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20 pages, 3067 KiB  
Article
Development of a Gluten Standard from Relevant Sources of Wheat and Investigation into Gluten Content of Supplemental Enzymes Generated During Fermentation
by Pyeongsug Kim, Natasha Kim Leeuwendaal, Jonathon Niño Charari, Joan Colom, John Deaton and Kieran Rea
Fermentation 2025, 11(1), 21; https://doi.org/10.3390/fermentation11010021 - 7 Jan 2025
Viewed by 187
Abstract
During fermentation, bacterial and fungal species synthesize substrate-specific enzymes to obtain nutrients. During this process, potential allergenic products, including immunologically important gluten peptides, can be created. Current protocols for assessing the levels of these peptides often overlook the specific gluten source. In this [...] Read more.
During fermentation, bacterial and fungal species synthesize substrate-specific enzymes to obtain nutrients. During this process, potential allergenic products, including immunologically important gluten peptides, can be created. Current protocols for assessing the levels of these peptides often overlook the specific gluten source. In this study, wheat sources provided by commercial enzyme suppliers underwent gluten extraction before being pooled into a Complete Gluten Mix, which then underwent variations of hydrolysis utilizing the digestive enzymes, pepsin and trypsin complexes. The resulting gluten peptide profiles were examined using the Wes automated Western blot system to confirm the presence of small, immunologically relevant gluten peptides. These hydrolysates were further tested for suitability as a relevant calibrant against commercially available ELISA standards. The PT3 calibrant, a hydrolyzed version of the Complete Gluten Mix, was found to be the most suitable, as it contained <50 kDa gluten peptides and gave similar absorbance readings to the majority of ELISA kit standards tested, and overlaid the GlutenTox® Competitive G12 antibody calibration curve, which was designed against the 33-mer immunogenic peptide from wheat. Additionally, no gluten bands were observed on the Wes for the enzymes of interest, which was confirmed through ELISA analysis. Full article
(This article belongs to the Special Issue Bioactive Compounds in Grain Fermentation: 2nd Edition)
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Figure 1
<p>Wes banding patterns of CGM, PT0.3, and PT3 at concentrations of 400, 200, 40, and 20 µg/mL with (<b>A</b>) G12, (<b>B</b>) R5, (<b>C</b>) MIoBS, (<b>D</b>) 2D4, (<b>E</b>) Skerritt, and (<b>F</b>) USDA. Each figure represents images obtained at a high dynamic range of 4.0 in Wes with different contrast settings for visualization of low-signal-intensity proteins or peptide bands. Lane information for immunoblots: 1Molecular Weight Ladder (12–230 kDa); 2—400 µg/mL CGM; 3—200 µg/mL CGM; 4—40 µg/mL CGM; 5—20 µg/mL CGM; 6—400 µg/mL PT0.3; 7—200 µg/mL PT0.3; 8—40 µg/mL PT0.3, 9—20 µg/mL PT0.3; 10—400 µg/mL PT3; 11—200 µg/mL PT3; 12—40 µg/mL PT3; 13—20 µg/mL PT3; 14—Molecular Weight Ladder (2–40 kDa).</p>
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<p>Wes banding patterns of PT3 at 400 µg/mL after maximized contrast with all 6 gluten-targeting antibodies on a 2–40 kDa cartridge. The &lt;5 kDa gluten peptide was only recognized by the G12 antibody as indicated in the highlighted box. Lane information for immunoblot: 1—Molecular Weight Ladder (2–40 kDa); 2—G12; 3—R5; 4—MioBS; 5—2D4; 6—Skerritt; 7—USDA.</p>
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<p>Commercial ELISA standard curves with PT hydrolysates at the same concentrations, as per the kit specifications, for antibodies (<b>A</b>) G12, (<b>B</b>) R5, (<b>C</b>) MIoBS, (<b>D</b>) 2D4, (<b>E</b>) Skerritt, and (<b>F</b>) USDA. Each data point represents the average of duplicate analysis of a single biological replicate. <span class="html-fig-inline" id="fermentation-11-00021-i001"><img alt="Fermentation 11 00021 i001" src="/fermentation/fermentation-11-00021/article_deploy/html/images/fermentation-11-00021-i001.png"/></span> Kit standard; <span class="html-fig-inline" id="fermentation-11-00021-i002"><img alt="Fermentation 11 00021 i002" src="/fermentation/fermentation-11-00021/article_deploy/html/images/fermentation-11-00021-i002.png"/></span> CGM; <span class="html-fig-inline" id="fermentation-11-00021-i003"><img alt="Fermentation 11 00021 i003" src="/fermentation/fermentation-11-00021/article_deploy/html/images/fermentation-11-00021-i003.png"/></span> PT3; <span class="html-fig-inline" id="fermentation-11-00021-i004"><img alt="Fermentation 11 00021 i004" src="/fermentation/fermentation-11-00021/article_deploy/html/images/fermentation-11-00021-i004.png"/></span> PT0.3.</p>
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<p>Wes banding patterns of ENZs (2 mg/mL) in the presence (lanes 3–13) and absence (lanes 15–25) of PT3 (200 µg/mL), bound by the antibodies (<b>A</b>) G12, (<b>B</b>) R5, (<b>C</b>) MIoBS, (<b>D</b>) 2D4, (<b>E</b>) Skerritt, and (<b>F</b>) USDA. Each figure represents images obtained at a high dynamic range of 4.0 in Wes with different contrast settings for visualization of low-signal-intensity proteins or peptide bands. Lane information for immunoblots: 1—Molecular Weight Ladder (12–230 kDa); 2—200 µg/mL PT3; 3—FPA+PT3; 4—AFP+PT3; 5—BP+PT3; 6—AlphaG+PT3; 7—CellAN+PT3; 8—FL+PT3; 9—LipAN+PT3; 10—Pep-I+PT3; 11—Pep-II+PT3; 12—INV+PT3; 13—LAA+PT3; 14—Molecular Weight Ladder (2–40 kDa); 15—FPA; 16—AFP; 17—BP; 18—AlphaG; 19—CellAN; 20—FL; 21—LipAN; 22—Pep-I; 23—Pep-II; 24—INV; 25—LAA.</p>
Full article ">Figure 4 Cont.
<p>Wes banding patterns of ENZs (2 mg/mL) in the presence (lanes 3–13) and absence (lanes 15–25) of PT3 (200 µg/mL), bound by the antibodies (<b>A</b>) G12, (<b>B</b>) R5, (<b>C</b>) MIoBS, (<b>D</b>) 2D4, (<b>E</b>) Skerritt, and (<b>F</b>) USDA. Each figure represents images obtained at a high dynamic range of 4.0 in Wes with different contrast settings for visualization of low-signal-intensity proteins or peptide bands. Lane information for immunoblots: 1—Molecular Weight Ladder (12–230 kDa); 2—200 µg/mL PT3; 3—FPA+PT3; 4—AFP+PT3; 5—BP+PT3; 6—AlphaG+PT3; 7—CellAN+PT3; 8—FL+PT3; 9—LipAN+PT3; 10—Pep-I+PT3; 11—Pep-II+PT3; 12—INV+PT3; 13—LAA+PT3; 14—Molecular Weight Ladder (2–40 kDa); 15—FPA; 16—AFP; 17—BP; 18—AlphaG; 19—CellAN; 20—FL; 21—LipAN; 22—Pep-I; 23—Pep-II; 24—INV; 25—LAA.</p>
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13 pages, 1581 KiB  
Article
Donor Identification, Genetic Diversity, Population Structure and Marker–Trait Association Analyses for Iron Toxicity Tolerance Using Rice Landraces
by Debanjana Saha, Udit Nandan Mishra, Chittaranjan Sahoo, Seema Tripathy, Uttam Kumar Behera, Susmita Das, Chandrasekhar Sahu, Shiv Datt, Manoj Kumar Rout, Tanmaya Lalitendu Mohanty, Shakti Prakash Mohanty, Saumya Ranjan Barik, Ishwar Chandra Mohanty and Sharat Kumar Pradhan
Diversity 2025, 17(1), 33; https://doi.org/10.3390/d17010033 - 31 Dec 2024
Viewed by 278
Abstract
Uptake of excess iron by lowland rice plants causes iron toxicity, which is a major problem in the affected areas. This study investigated molecular diversity, genetic structure, and marker–trait associations for tolerance to iron toxicity in a panel of germplasm lines using microsatellite [...] Read more.
Uptake of excess iron by lowland rice plants causes iron toxicity, which is a major problem in the affected areas. This study investigated molecular diversity, genetic structure, and marker–trait associations for tolerance to iron toxicity in a panel of germplasm lines using microsatellite markers. The studied population showed a moderate to high degree of genetic diversity, as revealed by the estimated molecular diversity parameters and principal component, cluster and box plot analyses. The landraces Mahipal, Dhusura, Dhabalabhuta, Champa, Sunapani and Kusuma were identified as suitable for cultivation in the areas affected by high iron levels. The landraces Dhusura, Kusuma, Kendrajhali, Ranisaheba, Panjabaniswarna, Mahipal, Dhinkisiali, Champa, Kalamara and Ratanmali, which showed low scores for tolerance, were considered good donors for iron toxicity tolerance improvement programs. Utilizing STRUCTURE software, a total of four genetic structure groups were detected in the panel germplasm of lines. These structural subgroups exhibited good correlations among their members for iron toxicity tolerance and other yield-related traits. Marker–trait association analysis validated the reported iron toxicity tolerance QTLs qFeTox 4.2 and qFeTox 4.3, which are useful for marker-assisted improvement. A new QTL, qFeTox 7.1, located on chromosome 7, was detected as controlling iron toxicity tolerance in rice. Full article
(This article belongs to the Special Issue Genetic Diversity and Plant Breeding)
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<p>Trait biplot diagram obtained using 9 studied traits and the panel population.</p>
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<p>Box plot diagram showing the variation in the nine traits in the panel population used for marker–trait analysis. DFF: days to 50% flowering, NETN: Number of Effective Tiller, PH: plant height, LBI: leaf bronzing score, PL: panicle length, NGPP: number of grains/panicle, GW: grain weight, YLD (Fe): yield under iron toxicity, YLD (C): yield under control.</p>
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<p>(<b>A</b>) Graph generated by plotting delta K and K for determination of peak value and (<b>B</b>) the genetic structure groups obtained for the studied panel population and sorted as per the group.</p>
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<p>Fst values obtained for the 4 subpopulations and alpha values obtained from the panel population.</p>
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15 pages, 679 KiB  
Article
Simulation of the Long-Term Toxicity Towards Bobwhite Quail (Colinus virginianus) by the Monte Carlo Method
by Nadia Iovine, Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni and Emilio Benfenati
J. Xenobiot. 2025, 15(1), 3; https://doi.org/10.3390/jox15010003 - 26 Dec 2024
Viewed by 511
Abstract
In this study, models for NOEL (No Observed Effect Level) and NOEC (No Observed Effect Concentration) related to long-term/reproduction toxicity of various organic pesticides are built up, evaluated, and compared with similar models proposed in the literature. The data have been obtained from [...] Read more.
In this study, models for NOEL (No Observed Effect Level) and NOEC (No Observed Effect Concentration) related to long-term/reproduction toxicity of various organic pesticides are built up, evaluated, and compared with similar models proposed in the literature. The data have been obtained from the EFSA OpenFoodTox database, collecting only data for the Bobwhite quail (Colinus virginianus). Models have been developed using the CORAL-2023 program, which can be used to develop quantitative structure–property/activity relationships (QSPRs/QSARs) and the Monte Carlo method for the optimization of the model. The software provided a model which may be considered useful for the practice. The determination coefficient of the best models for the external validation set was 0.665. Full article
(This article belongs to the Section Ecotoxicology)
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<p>The flow chart of the optimization of the correlation weight with target function (<span class="html-italic">TF</span>). <span class="html-italic">D</span> is some delta, i.e., the coefficient for modification of the correlation weights.</p>
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27 pages, 17565 KiB  
Article
Ulcerative Colitis, LAIR1 and TOX2 Expression, and Colorectal Cancer Deep Learning Image Classification Using Convolutional Neural Networks
by Joaquim Carreras, Giovanna Roncador and Rifat Hamoudi
Cancers 2024, 16(24), 4230; https://doi.org/10.3390/cancers16244230 - 19 Dec 2024
Viewed by 496
Abstract
Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep [...] Read more.
Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep learning). Methods: A convolutional neural network (CNN) was designed and trained to classify the three types of diagnosis, including 35 cases of ulcerative colitis (n = 9281 patches), 21 colon control (n = 12,246), and 18 colorectal cancer (n = 63,725). The data were partitioned into training (70%) and validation sets (10%) for training the network, and a test set (20%) to test the performance on the new data. The CNNs included transfer learning from ResNet-18, and a comparison with other CNN models was performed. Explainable artificial intelligence for computer vision was used with the Grad-CAM technique, and additional LAIR1 and TOX2 immunohistochemistry was performed in ulcerative colitis to analyze the immune microenvironment. Results: Conventional clinicopathological analysis showed that steroid-requiring ulcerative colitis was characterized by higher endoscopic Baron and histologic Geboes scores and LAIR1 expression in the lamina propria, but lower TOX2 expression in isolated lymphoid follicles (all p values < 0.05) compared to mesalazine-responsive ulcerative colitis. The CNN classification accuracy was 99.1% for ulcerative colitis, 99.8% for colorectal cancer, and 99.1% for colon control. The Grad-CAM heatmap confirmed which regions of the images were the most important. The CNNs also differentiated between steroid-requiring and mesalazine-responsive ulcerative colitis based on H&E, LAIR1, and TOX2 staining. Additional classification of 10 new cases of colorectal cancer (adenocarcinoma) were correctly classified. Conclusions: CNNs are especially suited for image classification in conditions such as ulcerative colitis and colorectal cancer; LAIR1 and TOX2 are relevant immuno-oncology markers in ulcerative colitis. Full article
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<p>Histological types. Upper section: the three types of samples that were included in this study were ulcerative colitis (<b>left</b>), colon control (<b>middle</b>), and colorectal cancer (adenocarcinoma, <b>right</b>); original magnification 100×. Lower section: the whole-slide images were split into patches of 224 × 224 size (i.e., divided into multiple images of 224 px width and 224 px height). Only the diagnostic areas marked in yellow were used in the CNN analysis; original magnification 200×.</p>
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<p>Images of ulcerative colitis. Whole-slide images at original magnification 200× were split into image patches of 224 × 224 size (i.e., divided into multiple images of 224 px width and 224 px height). Ulcerative colitis is an idiopathic chronic inflammation that affects the colon mucosa. This disorder characteristically affects the rectum and extends toward proximal sections of the colon in a continuous manner. Microscopically, there are signs of active chronic colitis when untreated. Chronicity includes distorted architecture of the crypts, such as atrophy, irregular spacing, shortening, and branching; inflammation of the lamina propria with basal lymphoplasmacytosis; and Panet cell metaplasia or hyperplasia. Disease activity is confirmed by neutrophil infiltration of the muscosa, cryptitis, crypt abscess, or ulceration. Typically, inflammation is limited to the mucosa and submucosa, and granulomas and fissuring ulcers are absent. Well-established disease can be associated with dysplasia of the epithelium, either low-grade or high-grade. The most commonly used score to evaluate the histological features is the Geboes score [<a href="#B93-cancers-16-04230" class="html-bibr">93</a>,<a href="#B94-cancers-16-04230" class="html-bibr">94</a>,<a href="#B95-cancers-16-04230" class="html-bibr">95</a>,<a href="#B96-cancers-16-04230" class="html-bibr">96</a>,<a href="#B97-cancers-16-04230" class="html-bibr">97</a>].</p>
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<p>Images of colorectal cancer (adenocarcinoma). Original magnification 200×. Whole-slide images were split into patches of 224 × 224 size. Adenocarcinoma of the colon is a glandular neoplasm that accounts for approximately 98% of all colonic cancers. Patients with inflammatory bowel disease, polyposis, and Lynch syndrome [<a href="#B98-cancers-16-04230" class="html-bibr">98</a>,<a href="#B99-cancers-16-04230" class="html-bibr">99</a>] are at a higher risk of developing colorectal cancer. Most cases display high or moderate differentiation of the carcinoma glands accompanied by marked growth of the fibrous connective tissue, known as desmoplasia [<a href="#B100-cancers-16-04230" class="html-bibr">100</a>,<a href="#B101-cancers-16-04230" class="html-bibr">101</a>]. The glands can show a cribriform pattern and are filled with necrotic debris. Adenocarcinomas are characterized by epithelial cells with stretched and stratified nuclei, which create complex glandular structures. The nuclei exhibit polymorphism and loss of polarity. The tumor immune microenvironment exhibits variable infiltration of inflammatory cells. There are several recognized subtypes, including adenoma-like, adenosquamous, mucinous, micropapillary, signet-ring, serrated, and sarcomatoid [<a href="#B93-cancers-16-04230" class="html-bibr">93</a>,<a href="#B101-cancers-16-04230" class="html-bibr">101</a>,<a href="#B102-cancers-16-04230" class="html-bibr">102</a>,<a href="#B103-cancers-16-04230" class="html-bibr">103</a>,<a href="#B104-cancers-16-04230" class="html-bibr">104</a>,<a href="#B105-cancers-16-04230" class="html-bibr">105</a>,<a href="#B106-cancers-16-04230" class="html-bibr">106</a>,<a href="#B107-cancers-16-04230" class="html-bibr">107</a>].</p>
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<p>Images of the colon control. Original magnification 200×. Whole-slide images were split into patches of 224 × 224 size. The colonic mucosa functions primarily to absorb water and electrolytes, a process carried out by absorptive columnar cells, and to produce mucus for lubrication, which is secreted by goblet cells. The mucosa comprises the epithelium, lamina propria, and muscularis mucosa. The epithelium invaginates and forms glands (crypts), where at its base there is also the presence of enteroendocrine, Paneth cells, and stem cells. The lamina propria is rich in capillaries and lymphatics. Loose connective tissue and nerve plexuses are found in the submucosa. The muscularis propria has an inner circular layer and an outer longitudinal layer, and within them, the Auerbach nerve plexus is located. The outer layers are the subserosa and serosa [<a href="#B93-cancers-16-04230" class="html-bibr">93</a>,<a href="#B108-cancers-16-04230" class="html-bibr">108</a>,<a href="#B109-cancers-16-04230" class="html-bibr">109</a>].</p>
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<p>Conventional immunohistochemical analysis of LAIR1 and TOX2 in ulcerative colitis. LAIR1 and TOX2 are two new immuno-oncology markers that target cells of the microenvironment. TOX2 is comparable to PD-1. In comparison to mesalazine-responsive ulcerative colitis, the steroid-requiring type was characterized by higher protein expression of LAIR1 (20.74% ± 7.48 vs. 28.18% ± 6.26, <span class="html-italic">p</span> = 0.001) and lower TOX2-positive cells in the isolated lymphoid follicles (ILFs) (11.74% ± 3.47 vs. 7.03% ± 5.03, <span class="html-italic">p</span> = 0.019). ILFs, isolated lymphoid follicles of the lamina propria. Original magnification 200×. The isolated lymphoid follicles are highlighted using a yellow circle.</p>
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<p>Network performance during training and validation. The data (image patches) were partitioned into a training set (70% of the images) to train the network and a validation set (10%) to test the performance of the network during the training; a test set (20%) was used as a holdout to test the performance of the trained network on new data. This figure shows the accuracy and loss during the training (70%) and validation (10%) sets. The CNN was based on transfer learning from ResNet-18.</p>
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<p>Confusion matrix of the test dataset (new data). The data were partitioned into a training set (70% of the image patches) to train the network and a validation set (10%) to test the performance of the network during the training; a test set (20%) was used as a holdout to test the performance of the trained network on new data.</p>
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<p>Explanation of network predictions using Grad-CAM. Grad-CAM was used to visualize which regions of the image were important for the classification decision (diagnosis) of the network. The most relevant regions are highlighted in red (jet colormap). The prediction scores for each diagnosis are shown below each hematoxylin and eosin (H&amp;E) image. ADK, adenocarcinoma (colorectal cancer); CC, colon control; UC, ulcerative colitis. Original magnification 200× (whole-slide images were split into patches of 224 × 224 size).</p>
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<p>Grad-CAM analysis of incorrectly classified images. Grad-CAM analysis was used to visualize which regions of the image were important for the classification decision (diagnosis) of the network. In most cases, the classification errors occurred because the network focused on incorrect areas within the image or because the image itself was not diagnostic from a histopathological point of view. Original magnification 200× (whole-slide images were split into patches of 224 × 224 size).</p>
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<p>Confusion matrix of the test dataset for the classification of steroid-requiring and nonsteroid-requiring/mesalazine-responsive. The test dataset included new data (holdout, 20%). The analysis was based on transfer learning from ResNet-18. The accuracy was 79.53%.</p>
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<p>Confusion matrix of the test dataset for the classification of steroid-requiring and nonsteroid-requiring/mesalazine-responsive using LAIR1 immunohistochemistry. The test dataset included new data (holdout, 20%). The analysis was based on transfer learning from ResNet-18. The accuracy was 88.31%.</p>
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<p>LAIR1 immunohistochemistry in the ulcerative colitis dataset and classification of steroid-requiring and nonsteroid-requiring/mesalazine-responsive ulcerative colitis cases. Overall, the inflammatory component was higher in the steroid-requiring cases. Original magnification 200×.</p>
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<p>Examples of the split images of LAIR1 immunohistochemistry in the ulcerative colitis dataset. The split images were used as input data in the CNN, which managed to classify between steroid-requiring and nonsteroid-requiring/mesalazine-responsive ulcerative cases. Overall, the inflammatory component was higher in the steroid-requiring cases. Original magnification 200× (whole-slide images were split into patches of 224 × 224 size).</p>
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<p>Confusion matrix of the test dataset for the classification of steroid-requiring and nonsteroid-requiring/mesalazine-responsive using TOX2 immunohistochemistry. The test dataset included new data (holdout, 20%). The analysis was based on transfer learning from ResNet-18. The accuracy was 85.62%.</p>
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<p>TOX2 immunohistochemistry in the ulcerative colitis dataset and classification of steroid-requiring and nonsteroid-requiring/mesalazine-responsive ulcerative colitis cases. Overall, the TOX2-positive inflammatory component was higher in the mesalazine-responsive cases. Original magnification 100×.</p>
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<p>Examples of the split images of TOX2 immunohistochemistry in the ulcerative colitis dataset. The split images were used as input data in the CNN, which managed to classify between steroid-requiring and nonsteroid-requiring/mesalazine-responsive ulcerative cases. Overall, the TOX2-positive inflammatory component was higher in the mesalazine-responsive cases. Original magnification 200× (whole-slide images were split into patches of 224 × 224 size).</p>
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14 pages, 2623 KiB  
Article
Effect of Gold Nanoparticles on Luminescence Enhancement in Antibodies for TORCH Detection
by Cuimei Chen and Ping Ding
Molecules 2024, 29(23), 5722; https://doi.org/10.3390/molecules29235722 - 4 Dec 2024
Viewed by 566
Abstract
Purposes: To explore the optimization method and application of Au-NP-enhanced luminol––H2O2 luminescence system in TORCH (TOX, RV, CMV, HSVI, and HSVII) detection. Method: 4.5 × 10−5 mmol/L gold nano solution was prepared with chloroauric acid as the reducing agent [...] Read more.
Purposes: To explore the optimization method and application of Au-NP-enhanced luminol––H2O2 luminescence system in TORCH (TOX, RV, CMV, HSVI, and HSVII) detection. Method: 4.5 × 10−5 mmol/L gold nano solution was prepared with chloroauric acid as the reducing agent and trisodium citrate as the stabilizer. After curing for 3 days, Au NPs participate in the luminal–H2O2 luminescence system to detect TORCH antibodies and establish the cut off value. SPSS 18.0 software was used to analyze the TORCH antibodies detected by the nano-gold-enhanced luminol luminescence method and TORCH kit. Additionally, its detection performance is studied. Results: The results of a paired t-test for the absorbance values of samples with and without gold nanoparticles showed that there were statistically significant differences (p < 0.001) between the two methods in the detection of TOX, RV, CMV, HSVI, and HSVII. The luminescence values with the addition of gold nanoparticles were significantly higher than those without gold nanoparticles. Using the Au NP–luminol–H2O2 chemiluminescence method, 127 serum samples were tested for TORCH antibodies. The sensitivities were 84.6%, 83.3%, 90.9%, 85.7%, and 84.6%, while the specificities were 94.7%, 96.5%, 96.6%, 97.3%, and 95.6%, respectively. The sensitivity and specificity of the chemiluminescence method enhanced by gold nanoparticles are significantly improved compared to the chemiluminescence method without enhancers. Conclusions: Au NPs participate in the luminal–H2O2 luminescent system. The absorbance, sensitivity, and specificity of TORCH antibodies show that Au NPs can enhance the luminol–H2O2 luminescent system. Au NP–luminol–H2O2 luminescence system has broad application prospects in the detection of eugenics. Full article
(This article belongs to the Section Nanochemistry)
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<p>(<b>A</b>) UV–vis spectrum of Au NPs; (<b>B</b>) TEM image of Au NPs.</p>
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<p>Effect of curing time on catalytic activity of Au NPs.</p>
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<p>Effect of Au-NPs concentration on the luminescence value of the system.</p>
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<p>Comparison of sensitivity between Au-NP/luminol–H<sub>2</sub>O<sub>2</sub> and non Au-NP/luminol–H<sub>2</sub>O<sub>2</sub>.</p>
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<p>Comparison of specificity between Au-NP/luminol–H<sub>2</sub>O<sub>2</sub> and non Au-NP/luminol–H<sub>2</sub>O<sub>2</sub>.</p>
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<p>Process of Au-NP-enhanced luminol chemiluminescence detection of TORCH antibodies.</p>
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<p>Diagram for formation of HO- free radicals in luminol–H<sub>2</sub>O<sub>2</sub> system.</p>
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19 pages, 5244 KiB  
Systematic Review
Long-Term Effects on Gonadal Function After Treatment of Colorectal Cancer: A Systematic Review and Meta-Analysis
by Christiane Anthon, Angela Vidal, Hanna Recker, Eva Piccand, Janna Pape, Susanna Weidlinger, Marko Kornmann, Tanya Karrer and Michael von Wolff
Cancers 2024, 16(23), 4005; https://doi.org/10.3390/cancers16234005 - 29 Nov 2024
Viewed by 546
Abstract
Background: The incidence of colorectal cancer (CRC) is increasing in the population under 50 years of age, with more than 10% of cases occurring in young adults. Fertility preservation counseling has therefore received increased attention in this younger patient population. The treatment of [...] Read more.
Background: The incidence of colorectal cancer (CRC) is increasing in the population under 50 years of age, with more than 10% of cases occurring in young adults. Fertility preservation counseling has therefore received increased attention in this younger patient population. The treatment of CRC is often based on multimodal therapies, including surgery, radiotherapy, chemotherapy, and, more recently, immunotherapy, which makes it difficult to estimate the expected effect of treatment on fertility. We, therefore, systematically analyzed the published literature on the gonadotoxic effects of CRC treatments to better advise patients on the risk of infertility and the need for fertility preservation measures. This systematic review and meta-analysis are part of the FertiTOX project, which aims to reduce the data gap regarding the gonadotoxicity of oncological therapies. Objectives: The aim of this review and meta-analysis is to evaluate the potential impact of CRC therapies on gonadal function to allow more accurate counseling regarding the risk of clinically relevant gonadotoxicity and the need for fertility preservation measures before oncological treatment. Materials and Methods: A systematic literature search was conducted in Medline, Embase, the Cochrane database of systematic reviews, and CENTRAL in March 2024. A total of 22 out of 4420 studies were included in the review. Outcomes were defined as clinically relevant gonadotoxicity, indicated by elevated follicle-stimulating hormone (FSH) and/or undetectable anti-Müllerian hormone (AMH) levels and/or the need for hormone replacement therapy in women and azoo-/oligozoospermia and/or low inhibin B levels in men. Studies with fewer than nine patients were excluded from the meta-analysis. Results: The qualitative analysis included 22 studies with 1634 subjects (775 women, 859 men). Treatment consisted of active surveillance after surgery (37.7%), chemotherapy (12.7%), radiation (0.2%), or radiochemotherapy (53.9%). In 0.5%, the therapy was not clearly described. The meta-analysis included ten studies and showed an overall prevalence of clinically relevant gonadotoxicity of 23% (95% CI: 13–37%). In women, the prevalence was 27% (95% CI: 11–54%), and in men, 18% (95% CI: 13–26%). A subanalysis by type of CRC was only possible for rectal cancer, with a prevalence of relevant gonadotoxicity of 39% (95% CI: 20–64%). In patients undergoing chemotherapy exclusively, the prevalence was 4% (95% CI: 2–10%). In those receiving only radiotherapy, the prevalence was 23% (95% CI: 10–44%); in contrast, it reached 68% (95% CI: 40–87%) in patients who received radiochemotherapy. Conclusions: This first meta-analysis of the clinically relevant gonadotoxicity of CRC therapies provides a basis for counseling on the risk of infertility and the need for fertility preservation measures. Despite the low prevalence of gonadotoxicity in cases receiving chemotherapy alone, fertility preservation is still recommended due to the uncertainty of subsequent therapy and the lack of large longitudinal data on individual treatment effects. Further prospective studies are needed to investigate the impact of CRC treatment on gonadal function and estimate the effect of new treatment modalities, such as immunotherapies. Full article
(This article belongs to the Section Systematic Review or Meta-Analysis in Cancer Research)
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<p>PRISMA flow diagram. A flowchart of the literature search and selection process.</p>
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<p>The pooled overall prevalence of general gonadotoxicity [<a href="#B27-cancers-16-04005" class="html-bibr">27</a>,<a href="#B30-cancers-16-04005" class="html-bibr">30</a>,<a href="#B32-cancers-16-04005" class="html-bibr">32</a>,<a href="#B33-cancers-16-04005" class="html-bibr">33</a>,<a href="#B34-cancers-16-04005" class="html-bibr">34</a>,<a href="#B35-cancers-16-04005" class="html-bibr">35</a>,<a href="#B37-cancers-16-04005" class="html-bibr">37</a>,<a href="#B38-cancers-16-04005" class="html-bibr">38</a>,<a href="#B40-cancers-16-04005" class="html-bibr">40</a>,<a href="#B43-cancers-16-04005" class="html-bibr">43</a>]. A forest plot of the proportions and 95% confidence intervals (CIs) in studies that evaluated the prevalence of clinically relevant gonadotoxicity in women and men following gonadotoxic therapy for CRC, where 0 means 0% clinically relevant gonadotoxicity and 1 = 100% clinically relevant gonadotoxicity. The blue square for each study indicates the proportion, the size of the box indicates the weight of the study, and the horizontal line indicates the 95% CI. The data in bold and the pink diamond represent the pooled prevalence for post-treatment clinically relevant gonadotoxicity and 95% CI. Overall estimates are shown in the fixed- and random-effects models.</p>
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<p>Pooled overall prevalence of gonadotoxicity in women [<a href="#B27-cancers-16-04005" class="html-bibr">27</a>,<a href="#B30-cancers-16-04005" class="html-bibr">30</a>,<a href="#B32-cancers-16-04005" class="html-bibr">32</a>,<a href="#B33-cancers-16-04005" class="html-bibr">33</a>,<a href="#B34-cancers-16-04005" class="html-bibr">34</a>,<a href="#B35-cancers-16-04005" class="html-bibr">35</a>]. For details, see legend of <a href="#cancers-16-04005-f002" class="html-fig">Figure 2</a>.</p>
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<p>Pooled overall prevalence of gonadotoxicity in men [<a href="#B35-cancers-16-04005" class="html-bibr">35</a>,<a href="#B37-cancers-16-04005" class="html-bibr">37</a>,<a href="#B38-cancers-16-04005" class="html-bibr">38</a>,<a href="#B40-cancers-16-04005" class="html-bibr">40</a>,<a href="#B43-cancers-16-04005" class="html-bibr">43</a>]. For details, see legend of <a href="#cancers-16-04005-f002" class="html-fig">Figure 2</a>.</p>
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<p>Pooled overall prevalence of gonadotoxicity, subgroup for rectal cancer [<a href="#B27-cancers-16-04005" class="html-bibr">27</a>,<a href="#B33-cancers-16-04005" class="html-bibr">33</a>,<a href="#B34-cancers-16-04005" class="html-bibr">34</a>,<a href="#B37-cancers-16-04005" class="html-bibr">37</a>,<a href="#B38-cancers-16-04005" class="html-bibr">38</a>,<a href="#B40-cancers-16-04005" class="html-bibr">40</a>,<a href="#B43-cancers-16-04005" class="html-bibr">43</a>]. For details, see legend of <a href="#cancers-16-04005-f002" class="html-fig">Figure 2</a>.</p>
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<p>Pooled overall prevalence of gonadotoxicity among those who received chemotherapy only [<a href="#B27-cancers-16-04005" class="html-bibr">27</a>,<a href="#B35-cancers-16-04005" class="html-bibr">35</a>,<a href="#B37-cancers-16-04005" class="html-bibr">37</a>]. For details, see legend of <a href="#cancers-16-04005-f002" class="html-fig">Figure 2</a>.</p>
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<p>Pooled overall prevalence of gonadotoxicity among those who received radiotherapy only [<a href="#B35-cancers-16-04005" class="html-bibr">35</a>,<a href="#B37-cancers-16-04005" class="html-bibr">37</a>,<a href="#B38-cancers-16-04005" class="html-bibr">38</a>]. For details, see legend of <a href="#cancers-16-04005-f002" class="html-fig">Figure 2</a>.</p>
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<p>Pooled overall prevalence of gonadotoxicity among those who received the combination of radiotherapy and chemotherapy treatment [<a href="#B27-cancers-16-04005" class="html-bibr">27</a>,<a href="#B33-cancers-16-04005" class="html-bibr">33</a>,<a href="#B34-cancers-16-04005" class="html-bibr">34</a>,<a href="#B35-cancers-16-04005" class="html-bibr">35</a>]. For details, see legend of <a href="#cancers-16-04005-f002" class="html-fig">Figure 2</a>.</p>
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1213 KiB  
Proceeding Paper
Molecular Docking/ADME-TOX-Based Analysis for New Anti-Colorectal Cancer Through Peroxiredoxin 1 Inhibition
by Imane Bensahbane, Nadjib Melkemi, Ismail Daoud and Faiza Asli
Chem. Proc. 2024, 16(1), 56; https://doi.org/10.3390/ecsoc-28-20215 - 14 Nov 2024
Viewed by 61
Abstract
Colorectal cancer ranks as the third most prevalent form of cancer on a global scale. The abnormal expression of Peroxiredoxin 1, or PRDX1, plays an important role in cancer progression and tumor cell survival. This makes inhibiting this protein a promising target for [...] Read more.
Colorectal cancer ranks as the third most prevalent form of cancer on a global scale. The abnormal expression of Peroxiredoxin 1, or PRDX1, plays an important role in cancer progression and tumor cell survival. This makes inhibiting this protein a promising target for colorectal cancer treatment. In order to develop effective PRDX1 inhibitors, a drug design investigation based on computational methods was carried out using a collection of recently synthesized compounds derived from two main chemical base structures: C-5 sulfenylated amino uracils and 1,2,3-triazole benzothiazole derivatives. To obtain the PRDX1 protein PDB ID: 7WET, molecular docking was performed on the studied compounds in combination with PRDX1. The 1,2,3-triazole benzothiazole derivatives showed interesting docking results. For instance, nine promising candidates were distinguished by their formation of better stable complexes with PRDX1 in terms of E (binding) from −7.0 to −7.3 kcal/mol, namely, 7WET-L18, 7WET-L17, 7WET-L25, 7WET-L19, 7WET-L20, 7WET-L26, 7WET-L22, 7WET-L23, and 7WET-L24, as well as an E of −6.8 kcal/mol for Celastrol, a known PRDX1 inhibitor. Moreover, an extensive evaluation of ADME-TOX was performed to predict the pharmacokinetic, pharmacodynamic, and toxicological properties of the compounds studied. The findings offer significant support for the prospective application of these analogs in the fight against colorectal cancer. Full article
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<p>Two-dimensional interactions and three-dimensional illustration of 7WET’s active site and L18.</p>
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19 pages, 1790 KiB  
Article
International Proficiency Test Targeting a Large Panel of Botulinum Neurotoxin Sero- and Subtypes in Different Matrices
by Christine Rasetti-Escargueil, Michel Robert Popoff, Bettina Kampa, Sylvia Worbs, Maud Marechal, Daniel Guerin, Eléa Paillares, Werner Luginbühl and Emmanuel Lemichez
Toxins 2024, 16(11), 485; https://doi.org/10.3390/toxins16110485 - 8 Nov 2024
Viewed by 887
Abstract
Detection of botulinum neurotoxins (BoNTs) involves a combination of technical challenges that call for the execution of inter-laboratory proficiency tests (PTs) to define the performance and ease of implementation of existing diagnostic methods regarding representative BoNT toxin-types spiked in clinical, food, or environmental [...] Read more.
Detection of botulinum neurotoxins (BoNTs) involves a combination of technical challenges that call for the execution of inter-laboratory proficiency tests (PTs) to define the performance and ease of implementation of existing diagnostic methods regarding representative BoNT toxin-types spiked in clinical, food, or environmental matrices. In the framework of the EU project EuroBioTox, we organized an international proficiency test for the detection and quantification of the clinically relevant BoNT/A, B, E, and F sero- and subtypes including concentrations as low as 0.5 ng/mL. BoNTs were spiked in serum, milk, and soil matrices. Here, we evaluate the results of 18 laboratories participating in this PT. Participants have implemented a wide array of detection methods based on functional, immunological, and mass spectrometric principles. Methods implemented in this proficiency test notably included endopeptidase assays either coupled to mass spectrometry (Endopep-MS) or enzyme-linked immunosorbent assays (Endopep-ELISA). This interlaboratory exercise pinpoints the most effective and complementary methods shared by the greatest number of participants, also highlighting the importance of combining the training of selected methods and of distributing toxin reference material to reduce the variability of quantitative data. Full article
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<p>Results reported by the participants after anonymization (lines) for each BoNT and by sample described in <a href="#toxins-16-00485-t001" class="html-table">Table 1</a> (columns). Green indicates that the participant reached a correct conclusion based on the results of one or more methods implemented. Red indicates a wrong conclusion. The absence of reported data is indicated as (-). For some reported data, more details are provided according to the figure legend: partially false (yellow) and no final conclusion (grey). The samples spiked with the indicated BoNT serotype are indicated with a blue arrowhead and blank samples with a grey arrowhead.</p>
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<p>Normal probability plots of selected samples. The normal probability plots show z’-scores without extremes (z’ &gt; 10), and without responses “&lt;LoD” or “&lt;LoQ” for samples with x<sub>pt</sub> &gt; 0.</p>
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<p>Overall z’-scores means for BoNT/A or BoNT/B quantification. The plots show the z’-score means (points) and their standard deviations (error bars span mean ± sd) as computed from the individual scores (results &lt; LoD or &lt;LoQ for BoNT-containing samples excluded).</p>
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18 pages, 822 KiB  
Article
Does Every Strain of Pseudomonas aeruginosa Attack the Same? Results of a Study of the Prevalence of Virulence Factors of Strains Obtained from Different Animal Species in Northeastern Poland
by Paweł Foksiński, Alicja Blank, Edyta Kaczorek-Łukowska, Joanna Małaczewska, Małgorzata Wróbel, Ewelina A. Wójcik, Patrycja Sowińska, Nina Pietrzyk, Rafał Matusiak and Roman Wójcik
Pathogens 2024, 13(11), 979; https://doi.org/10.3390/pathogens13110979 - 8 Nov 2024
Viewed by 1078
Abstract
Background: Pseudomonas aeruginosa is a pathogen that causes infections in animals and humans, with veterinary implications including ear infections in dogs, respiratory diseases in cats, and mastitis in ruminants. In humans, it causes severe hospital-acquired infections, particularly in immunosuppressed patients. This study aimed [...] Read more.
Background: Pseudomonas aeruginosa is a pathogen that causes infections in animals and humans, with veterinary implications including ear infections in dogs, respiratory diseases in cats, and mastitis in ruminants. In humans, it causes severe hospital-acquired infections, particularly in immunosuppressed patients. This study aimed to identify and assess the prevalence of specific virulence factors in Pseudomonas aeruginosa isolates. Methods: We analyzed 98 Pseudomonas aeruginosa isolates from various animal samples (dogs, cats, ruminants, fowl) from northeastern Poland in 2019–2022 for virulence-related genes (toxA, exoU, exoT, exoS, lasB, plcN, plcH, pldA, aprA, gacA, algD, pelA, endA, and oprF) by PCR and assessed biofilm formation at 48 and 72 h. Genomic diversity was assessed by ERIC-PCR. Results: The obtained results showed that all strains harbored the pelA gene (100%), while the lowest prevalence was found for pldA (24%) and exoU (36%). Regardless of the animal species, strong biofilm forming ability was prevalent among the strains after both 48 h (75%) and 72 h (74%). We obtained as many as 87 different genotyping profiles, where the dominant one was profile ERIC-48, observed in four strains. Conclusions: No correlation was found between presence or absence of determined genes and the nature of infection. Similarly, no correlation was found between biofilm-forming genes and biofilm strength. The high genetic diversity indicates challenges for effective prevention, emphasizing the need for ongoing monitoring and research. Full article
(This article belongs to the Section Bacterial Pathogens)
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<p>Percentage frequency of virulence genes (<span class="html-italic">tox</span>A, <span class="html-italic">exo</span>U, <span class="html-italic">exo</span>T, <span class="html-italic">exo</span>S, <span class="html-italic">las</span>B, <span class="html-italic">plc</span>N, <span class="html-italic">plc</span>H, <span class="html-italic">pld</span>A, <span class="html-italic">apr</span>A, <span class="html-italic">gac</span>A, <span class="html-italic">alg</span>D, <span class="html-italic">pel</span>A, <span class="html-italic">end</span>A, <span class="html-italic">opr</span>F) among the analyzed <span class="html-italic">P. aeruginosa</span> strains.</p>
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<p>Percentage of strong, medium and weak biofilm producers by species after 48 and 72 h.</p>
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23 pages, 7695 KiB  
Article
Rational Approach to New Chemical Entities with Antiproliferative Activity on Ab1 Tyrosine Kinase Encoded by the BCR-ABL Gene: An Hierarchical Biochemoinformatics Analysis
by Vitor H. da S. Sanches, Cleison C. Lobato, Luciane B. Silva, Igor V. F. dos Santos, Elcimar de S. Barros, Alexandre de A. Maciel, Elenilze F. B. Ferreira, Kauê S. da Costa, José M. Espejo-Román, Joaquín M. C. Rosa, Njogu M. Kimani and Cleydson B. R. Santos
Pharmaceuticals 2024, 17(11), 1491; https://doi.org/10.3390/ph17111491 - 6 Nov 2024
Viewed by 812
Abstract
Background: This study began with a search in three databases, totaling six libraries (ChemBridge-DIVERSet, ChemBridge-DIVERSet-EXP, Zinc_Drug Database, Zinc_Natural_Stock, Zinc_FDA_BindingDB, Maybridge) with approximately 2.5 million compounds with the aim of selecting potential inhibitors with antiproliferative activity on the chimeric tyrosine kinase encoded by the [...] Read more.
Background: This study began with a search in three databases, totaling six libraries (ChemBridge-DIVERSet, ChemBridge-DIVERSet-EXP, Zinc_Drug Database, Zinc_Natural_Stock, Zinc_FDA_BindingDB, Maybridge) with approximately 2.5 million compounds with the aim of selecting potential inhibitors with antiproliferative activity on the chimeric tyrosine kinase encoded by the BCR-ABL gene. Methods: Through hierarchical biochemoinformatics, ADME/Tox analyses, biological activity prediction, molecular docking simulations, synthetic accessibility and theoretical synthetic routes of promising compounds and their lipophilicity and water solubility were realized. Results: Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the top100/base (600 structures), in comparison with the commercial drug imatinib, showed that only nine exhibited the desired properties. In the prediction of biological activity, the results of the nine selected structures ranged from 13.7% < Pa < 65.8%, showing them to be potential protein kinase inhibitors. In the molecular docking simulations, the promising molecules LMQC01 and LMQC04 showed significant values in molecular targeting (PDB 1IEP—resolution 2.10 Å). LMQC04 presented better binding affinity (∆G = −12.2 kcal mol−1 with a variation of ±3.6 kcal mol−1) in relation to LMQC01. The LMQC01 and LMQC04 molecules were advanced for molecular dynamics (MD) simulation followed by Molecular Mechanics with generalized Born and Surface Area solvation (MM-GBSA); the comparable, low and stable RMSD and ΔE values for the protein and ligand in each complex suggest that the selected compounds form a stable complex with the Abl kinase domain. This stability is a positive indicator that LMQC01 and LMQC04 can potentially inhibit enzyme function. Synthetic accessibility (SA) analysis performed on the AMBIT and SwissADME webservers showed that LMQC01 and LMQC04 can be considered easy to synthesize. Our in silico results show that these molecules could be potent protein kinase inhibitors with potential antiproliferative activity on tyrosine kinase encoded by the BCR-ABL gene. Conclusions: In conclusion, the results suggest that these ligands, particularly LMQC04, may bind strongly to the studied target and may have appropriate ADME/Tox properties in experimental studies. Considering future in vitro or in vivo assays, we elaborated the theoretical synthetic routes of the promising compounds identified in the present study. Based on our in silico findings, the selected ligands show promise for future studies in developing chronic myeloid leukemia treatments. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design, 2nd Edition)
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<p>General scheme summarizing the methodological steps.</p>
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<p>Superpositions of the ligand with crystallographic pose (in red) with the calculated poses (in green)—Abl Kinase Domain (organism <span class="html-italic">Mus musculus</span>, PDB ID 1IEP), showing an RMSD value equal to 0.4721 Å.</p>
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<p>Interactions of imatinib with key amino acid residues in the active site of the Ab1 kinase domain.</p>
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<p>Predicted interactions between the BCR-ABL tyrosine kinase active site and compound LMQC01.</p>
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<p>Predicted interactions between the active site of BCR-ABL tyrosine kinase and the compound LMQC04.</p>
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<p>RMSD alignment analysis among apo-protein and ligand-complexes for C-Abl kinase domain (PDB ID: 1IEP) (<b>a</b>) based on 300 ns MD analysis. MMGBSA_∆G_Binding value line chart for 300 ns MD simulation (<b>b</b>).</p>
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<p>Result of 300 ns MD analysis for imatinib binding to the Abl-kinase domain. The protein–ligand RMSD plot of imatinib bound to the Abl-kinase domain (<b>a</b>) (PDB ID: 1IEP). Ligand–protein contact interactions scheme with the protein residues of imatinib bound to Abl-kinase (<b>b</b>). Protein–ligand contacts histogram of the interaction fraction of H-bond (green), hydrophobic bond (purple), ionic bond (magenta), and water bridges (blue) for imatinib (<b>c</b>). RMSF plot of imatinib (<b>d</b>) protein–ligand complex.</p>
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<p>Result of 300 ns MD analysis for LMQC01 and LMQC04 binding to the Abl-kinase domain. The protein–ligand RMSD plot of LMQC01 (<b>a</b>) and LMQC04 (<b>b</b>) bound to the Abl-kinase domain (PDB ID: 1IEP). Ligand–protein contact interactions scheme with the protein residues of LMQC01 (<b>c</b>) and LMQC04 (<b>d</b>) bound to Abl-kinase. Protein–ligand contacts histogram of the interaction fraction of H-bond (green), hydrophobic bond (purple), ionic bond (magenta), water bridges (blue), and halogen bonds (orange) for LMQC01 (<b>e</b>) and LMQC04 (<b>f</b>). RMSF plot of LMQC01 (<b>g</b>) and LMQC04 (<b>h</b>) protein–ligand complex.</p>
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<p>Graphical representation of the molecular overlay analysis between molecules (<b>a</b>) LMQC01 (yellow) and (<b>b</b>) LMQC04 (blue) with the reference molecule (imatinib—green).</p>
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<p>Synthetic route of the compound <b>LMQC 01</b>. Starting materials <b>I</b> and <b>II</b> are commercially available.</p>
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<p>Alternative synthetic route of the compound <b>LMQC 01</b>. 4 Å MS (molecular sieves). Starting materials <b>I</b>, <b>IV</b> and <b>V</b> are commercially available.</p>
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<p>Synthetic route of the compound <b>LMQC 04</b>. Starting materials <b>XIII, XV</b> and <b>XVII</b> are commercially available.</p>
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17 pages, 3100 KiB  
Article
Environmental Drivers of the Divergence of Harveyi Clade Pathogens with Distinctive Virulence Gene Profiles
by Andrei L. Barkovskii and Cameron Brown
Microorganisms 2024, 12(11), 2234; https://doi.org/10.3390/microorganisms12112234 - 5 Nov 2024
Viewed by 698
Abstract
Fish and shellfish pathogens of the Harveyi clade of the Vibrio genus cause significant losses to aquaculture yields and profits, with some of them also causing infections in humans. The present study aimed to evaluate the presence of Harveyi clade fish and shellfish [...] Read more.
Fish and shellfish pathogens of the Harveyi clade of the Vibrio genus cause significant losses to aquaculture yields and profits, with some of them also causing infections in humans. The present study aimed to evaluate the presence of Harveyi clade fish and shellfish pathogens and their possible diversification in response to environmental drivers in southeastern USA waters. The presence and abundance of potential pathogens were evaluated via the detection and quantitation of six Harveyi-clade-specific virulence genes (toxR, luxR, srp, vhha, vhh, and vhp; VGs) in environmental DNA with clade-specific primers. The environmental DNA was obtained from water and sediments collected from three Georgia (USA) cultured clam and wild oyster grounds. In sediments, the VG concentrations were, on average, three orders of magnitude higher than those in water. The most and least frequently detected VGs were vhp and toxR, respectively. In water, the VGs split into two groups based on their seasonal trends. The first group, composed of luxR, vhp, vhha, and vhh, peaked in August and remained at lower concentrations throughout the duration of the study. The second group, composed of toxR and srp, peaked in June and disappeared between July and December. The first group revealed a high adaptation of their carriers to an increase in temperature, tolerance to a wide range of pH, and a positive correlation with salinity up to 25 ppt. The second group of VGs demonstrated a lower adaptation of their carriers to temperature and negative correlations with pH, salinity, potential water density, conductivity, and dissolved solids but a positive correlation with turbidity. No such trends were observed in sediments. These data reveal the role of VGs in the adaptability of the Harveyi clade pathogens to environmental parameters, causing their diversification and possibly their stratification into different ecological niches due to changes in water temperature, acidity, salinity, and turbidity. This diversification and stratification may lead to further speciation and the emergence of new pathogens of this clade. Our data urge further monitoring of the presence and diversification of Harveyi clade pathogens in a global warming scenario. Full article
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<p>A map of the three study sites. Each site was in direct proximity to cultured clam and wild oyster beds. The sites were selected for better coverage of the study area.</p>
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<p>Average VG copy numbers in water. To better reflect their presence and concentrations within the study area, concentrations of each VG observed in sites 1–3 were averaged for every sampling event. Variations in average gene concentrations among sites were evaluated with Welch’s ANOVA, which revealed no statistically significant differences. The values for error bars were too low to be presented in logarithmic-scale figures, so these are not included.</p>
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<p>Average VG copy numbers in sediments. To better reflect their presence and concentrations within the study area, concentrations of each VG observed in sites 1–3 were averaged for every sampling event. Variations in average gene concentrations among sites were evaluated with Welch’s ANOVA, which revealed no statistically significant differences. The values for error bars were too low to be presented in logarithmic-scale figures, so these are not included.</p>
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<p>Impact of temperature on the presence of E ((<b>A</b>), top panel) and P ((<b>B</b>), bottom panel) VG groups. The dots in these figures represent averaged values for E- and P-groups of genes and water parameters for each sampling event. Since both values were averaged for sites 1–3 and the logarithmic Y scale was used to accommodate the concentration range, no error bars are included.</p>
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<p>Impact of pH on the presence of E ((<b>A</b>), top panel) and P ((<b>B</b>), bottom panel) VG groups. The dots in these figures represent averaged values for E- and P-groups of genes and water parameters for each sampling event. Since both values were averaged for sites 1–3 and the logarithmic Y scale was used to accommodate the concentration range, no error bars are included.</p>
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<p>Impact of salinity on the presence of E ((<b>A</b>), top panel) and P ((<b>B</b>), bottom panel) groups. The dots in these figures represent averaged values for E- and P-groups of genes and water parameters for each sampling event. Since both values were averaged for sites 1–3 and the logarithmic Y scale was used to accommodate the concentration range, no error bars are included.</p>
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15 pages, 4782 KiB  
Article
Porcine Nose Atrophy Assessed by Automatic Imaging and Detection of Bordetella bronchiseptica and Other Respiratory Pathogens in Lung and Nose
by Hanna Lichterfeld, Sara Trittmacher, Kathrin Gerdes, Kathrin Schmies, Joaquín Miguel, Irene Galé, Alba Puigredon Fontanet, Isaac Ballarà, Krista Marie Tenbrink and Isabel Hennig-Pauka
Animals 2024, 14(21), 3113; https://doi.org/10.3390/ani14213113 - 29 Oct 2024
Viewed by 725
Abstract
The nasal mucosa is a crucial filtering organ to prevent attachment and invasion of pathogens. To assess nasal health in relation to lung health, transverse cross sections of the nasal turbinates of 121 pigs suffering from respiratory disease and sent for diagnostic necropsy [...] Read more.
The nasal mucosa is a crucial filtering organ to prevent attachment and invasion of pathogens. To assess nasal health in relation to lung health, transverse cross sections of the nasal turbinates of 121 pigs suffering from respiratory disease and sent for diagnostic necropsy were scored visually and by an artificial intelligence (AI) medical diagnostic application (AI DIAGNOS), resulting in a high correlation of both scores (p < 0.001). Nasal samples of the diseased pigs were examined only for Bordetella (B.) bronchiseptica (PCR and bacteriological culture) and Pasteurella (P.) multocida (bacteriological culture). All pigs showed various degrees of inflammatory lung tissue alterations, and 35.5% of the pigs had atrophy of the nasal turbinates with no relation to detection rates of B. bronchiseptica (54.5%) and P. multocida (29.0%) in the nose. All P. multocida strains from nose samples were negative for the toxA gene so non-progressive atrophic rhinitis was diagnosed. Pigs positive for B. bronchiseptica in the nose were more often positive for B. bronchiseptica in the lung (p < 0.001) and for other bacterial species in the lower respiratory tract (p = 0.005). The new diagnostic application for scoring cross sections of nasal turbinates is a valuable tool for a fast and reproducible diagnostic. Full article
(This article belongs to the Section Pigs)
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<p>(<b>a</b>) NLS 0; (<b>b</b>) NLS 1.</p>
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<p>(<b>a</b>) NLS 4; (<b>b</b>) NLS 3.</p>
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<p>(<b>a</b>) NLS 8; (<b>b</b>) NLS 2.</p>
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<p>(<b>a</b>) NLS 12; (<b>b</b>) NLS 12.</p>
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<p>(<b>a</b>) NLS 12; (<b>b</b>) NLS 9.</p>
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<p>Prevalences of different pathogens in the lower respiratory tract (<span class="html-italic">n</span> = 121); numbers above the bars represent the number of pigs positive for the respective pathogen. The shaded grey parts of the bars depict the proportion of pigs simultaneously positive for <span class="html-italic">B. bronchiseptica</span> in the lung.</p>
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<p>Number of pathogenic bacterial species in the lower respiratory tract in pigs positive and negative for <span class="html-italic">B. bronchiseptica</span> in the nose by PCR (<span class="html-italic">n</span> = 121).</p>
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<p>Detection of <span class="html-italic">B. bronchiseptica</span> in the nose and the lower respiratory tract (<span class="html-italic">n</span> = 121).</p>
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<p>Association between <span class="html-italic">B. bronchiseptica</span> and severity of nasal turbinate lesions (<span class="html-italic">n</span> = 121); blue (negative for <span class="html-italic">B. bronchiseptica</span> in nose) and orange boxes (positive for <span class="html-italic">B. bronchiseptica</span> in nose) represent the interquartile data of the nasal lesion score (50% between 25% and 75% quartiles). The lines inside the boxes indicate the median. The upper and lower fences are defined as first and third quartile (represented by the lower/upper edge of the box) with minus/plus 1.5 times the interquartile range (IQR) indicating outliers (dots outside the boxes).</p>
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<p>Correlation between visual and automated nasal lesion scores (<span class="html-italic">n</span> = 121).</p>
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24 pages, 3514 KiB  
Article
Design, Synthesis, Molecular Docking, and ADME-Tox Investigations of Imidazo[1,2-a]Pyrimidines Derivatives as Antimicrobial Agents
by Djamila Benzenine, Ismail Daoud, Nadia Aissaoui, Zahira Kibou, Julio A. Seijas, M. Pilar Vázquez-Tato, Chewki Ziani-Cherif, Lahcen Belarbi and Noureddine Choukchou-Braham
Molecules 2024, 29(21), 5058; https://doi.org/10.3390/molecules29215058 - 26 Oct 2024
Viewed by 871
Abstract
A convenient and effective synthesis of imidazo[1,2-a]pyrimidine derivatives has been developed under microwave irradiations using Al2O3 as a catalyst in solvent-free conditions. The functionalized imidazo[1,2-a]pyrimidine derivatives are useful in biochemistry and medical science. In our investigation, [...] Read more.
A convenient and effective synthesis of imidazo[1,2-a]pyrimidine derivatives has been developed under microwave irradiations using Al2O3 as a catalyst in solvent-free conditions. The functionalized imidazo[1,2-a]pyrimidine derivatives are useful in biochemistry and medical science. In our investigation, the antimicrobial activity of the synthesized compounds was evaluated against 13 microorganisms, including 6 Gram-positive bacteria, 4 Gram-negative bacteria, and 3 pathogenic fungi. Bioactivity tests revealed that the majority of the compounds exhibited good antimicrobial activity. Finally, molecular docking simulations and ADME-T predictions were performed, showing that the most active compounds have good binding modes with microbial targets and promising pharmacokinetic safety profiles. Full article
(This article belongs to the Section Chemical Biology)
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<p>Pharmacologically active drugs holding imidazo[1,2-<span class="html-italic">a</span>]pyrimidine scaffolds.</p>
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<p>The 2D diagrams of the interaction between <b>3g</b> and <span class="html-italic">S. aureus</span> (PDB ID:4URM); <b>3k</b> and <span class="html-italic">S. aureus</span> (PDB ID:4URM); <b>3k</b> and <span class="html-italic">E. coli</span> (PDB ID:3FV5); <b>3j</b> and <span class="html-italic">B. cereus</span> (PDB ID:3DUW); <b>3g</b> and <span class="html-italic">B. subtilis</span> (PDB ID:2RHL); <b>3j</b> and <span class="html-italic">M. luteus</span> (PDB ID:3AQC); and <b>3k</b> and <span class="html-italic">C. albicans</span> (PDB ID:3Q70).</p>
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<p>The 2D diagrams of the interaction between XAM and <span class="html-italic">S. aureus</span> (PDB ID:4URM); 1EU and <span class="html-italic">E. coli</span> (PDB ID:3FV5); SAH and <span class="html-italic">B. cereus</span> (PDB ID:3DUW); GDP and <span class="html-italic">B. subtilis</span> (PDB ID:2RHL); 2DE and <span class="html-italic">M. luteus</span> (PDB ID:3AQC); and RIT and <span class="html-italic">C. albicans</span> (PDB ID:3Q70)<b>.</b></p>
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<p>The 3D diagrams of the interaction between <b>3g</b> and <span class="html-italic">S. aureus</span> (PDB ID:4URM); <b>3k</b> and <span class="html-italic">S. aureus</span> (PDB ID:4URM); <b>3k</b> and <span class="html-italic">E. coli</span> (PDB ID:3FV5); <b>3j</b> and <span class="html-italic">B. cereus</span> (PDB ID:3DUW); <b>3g</b> and <span class="html-italic">B. subtilis</span> (PDB ID:2RHL); <b>3j</b> and <span class="html-italic">M. luteus</span> (PDB ID:3AQC); and <b>3k</b> and <span class="html-italic">C. albicans</span> (PDB ID: 3Q70).</p>
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<p>Synthesis of imidazo[1,2-<span class="html-italic">a</span>]pyrimidine derivatives <b>3a</b>–<b>k.</b></p>
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20 pages, 906 KiB  
Article
Unveiling the Full Protein Effectorome of the Black Sigatoka Pathogen Pseudocercospora fijiensis—An In Silico Approach
by Karla Gisel Carreón-Anguiano, Jewel Nicole Anna Todd, César De los Santos-Briones, Santy Peraza-Echeverría, Ignacio Islas-Flores and Blondy Canto-Canché
Microbiol. Res. 2024, 15(3), 1880-1899; https://doi.org/10.3390/microbiolres15030126 - 14 Sep 2024
Viewed by 1053
Abstract
Pseudocercospora (previously Mycosphaerella) fijiensis is a hemibiotroph fungus and the causal agent of black Sigatoka disease, one of the most significant threats to banana production worldwide. Only a few genomics reports have paid any attention to effector proteins, which are key players [...] Read more.
Pseudocercospora (previously Mycosphaerella) fijiensis is a hemibiotroph fungus and the causal agent of black Sigatoka disease, one of the most significant threats to banana production worldwide. Only a few genomics reports have paid any attention to effector proteins, which are key players in pathogenicity. These reports focus on canonical effectors: small secreted proteins, rich in cysteines, containing a signal peptide and no transmembrane domain. Thus, bias in previous reports has resulted in the non-canonical effectors being, in effect, excluded from the discussion of effectors in P. fijiensis pathogenicity. Here, using WideEffHunter and EffHunter, bioinformatic tools which identify non-canonical and canonical effectors, respectively, we predict, for the first time, the full effectorome of P. fijiensis. This complete effectorome comprises 5179 proteins: 240 canonical and 4939 non-canonical effectors. Protein families related to key functions of the hemibiotrophic lifestyle, such as Salicylate hydroxylase and Isochorismatase, are widely represented families of effectors in the P. fijiensis genome. An analysis of the gene distribution in core and dispensable scaffolds of both classes of effectors revealed a novel genomic structure of the effectorome. The majority of the effectors (canonical and non-canonical) were found to be harbored in the core scaffolds, while dispensable scaffolds harbored less than 10% of the effectors, all of which were non-canonical. Additionally, we found the motifs RXLR, YFWxC, LysM, EAR, [Li]xAR, PDI, CRN, and ToxA in the effectors of P. fijiensis. This novel genomic structure of effectors (more enriched in the core than in the dispensable genome), as well as the occurrence of effector motifs which were also observed in four other fungi, evidences that these phenomena are not unique to P. fijiensis; rather, they are widely occurring characteristics of effectors in other fungi. Full article
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<p>Protein motifs found in the effectorome of <span class="html-italic">P. fijiensis</span>: (<b>A</b>) abundance of the protein motifs in the effectors of <span class="html-italic">P. fijiensis</span>. And (<b>B</b>) distribution of the <span class="html-italic">P. fijiensis</span> effector-motifs throughout the genomic scaffolds.</p>
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<p>Protein motifs found in the effectorome of <span class="html-italic">P. fijiensis</span>: (<b>A</b>) abundance of the protein motifs in the effectors of <span class="html-italic">P. fijiensis</span>. And (<b>B</b>) distribution of the <span class="html-italic">P. fijiensis</span> effector-motifs throughout the genomic scaffolds.</p>
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24 pages, 1956 KiB  
Article
Development of Novel Alaninamide Derivatives with Anticonvulsant Activity and Favorable Safety Profiles in Animal Models
by Michał Abram, Marcin Jakubiec, Paulina Koczurkiewicz-Adamczyk, Agata Doroz-Płonka, Anna Rapacz and Krzysztof Kamiński
Int. J. Mol. Sci. 2024, 25(18), 9861; https://doi.org/10.3390/ijms25189861 - 12 Sep 2024
Viewed by 726
Abstract
In our current study, we developed a focused series of original ((benzyloxy)benzyl)propanamide derivatives that demonstrated potent activity across in vivo mouse seizure models, specifically, maximal electroshock (MES) and 6 Hz (32 mA) seizures. Among these derivatives, compound 5 emerged as a lead molecule, [...] Read more.
In our current study, we developed a focused series of original ((benzyloxy)benzyl)propanamide derivatives that demonstrated potent activity across in vivo mouse seizure models, specifically, maximal electroshock (MES) and 6 Hz (32 mA) seizures. Among these derivatives, compound 5 emerged as a lead molecule, exhibiting robust protection following intraperitoneal (i.p.) injection, as follows: ED50 = 48.0 mg/kg in the MES test, ED50 = 45.2 mg/kg in the 6 Hz (32 mA) test, and ED50 = 201.3 mg/kg in the 6 Hz (44 mA) model. Additionally, compound 5 displayed low potential for inducing motor impairment in the rotarod test (TD50 > 300 mg/kg), indicating a potentially favorable therapeutic window. In vitro toxicity assays further supported its promising safety profile. We also attempted to identify a plausible mechanism of action of compound 5 by applying both binding and functional in vitro studies. Overall, the data obtained for this lead molecule justifies the more comprehensive preclinical development of compound 5 as a candidate for a potentially broad-spectrum and safe anticonvulsant. Full article
(This article belongs to the Special Issue Molecular Research in Epilepsy and Epileptogenesis)
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<p>Lead compound (<b><span class="html-italic">R</span></b>)<b>-AS-1</b>, [<a href="#B11-ijms-25-09861" class="html-bibr">11</a>] and design approach of hybrid compounds obtained in the current studies. The common structural elements forming the new hybrids are highlighted in color.</p>
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<p>The viability of HepG2 cells incubated in the presence of <b>5</b>, <b>17</b>, and <b>39</b>. HepG2 cells were exposed to growing concentrations (1–50 µM) of tested compounds for 24 h. Cell viability was measured using an MTT assay. Bars represent mean percent of cell viability normalized to non-treated cells (100%) ± SEM. The doxorubicin was tested as a positive control.</p>
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<p>The viability of SH-SY5Y cells incubated in the presence of compounds <b>5</b>, <b>17</b>, and <b>39</b>. SH-SY5Y cells were exposed to growing concentrations (1–50 µM) of tested compounds for 24 h. Cell viability was measured using an MTT assay. Bars represent mean percent of cells viability normalized to non-treated cells (100%) ± SEM. The doxorubicin was tested as a positive control.</p>
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<p>Synthesis of intermediates <b>1</b>–<b>4</b> and target compounds <b>5</b>–<b>23</b>.</p>
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<p>Synthesis of intermediates <b>24</b>–<b>35</b> and target pyrrolidin-2-on analogs <b>36</b>–<b>39</b>.</p>
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