Search Results (697)

Background: To assess the outcome of Keraring (Mediphacos, Brazil) implantation according to a topographic pattern-based nomogram in eyes with mild to moderate keratoconus. Materials and Methods: A topographic pattern-based nomogram was used to guide Keraring selection in 47 consecutive eyes with stage I-II keratoconus (Amsler-Krumeich staging), which underwent femtosecond laser-assisted implantation at a single center. Electronic data of LogMar uncorrected distance visual acuity (UDVA) and corrected distance visual acuity (CDVA) manifest refraction and tomographic analysis (Pentacam HR, Oculus, Germany) measured preoperatively and at the last postoperative examination were retrospectively analyzed. Results: Mean follow-up was 18.8 months. (range 3–35). Mean UDVA improved (p < 0.001) from 0.87 ± 0.27 to 0.35 ± 0.21. UDVA increased on average by 5.13 lines. Mean CDVA improved from 0.21 ± 0.10 to 0.09. ± 0.07, and the proportion of eyes with CDVA ≥ 20/25 increased from 29.8% to 85.1% after surgery. No eyes lost lines of CDVA. The Alpins correction index of astigmatism was 0.77 and the mean refractive cylinder decreased from 4.99 ± 1.89 to −2.31 ± 1.47 D (p < 0.001). Mean and maximal keratometry was reduced on average by −2.10 ± 1.42 D and −3.02 ± 3.68 D, respectively (p < 0.001). The RMS of corneal high-order aberrations dropped from 3.296 ± 1.180 µm to 2.192 ± 0.919 µm, and that of vertical coma from −2.656 ± 1.189 µm to −1.427 ± 1.024 µm (p < 0.001). All topometric indices improved after surgery. Conclusions: Planning Keraring implantation using the topographic pattern-based nomogram is very effective and safe in eyes with mild to moderate keratoconus. Using that nomogram of UDVA and CDVA are clinically significant. Full article

Introduction: Prostate cancer, notably prostate adenocarcinoma (PARD), has high incidence and mortality rates. Although typically resistant to immunotherapy, recent studies have found immune targets for prostate cancer. Stratifying patients by molecular subtypes may identify those who could benefit from immunotherapy. Methods: We used single-cell and bulk RNA sequencing data from GEO and TCGA databases. We characterized the tumor microenvironment at the single-cell level, analyzing cell interactions and identifying fibroblasts linked to mitophagy. Target genes were narrowed down at the bulk transcriptome level to construct a PARD prognosis prediction nomogram. Unsupervised consensus clustering classified PARD into subtypes, analyzing differences in clinical features, immune infiltration, and immunotherapy. Furthermore, the cellular functions of the genes of interest were verified in vitro. Results: We identified ten cell types and 160 mitophagy-related single-cell differentially expressed genes (MR-scDEGs). Strong interactions were observed between fibroblasts, endothelial cells, CD8⁺ T cells, and NK cells. Fibroblasts linked to mitophagy were divided into six subtypes. Intersection of DEGs from three bulk datasets with MR-scDEGs identified 26 key genes clustered into two subgroups. COX regression analysis identified seven prognostic key genes, enabling a prognostic nomogram model. High and low-risk groups showed significant differences in clinical features, immune infiltration, immunotherapy, and drug sensitivity. In prostate cancer cell lines, CAV1, PALLD, and ITGB8 are upregulated, while CLDN7 is downregulated. Knockdown of PALLD significantly inhibits the proliferation and colony-forming ability of PC3 and DU145 cells, suggesting the important roles of this gene in prostate cancer progression. Conclusions: This study analyzed mitophagy-related genes in PARD, predicting prognosis and aiding in subtype identification and immunotherapy response analysis. This approach offers new strategies for treating prostate cancer with specific molecular subtypes and helps develop potential biomarkers for personalized medicine strategies. Full article

Background and Objectives: Urinary tract infection (UTI) is a common comorbidity in diabetic patients, making up one of the causes of sepsis. This study aims to develop a nomogram to predict the risk probability of sepsis in diabetic patients with UTI (DPUTIs). Materials and Methods: This is a retrospective observational study. Clinical data for DPUTIs were extracted from the Medical Information Mart for Intensive Care IV database. Eligible DPUTIs were randomly divided into training and validation cohorts in a 7:3 ratio. Independent prognostic factors for sepsis risk were determined using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. A corresponding nomogram based on these factors was constructed to predict sepsis occurrence in DPUTIs. The discrimination of the nomogram was assessed by multiple indicators, including the area under the receiver operating characteristic curve (AUC), net reclassification improvement index (NRI), and integrated discrimination improvement (IDI). In addition, a calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: A total of 1990 DPUTIs were included. Nine independent prognostic factors were identified as predictive factors for sepsis risk in DPUTIs. The prognostic factors included urine red blood cell classification (urine RBC cat), urine white blood cell classification (urine WBC cat), blood glucose, age, temperature, white blood cells (WBCs), sequential organ failure assessment (SOFA) score, lymphocytes, and hematocrit. The AUC, NRI, and IDI of the nomogram indicated robust discrimination. The calibration curve and Hosmer–Lemeshow test showed good calibration of the nomogram. The DCA curve demonstrated a better clinical utility of the nomogram. Conclusions: The nomogram established in this study helps clinicians predict the probability of sepsis in DPUTIs, providing evidence for optimizing the management of related risk factors. Full article

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and distinguishing its subtypes holds significant value for diagnosis, treatment, and the prognosis. Methods: Unsupervised clustering analysis was conducted to classify HCC subtypes. Subtype signature genes were identified using LASSO, SVM, and logistic regression. Survival-related genes were identified using Cox regression, and their expression and function were validated via qPCR and gene interference. GO, KEGG, GSVA, and GSEA were used to determine enriched signaling pathways. ESTIMATE and CIBERSORT were used to calculate the stromal score, tumor purity, and immune cell infiltration. TIDE was employed to predict the patient response to immunotherapy. Finally, drug sensitivity was analyzed using the oncoPredict algorithm. Results: Two HCC subtypes with different gene expression profiles were identified, where subtype S1 exhibited a significantly shorter survival time. A subtype scoring formula and a nomogram were constructed, both of which showed an excellent predictive performance. COL11A1 and ACTL8 were identified as survival-related genes among the signature genes, and the downregulation of COL11A1 could suppress the invasion and migration of HepG2 cells. Subtype S1 was characterized by the upregulation of pathways related to collagen and the extracellular matrix, as well as downregulation associated with the xenobiotic metabolic process and fatty acid degradation. Subtype S1 showed higher stromal scores, immune scores, and ESTIMATE scores and infiltration of macrophages M0 and plasma cells, as well as lower tumor purity and infiltration of NK cells (resting/activated) and resting mast cells. Subtype S2 was more likely to benefit from immunotherapy. Subtype S1 appeared to be more sensitive to BMS-754807, JQ1, and Axitinib, while subtype S2 was more sensitive to SB505124, Pevonedistat, and Tamoxifen. Conclusions: HCC patients can be classified into two subtypes based on their gene expression profiles, which exhibit distinctions in terms of signaling pathways, the immune microenvironment, and drug sensitivity. Full article

Background/Objectives: The EndoPredict^® assay has been widely used in recent years to estimate the risk of distant recurrence and the absolute chemotherapy benefit for patients with estrogen (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. However, there are no well-defined criteria for selecting patients who may benefit from the test. The aim of this study was to develop a novel nomogram to estimate the probability of obtaining a high-risk EndoPredict^® result in clinical practice. Methods: The study cohort comprised 348 cases of T1-3/N0-1a/M0 ER-positive/HER2-negative breast carcinoma. A multivariate analysis was conducted using a training cohort (n = 270) based on clinicopathological features that demonstrated a statistically significant correlation with the EndoPredict^® result in a univariate analysis. The predictive model was subsequently represented as a nomogram to estimate the probability of obtaining a high-risk result in the EndoPredict^® assay. The predictive model was then validated using a separate validation cohort (n = 78). Results: The clinicopathological features incorporated into the nomogram included tumor size, tumor grade, sentinel lymph node status, pN stage, and Ki67. The internal validation of the model yielded an area under the curve (AUC) of 0.803 (95% CI = 0.751, 0.855) in the receiver operating characteristic (ROC) curve for the training cohort, with an optimal sensitivity and specificity at a threshold of 0.536. The external validation yielded an AUC of 0.789 (95% CI = 0.689, 0.890) in its ROC curve, with optimal sensitivity and specificity achieved at a threshold of 0.393. Conclusions: This study presents, for the first time, the development of a clinically accessible nomogram designed to estimate the probability of obtaining a high-risk result in the EndoPredict^® assay. The use of easily available clinicopathological features allows for the optimization of patient selection for the EndoPredict^® assay, ensuring that those who would most benefit from undergoing the test are identified. Full article

Background: The aim of this study was to develop nomograms predicting 5- and 7-year biochemical-recurrence (BCR)-free survival in high-risk prostate cancer (PCa) patients treated with carbon-ion radiotherapy (CIRT) and androgen deprivation therapy (ADT). Methods: We retrospectively evaluated 785 high-risk PCa patients treated with CIRT and ADT. Based on the least absolute shrinkage and selection operator model, two nomograms predicting 5- and 7-year BCR-free survival were developed and internally validated. The ability of each nomogram to predict BCR-free survival was determined by calculating the area under the survival curve (AUC). Results: The 5- and 7-year BCR-free survival rates were 92.1% and 89.3%, respectively. Age, prostate-specific antigen level, clinical T stage, and Gleason score were incorporated into the nomogram predicting 5-year BCR-free survival. In addition to these variables, the percentage of positive biopsy cores was also added to the nomogram predicting 7-year BCR-free survival. The AUC value of each nomogram showed suboptimal-to-good discrimination. Conclusions: We developed the first nomograms accurately predicting BCR-free survival in high-risk PCa patients treated with CIRT and ADT. These nomograms will enable adequate understanding and explanation of BCR-free survival to patients when clinicians use them. Full article

Background: Acute myeloid leukemia (AML) is an aggressive cancer with variable treatment responses. While clinical factors such as age and genetic mutations contribute to prognosis, recent studies suggest that CT attenuation scores may also predict treatment outcomes. This study aims to develop a nomogram combining clinical and CT-based factors to predict treatment response and guide personalized therapy for AML patients. Methods: This retrospective study included 74 newly diagnosed AML patients who underwent unenhanced abdominal CT scans within one week before receiving their first induction chemotherapy. Clinical biomarkers of tumor burden were also collected. Patients were classified into two groups based on treatment response: complete remission (CR; n = 24) and non-complete remission (NCR; n = 50). Multivariable logistic regression was used to identify independent predictors of treatment response. Predictive performance was evaluated using receiver operating characteristic (ROC) curves, and model consistency was assessed through calibration and decision curve analysis (DCA). Results: Significant differences in hemoglobin (Hb), platelets (Plt), and CT attenuation scores were observed between the CR and NCR groups (all p < 0.05). Multivariable logistic regression identified Hb, Plt, and CT attenuation scores as independent predictors of treatment response. A nomogram incorporating these factors demonstrated excellent predictive performance, with an area under the curve (AUC) of 0.912 (95% CI: 0.842–0.983), accuracy of 0.865 (95% CI: 0.765–0.933), sensitivity of 0.880 (95% CI: 0.790–0.970), and specificity of 0.833 (95% CI: 0.684–0.982). The CR nomogram displayed significant clinical value and excellent goodness of fit. Conclusions: The nomogram, which incorporates Hb, Plt, and CT attenuation scores, provides valuable insights into predicting treatment response in AML patients. This model offers strong discriminatory ability and could enhance personalized treatment planning and prognosis prediction for AML. Full article

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating the optimal use of existing antibiotics. Pharmacokinetic/pharmacodynamic (PK/PD) principles provide a scientific framework for optimizing antimicrobial therapy, particularly to respond to evolving resistance patterns. This review examines PK/PD strategies for antimicrobial dosing optimization, focusing on three key aspects. First, we discuss the importance of drug concentration management for enhancing efficacy while preventing toxicity, considering various patient populations, including pediatric and elderly patients with their unique physiological characteristics. Second, we analyze different PK modeling approaches: the classic top-down approach exemplified by population PK analysis, the bottom-up approach represented by physiologically based PK modeling, and hybrid models combining both approaches for enhanced predictive performance. Third, we explore clinical applications, including nomogram-based dosing strategies, Bayesian estimation, and emerging artificial intelligence applications, for real-time dose optimization. Critical challenges in implementing PK/PD simulation are addressed, particularly the selection of appropriate PK models, the optimization of PK/PD indices, and considerations concerning antimicrobial concentrations at infection sites. Understanding these principles and challenges is crucial for optimizing antimicrobial therapy and combating AMR through improved dosing strategies. Full article

Background: The tumor microenvironment (TME) plays a crucial role in the progression of lung adenocarcinoma (LUAD). However, understanding its dynamic immune and stromal modulation remains a complex challenge. Methods: We utilized the ESTIMATE algorithm to evaluate the immune and stromal components of the LUAD TME from the TCGA database. Correlations between these components and clinical characteristics and patient prognosis were analyzed. Toll-like receptor 7 (TLR7) was identified as a key prognostic biomarker through PPI network and COX regression analysis. Validation of TLR7 expression was conducted using GEO data, qPCR, WB, and IHC. A prognostic model was developed using a nomogram, incorporating TLR7 expression. Enrichment analysis, the Tumor Immune Estimation Resource database, and single-sample gene set enrichment analysis were used to explore TLR7’s potential function. The response of the TLR7 subgroup to immunotherapy and drug sensitivity was observed. Results: We found significant associations between the immune and stromal components of LUAD TME and clinical features and prognosis. Specifically, TLR7 was identified as a prognostic biomarker, where lower expression in tumor tissues was linked to worse outcomes. This finding was further confirmed by comparing TLR7 expression in LUAD cells to normal bronchial epithelial cells, revealing lower expression in the tumor cells. Incorporating TLR7 into a nomogram prognostic model resulted in a good predictor of patient survival. Additionally, TLR7 was associated with immune function and positively correlated with various immune cells. Importantly, patients with high TLR7 expression were more likely to benefit from anti-PD-1 checkpoint blockade therapy. We also identified four treatment candidates for patients with high TLR7 expression. Conclusion: TLR7 is a powerful clinical feature that predicts patient prognosis, immunotherapeutic response, and drug candidates, providing additional insights for the treatment of LUAD. Full article

Background: Patients with multiple myeloma (MM) who have a suboptimal response to induction therapy or early relapse are classified as functional high-risk (FHR) patients and have been shown to have a dismal prognosis. The aim of this study was to establish a predictive nomogram for patients with non-transplanted FHR MM. Materials and Methods: The group comprised 215 patients in our center between 1 January 2006 and 1 March 2024. To identify independent risk factors, univariate and multivariate logistic regression analyses were performed, and a nomogram was constructed to predict non-transplant FHR MM. To evaluate the nomogram’s predictive accuracy, we utilized bias-corrected AUC, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: Multivariate logistic regression demonstrated that younger age at onset, a higher proportion of LDH (more than 220 U/L), pattern A + C of M protein decline patterns, a lower proportion of patients with induction treatment efficacy than VGPR, and those undergoing maintenance therapies were independent risk factors for patients with non-transplanted FHR MM. The AUC scores for the training and internal validation groups were 0.940 (95% CI 0.893–0.986) and 0.978 (95% CI 0.930–1.000). DCA and CIC curves were utilized to further verify the clinical efficacy of the nomogram. Conclusions: We developed a nomogram that enables early prediction of non-transplant FHR MM patients. Younger age at onset, LDH ≥ 220 U/L, an A + C pattern of M-protein decline, and induction therapy efficacy not reaching VGPR are more likely to be FHR MM patients. Patients who do not undergo maintenance therapy are prone to early progression or relapse. Full article

Background/Objectives: Prostate cancer (PCa) is prevalent among men over 70. Treatment may involve interventions like radical prostatectomy. The objective of this study was to investigate the combination of adverse pathology patterns on PCa progression through the Briganti 2012 nomogram and EAU risk classes in elderly patients treated with robotic surgery. Methods: A cohort of 1047 patients treated from January 2013 to December 2021 was categorized as being older if aged 70 or above. The adverse pathology risk scores were ranked from zero to three. These scores were then analyzed for correlations with the Briganti 2012 nomogram via EAU risk groups and for PCa progression. Results: Overall, older age was detected in 287 patients who had higher rates of adverse pathology features combined into a pattern risk score of 3. Within each age group, the adverse pathology risk score patterns were positively predicted by the Briganti 2012 nomogram across EAU prognostic groups. After a median (95% CI) follow-up period of 95 months, PCa progression occurred in 237 patients, of whom 68 were elderly and more likely to progress as adverse pathology patterns increased, particularly for a risk score of 3 (p < 0.0001), which was almost three times higher than that in younger patients (p < 0.0001). Conclusions: Managing PCa in elderly patients is challenging due to their increasing life expectancy. The Briganti 2012 nomogram effectively predicts disease progression in this population. Elderly prostate cancer patients have higher severe pathology rates predicted independently by the Briganti 2012 nomogram, with nearly triple the risk of progression compared to that in younger cases, necessitating tailored treatment approaches. Full article

Surgery is the cornerstone of vulvar cancer treatment, but it is associated with a significant risk of complications that may impact prognosis, particularly in older patients with multiple comorbidities. The objective of this study was to evaluate the role of age, comorbidities, and frailty in predicting postoperative complications after vulvar cancer surgery and to develop a predictive nomogram. A retrospective cohort study was conducted, including patients who underwent surgery for vulvar cancer at two Italian institutions from January 2018 to December 2023. A logistic regression model for the rate of Clavien-Dindo 2+ 30-days complications was run, considering the age-adjusted Charlson Comorbidity Index (AACCI), body mass index (BMI), and frailty as exposures. Lesion characteristics and surgical procedures were considered as confounders. Among the 225 included patients, 50 (22.2%) had a grade 2+ complication. The predictive score of the nomogram ranged from 44 to 140. The AACCI (0–64 points) and BMI (0–100 points) were independently associated with a risk of complications. A nomogram including the AACCI and BMI predicts the risk of complications for patients undergoing surgery for vulvar cancer. The preoperative determination of the risk of complications enables surgical planning and allows a tailored peri- and postoperative management plan. Full article

Background: Immunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach. Methods: We defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA tumor types and 29 GTEx normal tissues. We explored the 46,993 single cells of two lung cancer datasets. The immunosenescence risk model (ISRM) was constructed in TCGA LUAD by network analysis, immune infiltration analysis, and lasso regression and validated by survival analysis, cox regression, and nomogram in four lung cancer cohorts. The predictive ability of ISRM for drug response and immunotherapy was detected by the oncopredict algorithm and XGBoost model. Results: We found that senescent lung tissues were significantly enriched in ISGS and revealed the heterogeneity of immunosenescence in pan-cancer. Single-cell and bulk transcriptomics characterized the distinct immune microenvironment between old and young lung cancer. The ISGS network revealed the crucial function modules and transcription factors. Multiplatform analysis revealed specific associations between immunosenescence and the tumor progression of lung cancer. The ISRM consisted of five risk genes (CD40LG, IL7, CX3CR1, TLR3, and TLR2), which improved the prognostic stratification of lung cancer across multiple datasets. The ISRM showed robustness in immunotherapy and anti-tumor therapy. We found that lung cancer patients with a high-risk score showed worse survival and lower expression of immune checkpoints, which were resistant to immunotherapy. Conclusions: Our study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery. Full article

Background: Inflammation plays a critical role in the prognosis of patients with pulmonary arterial hypertension (PAH). The lymphocyte-to-C-reactive protein ratio (LCR), as a novel inflammatory marker, has not been studied in patients with PAH. The objective of this study was to investigate the prognostic value of the LCR in patients with PAH. Methods: A retrospective cohort study was conducted on 116 patients with PAH diagnosed in Renji Hospital, School of Medicine, Shanghai Jiao Tong University, from January 2014 to December 2018. The primary outcome was a composite endpoint that included lung transplantation, rehospitalization for PAH, and all-cause death. The LCR is the ratio of the blood lymphocyte count to the C-reactive protein concentration. Results: A total of 116 patients with PAH were included in this study, with an average age of 41.53 years; 92.2% were female, and the event rate was 57.8%. Restricted cubic spline analysis confirmed a linear association between the LCR and the risk of clinical worsening events. Multivariate Cox proportional hazards analysis showed that the LCR was significantly negatively associated with clinical worsening events, with hazard ratios and 95% confidence intervals of 0.772 (0.614–0.970). The Kaplan–Meier curve showed that event-free survival decreased significantly when the LCR was less than 1.477. LASSO regression selected four potential predictors, including the LCR, to construct a nomogram. The nomogram had a high predictive strength, with an area under the ROC curve of 0.805 (0.713–0.896). The calibration curves and decision curve analysis indicated that the nomogram had good predictive performance and the ability to guide clinical management. Conclusions: The LCR is a valuable prognostic marker for predicting long-term clinical events in patients with PAH, and the nomogram incorporating the LCR could effectively stratify risk and guide clinical decision making. Full article

Background: Malnutrition is highly prevalent in patients with head and neck cancer, with relevant consequences in the treatment results. Methods: Multicenter observational study including 514 patients diagnosed with HNC. The morphofunctional assessment was carried out during the first 2 weeks of radiotherapy treatment. A correlation analysis between nutritional variables and groups of malnutrition, a multivariate logistic regression analysis, and a random forest analysis to select the most relevant variables to predict malnutrition were performed. Results: In total, 51.6% were undernourished (26.3% moderately and 25.3% severely). There was a negative correlation between morphofunctional variables and a positive correlation between hsCRP and well vs. moderate and well vs. severe malnutrition groups. The increase in different bioelectrical and ultrasound parameters was associated with a lower risk of moderate and severe malnutrition when groups with different degrees of malnutrition were compared. To predict the importance of morphofunctional variables on the risk of undernutrition, a nomogram, a random forest, and decision tree models were conducted. For the well vs. moderate, for the well vs. severe, and for the moderate vs. severe malnutrition groups, FFMI (cut-off < 20 kg/m²), BCMI (cut-off < 7.6 kg/m²), and RF-Y-axis (cut-off < 0.94 cm), respectively, were the most crucial variables, showing a greater probability of mortality in the two last comparisons. Conclusions: Malnutrition is very prevalent in HNC patients. Morphofunctional assessment with simple tools such as electrical impedance and muscle ultrasound allows an early nutritional diagnosis with an impact on survival. Therefore, these techniques should be incorporated into the daily clinical attention of patients with HNC. Full article