically, p values less than 0.006, ROB greater than 0.7 or less than 0.3). Note that when combinations of biomarkers were used, an important consideration to increase specificity should be used for biomarkers whose level of expression and / or intensity are independent of each other Table 2A. - F5ISL D) Chagas vs healthy Chagas vs No - Chagas M / Z average Main value p ROC value p ROC (kDa) F5ISL 1 0.43993 0.59 0.28210 0.57 2437.21 F5ISL 2 0.42433 0.46 0.15326 0.41 2473.05 F5ISL 3 0.13587 0.61 0.27409 0.56 2507.49 F5ISL 4 0.95752 0.49 0.57520 0.46 2541.94 F5ISL 5 0.24129 0.61 0.15326 0.61 2576.33 F5ISL 6 0.04579 0.71 0.00005 0.76 3170.99 F5ISL 7 0.15811 0.34 0.65131 0.47 4253.38 F5ISL 8 0.76957 0.56 0.00787 0.67 4274.46 F5ISL 9 0.09871 0.66 0.17516 0.42 4632.14 F5ISL 10 0.40905 0.56 0.41078 0.45 7928.86 F5ISL 11 0.04579 0.30 0.94236 0.51 8145.12 F5ISL 12 0.19192 0.34 0.00568 0.35 28104.33 F5ISL 13 0.00715 0.76 0.00047 0.71 75548.95 Table 2B - F5ISH n > Chagas vs. healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (KDa) F5ISH 1 0.23336 0.39 0.00615 0.34 27776.33 F5ISH 2 0.04394 0.31 0.00004 0.26 28036.18 F5ISH 3 0.01998 0.25 0.00006 0.24 28269.46 F5ISH 4 0.26845 0.61 0.00173 0.67 37686.13 F5ISH 5 0.05367 0.28 0.00567 0.33 43248.19 F5ISH 6 0.13261 0.36 0.00100 0.31 44584.56 F5ISH 7 0.03832 0.28 0.00030 0.29 59454.95 F5ISH 8 0.11861 0.33 0.00292 0.32 60518.21 F5ISH 9 0.07853 0.69 0.00060 0.71 75647.95 F5ISH 10 0.00497 0.78 0.00012 0.75 103795.90 F5ISH 11 0.01468 0.22 0.01229 0.34 154980.69 F5ISH 12 0.37906 0.42 0.02678 0.40 160953.72 Table 2C - F6ISH ro Chagas vs healthy Chagas vs No - Chagas M7Z average Main value p ROC value p ROC (kDa) F6ISH 1 0.83326 0.50 0.64223 0.53 10196.53 F6ISH 2 0.13205 0.70 0.01328 0.65 18089.54 F6ISH 3 0.35596 0.64 0.00238 0.70 24752.79 F6ISH 4 0.04294 0.27 0.07993 0.38 28084.41 F6ISH 5 0.01582 0.24 0.09303 0.41 28275.51 F6ISH 6 0.02057 0.24 0.18608 0.39 28400.83 F6ISH 7 0.68558 0.44 0.04704 0.37 55418.73 F6ISH 8 0.27791 0.39 0.0O044 0.27 55962.00 F6ISH 9 0.12393 0.33 0.0O037 0.27 56167.48 F6ISH 10 0.08313 0.33 0.0O119 0.27 56414.40 F6ISH 11 0.10888 0.33 0.0O015 0.26 57022.49 F6ISH 12 0.12393 0.30 0.0O014 0.28 57908.91 F6ISH 13 0.21241 0.39 0.0O405 0.32 59108.57 F6ISH 14 0.25022 0.36 0.01023 0.33 60116.69 F6ISH 15 0.00030 0.90 0.0O003 0.77 75426.74 F6ISH 16 0.02651 0.76 0.02372 0.62 84266.77 F6ISH 17 0.12393 0.64 0.0O158 0.71 133669.32 Table 2D - F6ISL ID Chagas vs healthy Chagas vs No - Chagas M / Z average Main] value p ROC value p ROC (kDa) F6ISL 1 0.87305 0.46 0.97937 0.49 3321.40 F6ISL 2 0.59429 0.56 0.0O496 0.67 5102.80 F6ISL 3 0.37949 0.43 0.0O203 0.67 6194.40 F6ISL 4 0.65074 0.54 0.25529 0.43 6632.75 F6ISL 5 0.93632 0.49 0.21460 0.43 6846.99 F6ISL 6 0.15811 0.39 0.21780 0.42 8937.06 F6ISL 7 0.54020 0.56 0.03780 0.62 24160.09 F6ISL 8 0.45586 0.41 0.02188 0.36 43910.53 F6ISL 9 0.20114 0.39 0.0O078 0.29 44015.50 F6ISL 10 0.11007 0.34 0.0O215 0.30 56654.33 F6ISL 11 0.03100 0.29 0.0O047 0.28 56807.45 F6ISL 12 0.11613 0.36 0.0O215 0.31 56852.91 F6ISL 13 0.29898 0.38 0.0O320 0.32 59020.40 F6ISL 14 0.47212 0.56 0.03053 0.63 74105.59 F6ISL 15 0.01537 0.74 0.0O339 0.67 75476.22 Table 2E - F4ISH ID Chagas vs healthy Chagas vs No - Chagas M / Z average Main value p ROC value p ROC (kDa) F4ISH 1 0.24430 0.34 0.01559 0.35 11705.05 F4ISH 2 0.06000 0.29 0.02338 0.36 13598.02 F4ISH 3 0.00940 0.22 0.1O260 0.40 13996.67 F4ISH 4 0.00501 0.19 0.0O692 0.34 14076.61 F4ISH 5 0.00092 0.17 0.0O045 0.26 14162.34 F4ISH 6 0.00074 0.17 0.0O006 0.25 14203.94 F4ISH 7 0.00172 0.17 0.0O018 0.26 14252.34 F4ISH 8 0.01560 0.24 0.0O735 0.32 28304.14 F4ISH 9 0.00501 0.21 0.0O421 0.33 28866.98 F4ISH 10 0.07817 0.33 0.02595 0.36 51287.67 F4ISH 11 0.01436 0.73 0.0O029 0.74 75141.77 F4ISH 12 0.06859 0.70 0.0O064 0.70 100518.18 F4ISH 13 0.19924 0.62 0.0O066 0.69 133704.04 F4ISH 14 0.20989 0.65 0.02529 0.63 147717.15 Table 2F - F3 SH ID Chagas vs. healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F3WSH 1 0.00556 0.25 0.56589 0.54 10110.75 F3WSH 2 0.34100 0.61 0.04498 0.62 11455.20 F3WSH 3 0.01268 0.79 0.00626 0.68 12502.93 F3WSH 4 0.06060 EYE 0.20526 0.42 13594.05 F3WSH 5 0.69500 0.54 0.69517 0.48 14059.44 F3WSH 6 0.65409 0.51 0.53547 0.46 14174.75 F3WSH 7 0.38530 0.63 0.60346 0.47 17402.10 F3 SH 8 0.00510 0.79 0.00000 0.80 24893.62 F3WSH 9 0.37016 0.42 0.07118 0.39 27950.80 F3 SH 10 0.59465 0.46 0.07262 0.40 28092.19 F3 SH 11 0.43295 0.39 0.03528 0.38 28269.84 F3WSH 12 0.30011 0.37 0.00486 0.32. 29245.29 F3 SH 13 0.15321 0.35 0.97819 0.50 37345.84 F3 SH 14 0.28723 0.38 0.00172 0.31 51352.12 Table 2G - F3WSL p > Chagas vs. healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F3WSL 1 0.01298 0.76 0.001887 0.69 2694.25 F3WSL 2 0.28700 0.63 0.000190 0.72 2790.34 F3WSL 3 0.68216 0.50 0.003097 0.68 2993.03 F3WSL 4 0.31243 0.58 0.000018 0.76 3013.11 F3 SL 5 0.32569 0.63 0.001274 0.69 3033.30 F3WSL 6 0.35329 0.60 0.010395 0.67 3148.90 F3WSL 7 0.89142 0.47 0.251317 0.56 3388.92 F3WSL 8 0.74321 0.53 0.006515 0.69 3412.41 F3 SL 9 0.86988 0.47 0.005399 0.68 3499.33 F3 SL 10 0.11332 0.65 0.000112 0.73 3655.82 F3WSL 11 0.64256 0.58 0.411129 0.55 3744.27 F3 SL 12 0.00292 0.81 0.002459 0.68 3932.19 F3WSL 13 0.29953 0.63 0.000165 0.73 3982.22 F3 SL 14 0.00223 0.19 0.003097 0.33 4077.76 F3WSL 15 0.68216 0.47 0.795389 0.49 4149.05 F3 SL 16 0.76394 0.47 0.005699 0.67 4220.44 F3WSL 17 0.39737 0.58 0.000003 0.79 4242.16 F3 SL 18 0.46105 0.55 0.000125 0.71 4424.62 F3 SL 19 0.16382 0.65 0.000165 0.72 4450.87 F3 SL 20 0.00087 0.86 0.000006 0.78 5380.70 F3 SL 21 0.62313 0.42 0.000579 EYE 5643.49 F3WSL 22 0.70230 0.53 0.000850 0.29 5901.48 F3WSL 23 0.19005 0.65 0.000002 0.80 5988.65 F3WSL 24 0.19005 0.65 0.000016 0.76 6008.67 F3WSL 25 0.42852 0.63 0.000025 0.77 6146.33 F3WSL 26 0.15571 0.65 0.000010 0.79 6192.87 F3 SL 27 0.38232 0.63 0.000905 EYE 6391.74 F3WSL 28 0.56643 0.45 0.863898 0.52 6450.92 F3WSL 29 0.32569 0.37 0.000006 0.21 6499.23 F3 SL 30 0.01511 0.24 0.000144 0.26 6519.18 F3WSL 31 0.14791 0.65 0.000002 0J7 6877.34 F3 SL 32 0.01511 0J6 0.000120 0J4 7080.70 F3WSL 33 0.07597 0.68 0.000525 EYE 7559.15 F3WSL 34 0.13321 0.32 0.277646 0.40 8126.14 F3WSL 35 0.18098 0.37 0.594848 0.43 8141.63 F3 SL 36 0.05960 0.68 0.000144 0.73 8859.26 F3WSL 37 0.13321 0.37 0.034506 0.40 8934.40 F3WSL 38 0.38232 0.60 0.001625 EYE 9185.80 F3 SL 39 0.01202 0.76 0.000008 0.78 24827.43 F3WSL 40 0.29953 0.40 0.006691 0.35 33349.46 F3WSL 41 0.08544 0.68 0.068149 0.60 53760.45 F3 SL 42 0.07597 0.35 0.003469 0.34 66517.71 F3WSL 43 0.38232 0.55 0.005699 0.68 72985.70 Table 2H- F1WSL ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F1WSL 1 0.54604 0.43 0.62577 0.48 2518.63 F1 SL 2 0.93127 0.50 0.00598 0.68 2989.29 F1 SL 3 0.07469 0.69 • 0.01187 0.68 3008.29 F1WSL 4 0.52709 0.59 0.03154 0.65 3172.94 F1 SL 5 0.12760 0.37 0.00559 0.32 3410.48 F1 SL 6 0.54604 0.57 0.00662 0.69 3829.34 F1 SL 7 0.43763 0.57 0.02053 0.36 3850.31 F1 SL 8 0.11385 0.67 0.00088 0.72 3873.66 F1 SL 9 0.05778 0.31 0.00287 0.31 3897.07 F1 SL 10 0.15060 0.33 0.05109 0.38 4071.59 F1WSL 11 0.02314 0J4 0.00297 0.69 4185.29 F1WSL 12 0.00751 0.76 0.00126 0J3 4396.10 F1 SL 13 0.32836 0.38 0.28232 0.44 4483.28 F1WSL 14 0.31433 0.59 0.00662 0.69 4807.13 F1WSL 15 0.00529 0.20 0.00191 0.32 5021.55 F1 SL 16 0.10742 0.67 0.00022 0.75 5378.75 F1WSL 17 0.05778 0.31 0.39958 0.55 5433.99 F1WSL 18 0.01703 0.79 0.00439 0.69 5632.38 F1 SL 19 0.45479 0.57 0.00037 0.73 6142.29 F1 SL 20 0.23853 0.62 0.00014 0J6 6190.17 F1 SL 21 0.19578 0.36 0.22700 0.42 6449.89 F1 SL 22 0.05778 0.30 0.00598 0.31 6632.47 F1 SL 23 0.04730 0.31 0.01387 0.34 6806.45 F1 SL 24 0.01574 0J2 0.64952 0.49 7184.12 F1WSL 25 0.02146 0.79 0.48500 0.45 7481.21 F1WSL 26 0.00017 0.86 0.00032 0.73 7554.75 F1WSL 27 0.05060 EYE 0.00037 0J2 7735.75 F1WSL 28 0.54604 0.46 0.14962 0.40 8128.46 F1WSL 29 0.45479 0.40 0.19854 0.42 8142.18 F1WSL 30 0.38845 0.42 0.18350 0.42 8335.48 F1WSL 31 0.40445 0.40 0.11810 0.40 8351.32 F1 SL 32 0.01988 0.25 0.00540 0.32 8932.95 F1 SL 33 0.00967 0.76 0.00019 0J6 10409.97 F1WSL 34 0.00751 0J6 0.00023 0J4 10539.14 F1WSL 35 0.97707 0.52 0.00807 0.68 11232.39 F1 SL 36 0.27465 0.64 0.01O13 0.64 12703.49 F1 SL 37 0.52709 0.43 0.00108 EYE 28719.77 F1WSL 38 0.26222 0.38 0.00834 0.68 36932.80 Table 21 - F1WSH ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F1WSH 1 0.22056 0.36 0.22O56 0.36 10006.31 F1 SH 2 0.15041 0.35 0.15041 0.35 10069.27 F1WSH 3 0.15811 0.35 0.15811 0.35 10073.29 F1WSH 4 0.15041 0.35 0.15041 0.35 10075.50 F1WSH 5 0.12902 0.33 0.12902 0.33 10077.45 F1WSH 6 0.12244 0.33 0.12244 0.33 10078.60 F1WSH 7 0.12244 0.33 0.12244 0.33 10079.39 F1WSH 8 0.12902 0.33 0.12902 0.33 10080.13 F1 SH 9 0.15041 0.35 0.15041 0.35 10082.30 F1WSH 10 0.05189 0.29 0.05189 0.29 10100.16 F1WSH 11 0.83129 0.46 0.83129 0.46 12723.86 F1 SH 12 0.19192 0.39 0.19192 0.39 12929.77 F1WSH 13 0.00838 0.26 0.00838 0.26 13589.55 F1WSH 14 0.25215 0.36 0.25215 0.36 13952.94 F1 SH 15 0.31155 0.61 0.31155 0.61 15661.83 F1 SH 16 0.20114 0.39 0.20114 0.39 16271.62 F1 SH 17 0.83129 0.46 0.83129 0.46 16516.70 F1WSH 18 0.63168 0.51 0.63168 0.51 16788.70 F1WSH 19 0.89407 0.51 0.89407 0.51 18621.48 F1 SH 20 0.91517 0.46 0.91517 0.46 28729.81 F1WSH 21 0.93632 0.54 0.93632 0.54 28889.99 F1WSH 22 0.61286 0.49 0.61286 0.49 31730.11 F1WSH 23 0.43993 0.41 0.43993 0.41 53928.97 F1WSH 24 0.02529 0.26 0.02529 0.26 61867.95 F1 SH 25 0.04876 0.31 0.04876 0.31 62260.46 F1 SH 26 0.06230 0.31 0.06230 0.31 62368.09 F1 SH 27 0.24129 0.40 0.24129 0.40 62937.29 Table 2J- F2 S F2 SL 2 0.95646 0.47 0.12505 0.40 4071.70 F2 SL 3 0.00319 0J8 0.00783 0.66 4394.77 F2WSL 4 0.07157 0J1 0.03762 0.61 4575.09 F2 SL 5 0.93472 0.50 0.57072 0.54 4810.17 F2 SL 6 0.10723 0.35 0.50128 0.55 5452.99 F2WSL 7 0.36762 0.42 0.00471 0.33 34200.33 Table 2K- F2WSH ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F2WSH 1 0.93472 0.53 0.80257 0.48 17119.69 F2 SH 2 0.53006 0.58 0.05052 0.62 28721.90 F2WSH 3 0.70230 0.47 0.02226 0.36 31716.47 F2WSH 4 0.64256 0.45 0.07268 0.3J 32505.96 F2 SH 5 0.84844 0.53 0.14553 0.43 33800.52 Table 2L - F5WS ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F5 SLJL 0.15321 0.66 0.01045 0.65 2515.32 F5 SL_2 0.02694 0.72 0.00344 0.70 2717.45 F5 SL_3 0.02694 0.75 0.00068 0.71 2878.07 F5WSL 4 0.14531 0.64 0.01253 0.66 3148.08 F5 SL_5 0.69500 0.46 0.01574 0.63 3177.70 F5 SL_6 0.75804 0.56 0.00965 0.33 4062.36 F5 SL_7 0.37016 0.41 0.43 0.54706 4133.16 F5 SL_8 0.55645 0.42 0.01319 0.34 4745.72 F5WSL_9 0.10431 0.68 0.00234 0.68 5277.01 F5WSL_10 0.31336 0.61 0.02063 0.63 5469.51 F5WSLJL1 0.69500 0.56 0.00162 0.70 5989.66 F5 SL_12 0.20758 0.65 0.00248 0.68 6008.46 F5WSL L3 0.73682 0.51 0.00195 0.69 6192.91 F5WSL_14 0.11681 0.35 0.54149 0.47 6231.01 F5 SL_15 0.13773 0.35 0.31176 0.42 6334.92 F5WSL_16 0.40081 0.42 0.32952 0.55 6451.57 F5WSL_17 0.00016 0.12 0.00003 0.23 6836.65 F5 SL_18 0.44956 0.45 0.47720 0.54 8128.52 F5WSL .19 0.95533 0.51 0.13035 0.58 8579.30 F5WSL_20 0.02165 0.29 0.54149 0.45 8947.28 F5 SL_21 0.40081 0.40 0.24713 0.58 9291.62 F5WSL_22 0.00155 0.84 0.00091 0.71 15267.51 F5WSL_23 0.03105 0.75 0.06433 0.62 48884.47 Table 2M - F5WSH ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F5WSH 1 0.10129 0.69 0.000006 0.8O 10036.09 F5WSH 2 0.10129 0.64. 0.000048 0.75 10112.27 F5WSH 3 0.86305 0.55 0.000097 0.74 10207.38 F5WSH 4 0.07949 0.69 0.000638 0.73 10435.82 F5WSH 5 0.25018 0.59 0.000406 0.71 10775.19 F5WSH 6 0.01988 0J4 0.000009 0.77 10896.88 F5WSH 7 0.03339 0J2 0.000000 0.88 10973.45 F5WSH 8 0.00369 0.79 0.000005 0.8O 11106.17 F5WSH 9 0.00105 0.84 0.589417 0.5O 11865.96 F5WSH 10 0.37283 0.41 0.003806 0.69 12112.62 F5WSH 11 0.93127 0.50 0.000617 0.71 13397.15 F5WSH 12 0.02494 0.69 0.723651 0.51 13539.82 F5 SH 13 0.01574 0J6 0.830538 0.5O 14038.99 F5WSH 14 0.14261 0.67 0.874313 0.48 14063.23 F5WSH 15 0.19578 0.64 0.000077 0.76 15260.86 F5WSH 16 0.00062 0.81 0.002652 0.69 15395.25 F5WSH 17 0.00142 0.81 0.017657 0.62 15592.45 F5WSH 18 0.02686 0.69 0.045442 0.62 17743.31 F5WSH 19 0.21640 0.36 0.003279 0.68 17901.53 F5 SH 20 0.25018 0.36 0.001661 0.68 18093.56 F5WSH 21 0.45479 0.41 0.032346 0.65 18761.64 F5WSH 22 0.03108 0.29 0.397140 0.55 21983.97 F5WSH 23 0.70861 0.53 0.003920 0.31 23152.28 F5WSH 24 0.02890 0.30 0.000576 0.72 24913.62 F5WSH 25 0.75183 0.52 0.004158 0.31 29155.34 F5WSH 26 0.03339 0.29 0.260214 0.41 33508.17 F5 SH 27 0.54604 0.46 0.007574 0.33 51295.58 F5WSH 28 0.79584 0.48 0.000329 0.28 56742.73 F5WSH 29 0.28748 0.42 0.004409 0.32 59336.98 F5WSH 30 0.28748 0.39 0.004409 0.32 59669.53 F5WSH 31 0.19578 0.34 0.005098 0.33 60588.18 F5WSH 32 0.13494 0.34 0.002071 0.69 75823.33 F5WSH 33 0.50848 0.57 0.002136 0.33 95220.61 F5WSH 34 0.04418 0.29 Table 2N - F4WSL p > Chagas vs. healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F4WSL 1 0.00317 0.80 0.000127 0.73 3010.33 F4WSL 2 0.73348 0.53 0.001180 0.69 3178.31 F4WSL 3 0.05728 0J2 0.606858 0.49 3382.81 F4WSL 4 0.30701 0.64 0.046159 0.63 3969.24 F4WSL 5 0.33467 0.42 0.639341 0.46 5019.46 F4WSL 6 0.09410 0.28 0.963355 0.50 6458.30 F4 SL 7 0.01156 0.78 0.002083 0.71 7564.24 F4WSL 8 0.22241 0.67 0.001386 EYE 7737.45 F4WSL 9 0.18232 0.38 0.832623 0.49 8132.25 F4 SL 10 0.64983 0.44 0.538133 0.45 8150.86 F4 SL 11 0.25637 0.33 0.144018 0.42 8943.87 F4 SL 12 0.21184 0.36 0.706371 0.50 9305.26 F4 SL 13 Table 20 - F4WSH ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F4 SH 1 0.17971 0.39 0J22112 0.47 10111.80 F4 SH 2 0.14704 0.62 0.617205 0.47 13601.95 F4WSH 3 0.37839 0.63 0.152097 0.58 24762.11 F4 SH 4 0.03813 0.28 0.000041 0.24 95163.38 Table 2P - F6WSL ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F6WSL 1 0.63168 0.46 0.427855 0.44 3110.28 F6WSL 2 0.81058 0.51 0.717381 0.49 3321.25 F6 SL 3 0.59429 0.44 0.208303 0.42 3330.05 F6 SL 4 0.68955 0.41 0.623261 0.54 6631.71 F6WSL 5 0.29898 0.64 0.972500 0.51 6844.57 F6WSL 6 0.29898 0.41 0.388791 0.45 8938.10 F6WSL 7 0.57599 0.46 0.258908 0.42 48509.95 F6WSL 8 0.03313 0.29 0.000935 0.27 48614.08 F6WSL 9 0.00201 0.18 0.037016 0.36 65062.92 F6WSL 10 0.32445 0.39 0.023417 0.37 73971.36 Table 2Q - F6WSH Chagas vs healthy Chagas vs No - Chagas M / Z average Main value p ROC value p RQC (kDa) F6WSHJL 0.63168 0.46 0.427855 0.44 3110.28 F6WSH .2 0.81058 0.51 0.717381 0.49 3321.25 F6WSH_3 0.59429 0.44 0.208303 0.42 3330.05 F6WSH 4 0.68955 0.41 0.623261 0.54 6631.71 F6WSH 5 0.29898 0.64 0.972500 0.51 6844.57 F6WSH 6 0.29898 0.41 0.388791 0.45 8938.10 F6WSH 7 0.57599 0.46 0.258908 0.42 48509.95 F6 SH 8 0.03313 0.29 0.000935 0.27 48614.08 F6 SH 9 0.00201 0.18 0.037016 0.36 65062.92 F6WSLH 10 0.32445 0.39 0.023417 0.37 73971.36 Table 2R - F1ISL ID Chagas vs healthy Chagas vs No -Chagas M Z average Main value p ROC value p ROC (kDa) F1ISL 1 0.47233 0.59 0.00008 0.76 3183.51 F1ISL 2 0.52709 0.57 0.00000 0.81 3200.64 F1ISL 3 0.38845 0.41 0.85509 0.52 3292.18 F1ISL 4 0.01454 0.76 0.00023 0.71 3788.89 F1ISL 5 0.66631 0.55 0.00197 0.69 3877.48 F1ISL 6 0.02686 0.26 0.00191 0.32 3903.41 F1ISL 7 0.00095 0.15 0.00472 0.32 4073.87 F1ISL 8 0.01703 0.27 0.00239 0.31 4105.74 F1ISL 9 0.02146 0.25 0.00088 0.28 4175.72 F1ISL 10 0.23853 0.38 0.00004 0.22 4230.14 F1ISL 11 0.81810 0.46 0.00217 0.32 4238.67 F1ISL 12 0.19578 0.38 0.00028 0.26 4260.76 F1ISL 13 0.34278 0.40 0.00458 0.32 4275.27 F1ISL 14 0.02146 0.29 0.00894 0.34 4292.31 F1ISL 15 0.31433 0.40 0.00501 0.33 4351.52 F1ISL 16 0.08986 0.31 0.00016 0.26 4481.20 F1ISL 17 0.03108 0.29 0.00307 0.67 4661.41 F1ISL 18 0.04730 0.69 0.00869 0.68 4797.37 F1ISL 19 0.45479 0.57 0.01028 0.65 4811.15 F1ISL 20 0.58492 0.57 0.00246 0.68 5154.34 F1ISL 21 0.02314 0.69 0.00069 0.71 5288.40 F1ISL 22 0.00689 0.76 0.00000 0.82 5380.40 F1ISL 23 0.01841 0.74 0.00074 0.71 5591.40 F1ISL 24 0.42085 0.58 0.00091 0.72 5620.59 F1ISL 25 0.03339 0.72 0.00239 0.67 5635.51 F1ISL 26 0.75183 0.57 0.03446 0.35 5764.27 F1ISL 27 0.25018 0.62 0.00058 0.70 6007.31 F1ISL 28 0.01841 0.74 0.00016 0.74 6067.44 F1ISL 29 0.15894 0.67 0.00001 0.78 6145.12 • F1ISL 30 0.30070 0.62 0.00000 0.82 6191.34 F1ISL 31 0.00443 0.79 0.00000 0.81 6217.49 F1ISL 32 0.00307 0.81 0.00006 0.75 6256.47 F1ISL 33 0.00173 0.81 0.00271 0.67 6292.96 F1ISL 34 0.00190 0.81 0.00000 0.80 6348.54 F1ISL 35 0.00190 0.81 0.00001 0.76 6377.81 F1ISL 36 0.02890 0.74 0.00001 0.78 6398.74 F1ISL 37 0.18603 0.64 0.00020 0J5 6532.08 F1ISL 38 0.31433 0.38 0.01057 0.34 6808.21 F1ISL 39 0.54604 0.52 0.08889 0.39 and 190.03 ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F1ISL 40 0.00578 0.81 0.00564 0.69 7429.92 F1ISL 41 0.01454 0.76 0.03208 0.64 7487.05 F1ISL 42 0.01574 0.76 0.00000 0.80 7555.93 F1ISL 43 0J5183 0.53 0.00022 0J4 7738.82 F1ISL 44 0.00443 0.21 0.03365 0.36 8129.09 F1ISL 45 0.00529 0.23 0.04354 0.37 8144.24 F1ISL 46 0.00336 0.17 0.03529 0.41 8334.56 F1ISL 47 0.00631 0.20 0.06638 0.39 8349.58 F1ISL 48 0.02146 0.22 0.00776 0.33 8440.26 F1ISL 49 0.01703 0.26 0.00945 0.34 8449.38 F1ISL 50 0.01988 0.29 0.02450 0.32 8457.02 F1ISL 51 0.28748 0.42 0.01147 0.32 8642.68 F1ISL 52 0.64554 0.47 0.00108 0.28 8675.93 F1ISL 53 0.05060 0.29 0.00074 0.30 8741.03 F1ISL 54 0.00889 0.24 0.00348 0.32 8933.11 F1ISL 55 0.00307 0.19 0.00458 0.30 8949.87 F1ISL 56 0.02494 0.27 0.86264 0.51 9154.81 F1ISL 57 0.03585 0.27 0.00869 0.69 9254.28 F1ISL 58 0.05060 0.29 0.00239 0.70 9302.80 F1ISL 59 0.07949 0.29 0.00137 0.71 9372.34 F1ISL 60 0.06579 0.34 0.00162 0.72 9512.39 F1ISL 61 0.05060 0.69 0.00203 0.69 10425.58 F1ISL 62 0.00967 0.79 0.00108 0.72 12728.23 F1ISL 63 0.16762 0.36 0.00337 0.68 28797.66 F1ISL 64 0.13494 0.64 0.06089 0.62 36203.31 F1ISL 65 0.05778 0.32 0.00116 0.30 67405.39 Table 2S - F1ISH pay attention to this ID Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F1ISH 1 0.02708 0J1 0.00163 0.68 10414.53 F1ISH 2 1.00000 0.49 0.00509 0.37 11743.97 F1ISH 3 0.00715 0.76 0.00003 0.78 12714.74 F1ISH 4 0.40905. 0.39 0.50029 0.47 13588.84 F1ISH 5 0.07438 0.33 0.06602 0.40 16312.03 F1ISH 6 0.08345 0.29 0.00495 0.66 28774.99 F1ISH 7 0.00201 0.20 0.01810 0.35 69109.75 Table 2T - F2ISL ID Chagas vs healthy Chagas vs No - Chagas M / Z average Main value p ROC value p ROC (kDa) F2ISL 1 0.21069 0.36 0.01438 0.35 3509.87 F2ISL 2 0.06230 0.71 0.75637 0.53 4078.84 F2ISL 3 0.00127 0.81 0.00005 0J5 4397.12 F2ISL 4 0.15041 0.66 0.00434 0.67 4429.43 F2ISL 5 0.03539 0.66 0.00368 0.69 4510.82 F2ISL 6 0.08345 0.66 0.00015 0J3 4582.77 F2ISL 7 0.97875 0.54 0.00106 EYE 6147.07 F2ISL 8 0.12244 0.31 0.03473 0.38 8155.79 F2ISL 9 0.42433 0.41 0.49054 0.47 8356.10 F2ISL 10 0.17440 0.39 0.04650 0.37 43543.21 F2ISL 11 0.59429 0.44 0.01101 0.33 49105.79 Table 2U - F2ISH ID Chagas vs healthy Chagas vs No - Chagas M / Z average Main] value p ROC value p ROC (kDa) F2ISH 1 0.12902 0.38 0.14526 0.40 10982.95 F2ISH 2 0.39410 0.40 0.11676 0.39 11832.63 F2ISH 3 0.50555 0.41 0.76951 0.52 37709.02 F2ISH 4 0.15811 0.34 0.86315 0.48 54031.83 F2ISH 5 0.15041 0.36 0.02504 0.37 88226.08 F2ISH 6 0.15811 0.34 0.95190 0.49 89062.98 F2ISH 7 0.63168 0.41 0.51248 0.55 89941.18 F2ISH 8 0.61286 0.44 0.31745 0.44 91183.36 Table 2V- F3ISL ro Chagas vs healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F3ISL 1 0.94291 0.53 0.39164 0.56 4160.88.

F3ISL 2 0.54272 0.43 0.64776 0.53 4819.12 F3ISL 3 1.00000 0.52 0.11236 0.62 5992.34 F3ISL 4 0.26701 EYE 0.00752 0.68 6149.50 F3ISL 5 0.09240 0.27 0.82819 0.48 8145.73 F3ISL 6 0.18523 0.33 0.68093 0.48 8963.86 F3ISL 7 0.13262 0.30 0.00921 0.32 28930.20 F3ISL 8 0.00650 0.21 0.00247 0.28 30705.35 Table 2W - F3ISH ID Chagas vs healthy Chagas vs No - Chagas M7Z average Main value p ROC value p ROC (kDa) F3ISH 1 0.63594 0.56 0.51746 0.46 10257.40 F3ISH 2 0.89241 0.50 0.99229 0.50 10444.29 F3ISH 3 0.54277 0.44 0.00234 0.33 11642.51 F3ISH 4 0.17621 0.66 0.00086 0J2 24919.40 F3ISH 5 0.01121 0.22 0.00063 0.30 29079.28 F3ISH 6 0.00683 0.22 0.00015 0.26 30617.80 F3ISH 7 0.01636 0.22 0.00022 0.28 37518.65 Table 2X: - F4ISL p > Chagas vs. healthy Chagas vs No -Chagas M / Z average Main value p ROC value p ROC (kDa) F4ISL 1 0.14261 0.64 0.00233 0.71 3174.20 F4ISL 2 0.11385 0.65 0.00557 0.71 3191.33 F4ISL 3 0.64554 0.57 0.00217 0.69 3782.82 F4ISL 4 0.19578 0.62 0.02666 0.66 3824.52 F4ISL 5 0.06579 0.69 0.00005 0.75 5380.50 F4ISL 6 0.04730 0.69 0.00021 0.75 6008.33 F4ISL 7 0.95415 0.52 0.00415 0.67 6192.58 F4ISL 8 0.01342 0.76 0.00065 0.72 7562.65 F4ISL 9 0.28748 0.34 0.61863 0.48 8144.89 F4ISL 10 0.08455 0.31 0.00106 0.29 8945.38 F4ISL 11 0.03585 0.29 0.01359 0.34 30027.72 F4ISL 12 0.04124 0.31 0.02666 0.36 51843.34 Table 4 - Preferred biomarkers: Chagas vs Chagas-free Main Chagas vs healthy M / Z average p-value ROC (kDa) F5ISL 6 0.00005 0J6 3170.99 F5ISL 13 0.00047 0.71 75548.95 F5ISH 2 0.00004 0.26 28036.18 F5ISH 3 0.00006 0.24 28269.46 F5ISH 7 0.00030 0.29 59454.95 F5ISH 9 0.00060 0.71 75647.95 F5ISH 10 0.00012 0.75 103795.90 F6ISH 8 0.00044 0.27 55962.00 F6ISH 9 0.00037 0.27 56167.48 F6ISH 10 0.00119 0.27 56414.40 F6ISH 11 0.00015 0.26 57022.49 F6ISH 12 0.00014 0.28 57908.91 F6ISH 15 0.00003 0.77 75426.74 F6ISL 9 0.00078 0.29 44015.50 F6ISL 11 0.00047 0.28 56807.45 F4ISH 5 0.00045 0.26 14162.34 F4ISH 6 0.00006 0.25 14203.94 F4ISH 7 0.00018 0.26 14252.34 F4ISH 11 0.00029 0.74 75141.77 F4ISH 12 0.00064 EYE 100518.18 F3 SH 8 0.00000 0.80 24893.62 - F3 SL 4 0.000018 0.76 3013.11 F3 SL 17 0.000003 0.79 4242.16 F3WSL 20 0.000006 0J8 5380.70 F3WSL 23 0.000002 0.80 5988.65 F3WSL 24 0.000016 0.76 6008.67 F3WSL 25 0.000025 0.77 6146.33 F3WSL 26 0.000010 0.79 6192.87 F3WSL 29 0.000006 0.21 6499.23 F3 SL 31 0.000002 0.77 6877.34 F3 SL 39 0.000008 0.78 24827.43 F1WSL 8 0.00088 0.72 3873.66 F1 SL 12 0.00126 0.73 4396.10 F1WSL 16 0.00022 0.75 5378.75 F1WSL 19 0.00037 0.73 6142.29 F1 SL 20 0.00014 0.76 6190.17 F1 SL 26 0.00032 0.73 7554.75 F1WSL 27 0.00037 0.72 7735.75 F1 SL 33 0.00019 0J6 10409.97 F4ISL_8 0.00065 0.72 7562.65 F4ISL 10 0.00106 0.29 8945.38 C. USE OF BIOMARKERS FOR DIFFERENCES BETWEEN STAGES DIFFERENT FROM CHAGAS DISEASE This example demonstrates the utility of the methods of the present invention for identifying biomarkers that indicate when the individual with Chagas disease is acutely infected against chronically infected. The samples were analyzed from Venezuelan patients chronically infected with the disease and compared with the samples from acutely infected Guatemalan pediatric patients (as measured by an RKG test). The results are summarized in Table 5 below and in Figures 5 and 6.

MW (kDa) Protein Figure Significance 6.454 Apo-1 6A-C? = 0.7 8.127 Apo-1 7A-C p = 0.001 8.127 7D p = 0.2 8.351 8A-C p = 0.08 8.937 9A-C p = 0.002 9.308 Apo-1 10A-C p = 0.218 (C-term) D. USE OF BIOMARKERS TO DIFFERENTIATE AMONG DIFFERENT PARASITIC DISEASES This example demonstrates the utility of the present methods for identifying biomarkers that indicate the status in an individual of the presence or absence of Chagas disease that differs from a different trypanosome infection or other parasitic infection. Here, biomarkers were identified by indicating the presence or absence of Chagas disease when distinguished from a different trypanosome infection, such as African trypanosomiasis (sleeping sickness), a protozoan infection, such as babesiosis and a parasitic infection, such like malaria. Biomarkers that specifically indicate the presence or absence of Chagas disease were also compared with uninfected individuals. Several specific biomarkers for Chagas disease were identified. For example, a biomarker of 8.351 kDa and a biomarker of 9.3 kDa. The presence, or a comparatively greater presence, of one or more of these biomarkers in a sample from an individual is indicative of the specific presence of T. cruzei infection and the specific presence of Chagas disease. The results are shown in Figures 7-9. It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in clarity of this will be suggested by those skilled in the art and that are included within the spirit and scope of this application and extension of the claims. annexes. All publications, patents and patent applications cited herein were incorporated by reference in their entirety for all purposes.

Claims (34)

1. CLAIMS 1. A method to qualify the status of Chagas disease in a subject, characterized because it comprises: a. measuring at least one biomarker in a biological sample of the subject, wherein at least one biomarker is selected from the group comprising the biomarkers of Table 1 and Tables 2A-2X; and b. correlate the measurement with Chagas disease data.
2. 2. The method according to claim 1, characterized in that at least one biomarker is selected from the group comprising the biomarkers of Table 3 and Table 4.
3. 3. The method according to the claim 1, characterized in that at least one biomarker is selected from the group comprising: MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO.
4. 4. The method according to claim 1, characterized in that it comprises measuring each of MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO.
5. 5. The method according to claim 1, characterized in that it further comprises measuring an additional biomarker listed in Table 1 and Tables 2A-2X.
6. 6. The method according to any of claims 1, 2, 3 or 4, characterized in that it comprises measuring one or more biomarkers selected from the group of biomarkers with molecular weights of 4.4, 4.8, 7.8, 8.9, 9.3, 13.6, 16.3, 28.7 and 54.04 kDa. The method according to any one of claims 1, 2, 3 or 4, characterized in that at least one biomarker is measured by capturing the biomarker on an adsorbent surface of a SELDI probe and detecting biomarkers captured by desorption mass spectrometry -Laserization laser. 8. The method according to any of claims 1, 2, 3 or 4, characterized in that at least one biomarker is measured by immunological titration. 9. The method according to any of claims 1, 2, 3 or 4, characterized in that the sample is serum. The method according to any of claims 1, 2, 3 or 4, characterized in that the correlation is carried out by programming elements of classification algorithms. The method according to any of claims 1, 2, 3 or 4, characterized in that the status of Chagas disease is selected from chronic symptomatic, chronic asymptomatic, acute and uninfected. 12. The method according to any of claims 1, 2, 3 or 4, characterized in that Chagas disease is selected from Chagas disease against healthy. 13. The method according to claim 12, characterized in that at least one biomarker is selected from the biomarkers of Table 3. The method according to any of claims 1, 2, 3 or 4, characterized in that the status of Chagas disease is selected from Chagas disease against without Chagas disease. 15. The method according to claim 14, characterized in that at least one biomarker is selected from the biomarkers of Table 4. 16. The method according to claim 14, characterized in that at least one biomarker is selected from the biomarkers with molecular weights of 8.351 kDa, 9.3 kDa, 7.3 kDa, 6.04 kDa, 4.4 kDa, 4.07 kDa and 5.1 kDa, as illustrated in Figures 7-9. The method according to any of claims 1, 2, 3 or 4, characterized in that it further comprises (c) handling the treatment of a subject based on status. 18. The method according to claim 7, characterized in that the adsorbent is a cation exchange adsorbent. 19. The method according to claim 7, characterized in that the adsorbent is a chelated metal adsorbent. The method according to claim 17, characterized in that if the measurement is correlated with Chagas disease, the treatment management of the subject comprises administering one or more drugs selected from the group comprising nifurtimox, beznidazole or allopurinol. 21. The method according to claim 17, characterized in that it further comprises: (d) measuring in at least one biomarker after handling the subject. 22. A method comprising measuring at least one biomarker in a biological sample, characterized in that at least one biomarker is selected from the group comprising the biomarkers of Table 1 and Tables 2A-2X. 23. The method according to claim 22, characterized in that at least one biomarker is selected from the group comprising the biomarkers of Table 3 and Table 4. 24. The method according to claim 22, characterized in that at least one The biomarker is selected from the group comprising: MlP-la, Apo-IA, Fibronectin, anaphylatoxin C3 and MllO. 25. The method according to claim 22, characterized in that it also comprises measuring each of the following biomarkers: MlP-la, Apo-IA, Fibronectin, anaphylatoxin C3 and MllO. 26. The method according to claim 25, characterized in that it further comprises measuring one or more additional biomarkers listed in Table 1 and Tables 2A-2X. 27. The method according to any of claims 22 or 25, characterized by further comprising measuring one or more biomarkers selected from the group of molecular weight biomarkers 4.4, 4.8, 7.8, 8.9, 9.3, 13.6, 16.3, 28.7 and 54.04 kDa . The method according to any of claims 22, 23, 24 or 25, characterized in that the biomarker is measured by capturing the biomarker on an adsorbent surface of a SELDI probe and detecting the biomarkers captured by desorption / ionization mass spectrometry To be. 29. The method according to any of claims 22, 23, 24 or 25, characterized in that the sample is a serum sample. 30. The method according to claim 29, characterized in that the serum sample is obtained from blood. 31. The method of compliance with the claim 30, characterized in that it further comprises purifying the blood sample if the level of one or more biomarkers in the sample correlates with infection by Chagas disease. 32. The method of compliance with the claim 31, characterized in that purifying the blood sample includes treating the blood with one or more agents selected from the group comprising gentian violet, ascorbic acid and aminoquinoline R6026. 33. The method according to claim 28, characterized in that the adsorbent is a cation exchange adsorbent. 34. The method of compliance with the claim 28, characterized in that the adsorbent is a chelated metal adsorbent. 35. equipment characterized in that it comprises: (a) a solid support comprising at least one capture reagent arranged thereto, wherein the capture reagent binds a biomarker from a first group comprising the biomarkers of Table 1 and Tables 2A-2X. (b) instructions for using the solid support to detect a biomarker of Table 1 and tables 2A-2X. 36. The equipment according to claim 35, characterized in that it comprises instructions for using the solid support to detect a biomarker selected from the group comprising the biomarkers of Table 3 and Table 4. 37. The equipment according to the claim 35, characterized in that it comprises instructions for using the solid support to detect a biomarker selected from the group comprising: MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO. 38. The equipment according to claim 35, characterized in that it comprises instructions for using the solid support to detect each of the biomarkers - MlP-la, Apola, Fibronectin, anaphylatoxin C3 and MllO. 39. The equipment according to claim 38, characterized in that it also comprises instructions for using the solid support to detect one or more biomarkers selected from the group of biomarkers with molecular weights of 4.4, 4.8, 7.8, 8.9, 9.3, 13.6, 16.3, 28.7 and 54.04 kDa. 41. The equipment according to any of claims 35, 36, 37 or 38, characterized in that the solid support comprises a capture reagent which is a SELDI probe. 42. The equipment according to any of claims 35, 36, 37 or 38, characterized in that it additionally comprises (c) a container that contains at least one of the biomarkers of Table 1 and Tables 2A-2X. 43. The equipment according to claim 35, characterized in that the capture reagent is a cation exchange adsorbent. 44. The equipment according to any of the claims 35, 36, 37 or 38, characterized in that it additionally comprises (c) an anion exchange adsorbent chromatography. 45. Equipment characterized in that it comprises: (a) a solid support comprising at least one capture reagent ordered to it, where the capture reagents are linked to at least one biomarker selected from the group comprising the biomarkers of Table 1 and Tables 2A-2X. (b) a container that contains at least one of the biomarkers. 46. The equipment according to claim 45, characterized in that the container contains at least one biomarker selected from the group comprising the biomarkers of Table 3 and Table 4. 47. The equipment according to claim 45, characterized in that the container contains at least one biomarker selected from the group comprising: MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO. 48. The equipment in accordance with the claim 45, characterized in that the container contains each of the following biomarkers: MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO. 49. The equipment in accordance with the claim 48, characterized in that the container further comprises one or more additional biomarkers listed in Table 1 and Tables 2A-2X. 50. The equipment according to any of claims 45 or 48, characterized in that the container also comprises one or more biomarkers selected from the group of biomarkers with molecular weights 4.4, 4.8, 7.8, 8.9, 9.3, 13.6, 16.3, 28.7 and 54.04 kDa 51. The equipment according to any of claims 46, 47, 48 or 49, characterized in that the solid support comprises a capture reagent which is a SELDI probe. 52. The equipment according to any of claims 46, 47, 48 or 49, characterized in that it additionally comprises (c) anion exchange adsorbent chromatography. 53. The equipment according to claim 45, characterized in that the capture reagent is a cation exchange adsorbent. 54. A product of program elements, characterized in that it comprises: a. a code that accesses the data attributed to a sample, the data comprises measuring at least one biomarker in the sample, the biomarker is selected from the group comprising the biomarkers of Table 1 and Tables 2A-2X; and b. a code that executes a classification algorithm that classifies the Chagas disease status of the sample as a function of the measurement. 55. The product of program elements according to claim 54, characterized in that the classification algorithm that classifies the Chagas disease status of the sample as a function of the measurement of a biomarker selected from the group comprising the biomarkers of the Table 3 and Table 4. 56. The product of program elements according to claim 54, characterized in that the classification algorithm that classifies the Chagas disease status of the sample as a function of the measurement of a biomarker selected from the group. comprising: MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO. 57. The product of program elements according to claim 54, characterized in that the classification algorithm that classifies the Chagas disease status of the sample as a function of the measurement of a biomarker of each of the biomarkers: MlP- la, Apo la, Fibronectin, anaphylatoxin C3 and MllO. 58. The product of program elements according to claim 54, characterized in that the classification algorithm classifies the Chagas disease status of the sample in addition as a function of the measurement of an additional biomarker listed in Table 1 and Tables 2A- 2X. 59. The product of program elements according to claim 54, characterized in that the classification algorithm classifies the Chagas disease status of the sample in addition as a function of the measurement of one or more biomarkers with molecular weights 4.4, 4.8, 7.8 , 8.9, 9.3, 13.6, 16.3, 28.7 and 54.04 kDa. 60. The product of program elements according to claim 54, characterized in that the classification algorithm classifies the Chagas disease status of the sample further as a function of the measurement of one or more biomarkers selected from the group of biomarkers comprising the F1 biomarkers H_2, F4IH_4, F3 L_8 and F1IL_3 of Table 1. 61. A purified molecule selected from the biomarkers of Table 1 and Tables 2A-2X. 62. A method characterized in that it comprises detecting a biomarker of Table 1 or Tables 2A-2X by mass spectrometry or immunological titration. 63. A method comprising measuring at least three biomarkers in a biological sample, characterized in that at least three biomarkers are selected from the group comprising the biomarkers of Table 1 and Tables 2A-2X. 64. The method according to claim 63, characterized in that at least three biomarkers comprise biomarkers selected from the group comprising the biomarkers of Table 3 and Table 4. 65. The method according to claim 63, characterized in that at least three biomarkers comprise biomarkers of the group comprising: MlP-la, Apo la, Fibronectin, anaphylatoxin C3 and MllO. 66. The method according to claim 63, characterized in that at least three biomarkers comprise ApoA, Fibronectin and anaphylatoxin C3. 67. The method according to claim 63, characterized in that at least three biomarkers comprise biomarkers selected from the group comprising: F1WH_2, F4IH_4, F3WL_8 and F1IL_3 of Table 1. 68. A method to qualify the status of Chagas disease in a subject in comparison to the status of a different parasitic infection, characterized in that the method comprises: (a) measuring at least one biomarker in a biological sample of the subject, where at least one biomarker specifically indicates the presence of Chagas disease and does not indicate the presence of a different parasitic infection; Y (b) correlate the measurement with the status of Chagas disease compared to the status of a different parasitic infection. 69. The method according to claim 68, characterized in that it further comprises measuring one or more biomarkers selected from the group of biomarkers of Table 4. 70. The method according to the claim 68, characterized in that it further comprises measuring one or more biomarkers selected from the group of biomarkers with molecular weights of 8,351 kDa, 9.3 kDa, 7.3 kDa, 6.04 kDa, 4.4 kDa, 4.07 kDa and 5.1 kDa, as illustrated in Figures 7-9 . 71. The method according to claim 70, characterized in that the parasitic infection comprises a kinetoplastid infection. 72. The method according to claim 70, characterized in that the parasitic infection is selected from the group comprising Leishmaniasis, African trypanosomiasis (sleeping sickness), malaria and babesiosis. 73. A method for monitoring the progress of Chagas disease in a patient, characterized in that it comprises: (a) measuring at least one biomarker in a first biological sample of the patient, where at least one biomarker indicates the presence of Chagas disease; and (b) measuring at least one biomarker in a second biological sample from a subject, wherein the second biological sample is obtained from the subject after taking the first biological sample; and (c) correlating the measurements with the progression or regression of Chagas disease in the subject. 74. The method according to claim 73, characterized in that at least one biomarker is selected from the group consisting of biomarkers of Tables 1- 2A-2X, 3 and 4. 75. The method according to claim 73, characterized because at least one biomarker is selected from the group consisting of 8,127 kDa (Apo-1) and 8,397 kDa proteins.