Sulfonyl thiourea derivatives from 2-aminodiarylpyrimidines: In vitro and in silico evaluation as potential carbonic anhydrase I, II, IX, and XII inhibitors.

A series of synthesized sulfonyl thiourea derivatives (7a-o) of substituted 2-amino-4,6-diarylpyrimidines (4a-o) exhibited the remarkable inhibitory activity against some the human carbonic anhydrases (hCAs), including hCA I, II, IX, and XII isoforms. The inhibitory efficacy of synthesized sulfonyl thiourea derivatives were experimentally validated by in vitro enzymatic assays. 7a (KI = 46.14 nM), 7j (KI = 48.92 nM), and 7m (KI = 62.59 nM) (for isoform hCA I); 7f (KI = 42.72 nM), 7i (KI = 40.98 nM), and 7j (KI = 33.40 nM) (for isoform hCA II); 7j (KI = 228.5 nM), 7m (KI = 195.4 nM), and 7n (KI = 210.1 nM) (for isoform hCA IX); 7l (KI = 116.9 nM), 7m (KI = 118.8 nM), and 7n (KI = 147.2 nM) (for isoform hCA XII) in comparison with KI values of 452.1, 327.3, 437.2, and 338.9 nM, respectively, of the standard drug AAZ. These compounds also had significantly more potent inhibitory action against cytosolic isoform hCA I and tumor-associated isoforms hCA IX and hCA XII. Furthermore, the potential inhibitory compounds were subjected to in silico screening for molecular docking and molecular dynamics simulations. The results of in vitro and in silico studies revealed that compounds 7a, 7j, and 7m were the most promising derivatives in this series due to their significant effects on studied hCA I, II, IX, and XII isoforms, respectively. The results showed that the sulfonyl thiourea moiety was accommodated deeply in the active site and interacted with the zinc ion in the receptors.

Thiourea derivatives containing 4-arylthiazoles and d-glucose moiety: design, synthesis, antimicrobial activity evaluation, and molecular docking/dynamics simulations.

Some substituted glucose-conjugated thioureas containing 1,3-thiazole ring, 4a-h, were synthesized by the reaction of the corresponding substituted 2-amino-4-phenyl-1,3-thiazoles 2a-h with 2,3,4,6-tetra-O-acetyl-ß-d-glucopyranosyl isocyanate. The antibacterial and antifungal activities of these thiazole-containing thioureas were estimated using a minimum inhibitory concentration protocol. Among these compounds, 4c, 4g, and 4h were better inhibitors with MIC = 0.78-3.125 µg mL-1. These three compounds were also tested for their ability to inhibit S. aureus enzymes, including DNA gyrase, DNA topoisomerase IV (Topo IV), and dihydrofolate reductase, and compound 4h was found to be a strong inhibitor with IC50 = 1.25 ± 0.12, 67.28 ± 1.21, and 0.13 ± 0.05 µM, respectively. Induced-fit docking and MM-GBSA calculations were performed to observe the binding efficiencies and steric interactions of these compounds. The obtained results showed that compound 4h is compatible with the active site of S. aureus DNA gyrase 2XCS with four H-bond interactions with residues Ala1118, Met1121, and F:DC11 and also three interactions with F:DG10 (two interactions) and F:DC11 (one interaction). Molecular dynamics simulation in a water solvent system showed that ligand 4h had active interactions with enzyme 2XCS through residues Ala1083, Glu1088, Ala1118, Gly1117, and Met1121.

Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI.

BACKGROUND: Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning. PURPOSE: To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI). STUDY TYPE: Bicentric retrospective study. SUBJECTS: In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model. SEQUENCE: A 3 T, coronal, and sagittal images from T1-weighted proton density (PD) fast spin-echo (FSE) with fat saturation and T2-weighted FSE with fat saturation sequences. ASSESSMENT: The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet-53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images. STATISTICAL TESTS: Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two-way analysis of variance, Wilcoxon signed-rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists. RESULTS: The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025). DATA CONCLUSION: The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Net (DCLU-Net). MATERIALS AND METHODS: A total of 297 participants who underwent of total of 303 MRI examination of the knee with fat-saturated proton density (PD) fast spin-echo (FSE) sequence in the sagittal plane were included. There were 214 men and 83 women, with a mean age of 37.46 ± 1.40 (standard deviation) years (range: 29-44 years). Of these, 107 participants had intact ACL (36%), 98 had partially torn ACL (33%), and 92 had fully ruptured ACL (31%). The DCLU-Net was combined with radiomic features for enhancing performances in the diagnosis of ACL tear. The different evaluation metrics for both classification (accuracy, sensitivity, accuracy) and segmentation (mean Dice similarity coefficient and root mean square error) were compared individually for each image class across the three phases of the model, with each value being compared to its respective value from the previous phase. Findings at arthroscopic knee surgery were used as the standard of reference. RESULTS: With the addition of radiomic features, the final model yielded accuracies of 90% (95% CI: 83-92), 82% (95% CI: 73-86), and 92% (95% CI: 87-94) for classifying ACL as intact, partially torn and fully ruptured, respectively. The DCLU-Net achieved mean Dice similarity coefficient and root mean square error of 0.78 (95% CI: 0.71-0.80) and 0.05 (95% CI: 0.06-0.07), respectively, when segmenting the three ACL conditions with pseudo data (P < 0.001). CONCLUSION: A dual-modules deep learning model with segmentation and classification capabilities was successfully developed. In addition, the use of semi-supervised techniques significantly reduced the amount of manual segmentation data without compromising performance.

Tolerance of Chemoorganotrophic Bioleaching Microorganisms to Heavy Metal and Alkaline Stresses.

The bioleaching potential of the bacterium Bacillus mucilaginosus and the fungus Aspergillus niger towards industrial residues was investigated by assessing their response towards various heavy metals (including arsenic, cadmium, cobalt, chromium, nickel, lead, and zinc) and elevated pH. The plate diffusion method was performed for each metal to determine the toxicity effect. Liquid batch cultures were set up for more quantitative evaluation as well as for studying the influence of basicity. Growth curves were prepared using bacterial/fungal growth counting techniques such as plate counting, optical density measurement, and dry biomass determination. Cadmium, nickel, and arsenite had a negative influence on the growth of B. mucilaginosus, whereas A. niger was sensitive to cadmium and arsenate. However, it was shown that growth recovered when microorganisms cultured in the presence of these metals were inoculated onto metal-free medium. Based on the findings of the bacteriostatic/fungistatic effect of the metals and the adaptability of the microorganisms to fairly elevated pH values, it is concluded that both strains have potential applicability for further research concerning bioleaching of alkaline waste materials.