Outcome of tailored therapy in rheumatic heart disease with persistent atrial fibrillation (RHD-AF).

INTRODUCTION: Rheumatic heart disease with persistent atrial fibrillation (RHD-AF) is associated with increased morbidity. However, there is no standardized approach for the maintenance of sinus rhythm (SR) in them. We aimed to determine the utility of a stepwise approach to achieve SR in RHD-AF. METHODS: Consecutive patients with RHD-AF from July 2021 to August 2023 formed the study cohort. The stepwise approach included pharmacological rhythm control and/or electrical cardioversion (Central illustration). In patients with recurrence, additional options included AF ablation or pace and ablate strategy with conduction system pacing or biventricular pacing. Clinical improvement, NT-proBNP, 6-Minute Walk Test (6MWT), heart failure (HF) hospitalizations, and thromboembolic complications were documented during follow-up. RESULTS: Eighty-three patients with RHD-AF (mean age 56.13 ± 9.51 years, women 72.28%) were included. Utilizing this approach, 43 (51.81%) achieved and maintained SR during the study period of 11.04 ± 7.14 months. These patients had improved functional class, lower NT-proBNP, better distance covered for 6MWT, and reduced HF hospitalizations. The duration of AF was shorter in patients who achieved SR, compared to those who remained in AF (3.15 ± 1.29 vs 6.93 ± 5.23, p = 0.041). Thirty-five percent (29) maintained SR after a single cardioversion over the study period. Only one underwent AF ablation. Of the 24 who underwent pace and ablate strategy, atrial lead was implanted in 22 (hybrid approach), and 50% of these achieved and maintained SR. Among these 24, none had HF hospitalizations, but patients who maintained SR had further improvement in clinical and functional parameters. CONCLUSIONS: RHD-AF patients who could achieve SR with a stepwise approach, had better clinical outcomes and lower HF hospitalizations.

Biomass Derived Biofluorescent Carbon Dots for Energy Applications: Current Progress and Prospects.

Biomass resources are often disposed of inefficiently and it causes environmental degradation. These wastes can be turned into bio-products using effective conversion techniques. The synthesis of high-value bio-products from biomass adheres to the principles of a sustainable circular economy in a variety of industries, including agriculture. Recently, fluorescent carbon dots (C-dots) derived from biowastes have emerged as a breakthrough in the field, showcasing outstanding fluorescence properties and biocompatibility. The C-dots exhibit unique quantum confinement properties due to their small size, contributing to their exceptional fluorescence. The significance of their fluorescent properties lies in their versatile applications, particularly in bio-imaging and energy devices. Their rapid and straight-forward production using green/chemical precursors has further accelerated their adoption in diverse applications. The use of green precursors for C-dot not only addresses the biomass disposal issue through a scientific approach, but also establishes a path for a circular economy. This approach not only minimizes biowaste, which also harnesses the potential of fluorescent C-dots to contribute to sustainable practices in agriculture. This review explores recent developments and challenges in synthesizing high-quality C-dots from agro-residues, shedding light on their crucial role in advancing technologies for a cleaner and more sustainable future.

Involvement of ObRb receptor, nitric oxide, and BKCa channel signaling pathways in leptin-induced relaxation of pregnant mouse uterus.

The purpose of this study was to determine the receptor subtype and the underlying mechanisms involved in the relaxant effect to leptin in mid- and late-pregnant mouse uterus. We determined the relative mRNA expression of receptor subtypes, eNOS, and BKCa channel by quantitative PCR and also the overall receptor expression by immunohistochemistry. Isometric tension studies were conducted to evaluate the effects of leptin and to delineate its mechanisms. A selective siRNA for the ObRb receptor was used to determine the participation of the receptor subtype in biochemical and molecular effects of leptin. The relaxant response to leptin was greater in mid-pregnancy compared to late pregnancy and was mediated by the activation of BKCa channels by eNOS-derived nitric oxide in an ObRb receptor-dependent manner. In comparison to mid-pregnancy, expression of short forms (mainly ObRa receptor) of the receptor was significantly increased in late pregnancy, whereas ObRb receptor expression was similar in both phases. The results of the study suggest that ObRb receptor mediates leptin-induced increase in eNOS expression and NO synthesis. Leptin-induced eNOS expression and activation cause cGMP-independent stimulation of BKCa channels causing uterine relaxation. Increased short forms of the receptors and reduced BKCa channels exert a negative effect on uterine relaxation in late pregnancy. Leptin may have a physiological role in maintaining uterine quiescence in mid-pregnancy and its reduced relaxant response in late gestation may facilitate labor. Further, ObRb receptor agonists may be useful in the management of preterm labor.

Assessing the Impact of AI-Enhanced Diagnostic Tools on the Treatment Planning of Orthodontic Cases: An RCT.

Background: Orthodontic treatment planning involves the precise assessment of dental and skeletal anomalies, which can be facilitated by AI-enhanced diagnostic tools. Materials and Methods: A total of 100 orthodontic cases were included in this RCT. Patients were randomly assigned to two groups: an AI-enhanced diagnostic group and a traditional diagnostic group. The AI-enhanced diagnostic group underwent orthodontic assessment with the aid of AI-powered software, which provided automated cephalometric analysis, 3D model evaluations, and treatment suggestions. The traditional diagnostic group received conventional diagnostic assessments by orthodontists. The primary outcome measures included treatment planning accuracy, treatment time, and patient satisfaction. Secondary outcomes included the number of appointments required and treatment cost. Results: The AI-enhanced diagnostic group demonstrated a significantly higher accuracy in treatment planning compared to the traditional diagnostic group (P < 0.05). The AI group also required fewer appointments (mean ± SD: 10.2 ± 2.1 vs. 12.8 ± 3.4) and had a shorter treatment time (mean ± SD: 14.6 ± 3.2 months vs. 18.9 ± 4.5 months) (P < 0.001 for both comparisons). Additionally, patient satisfaction scores were higher in the AI group (mean ± SD: 9.2 ± 0.6 vs. 8.1 ± 0.8) (P < 0.001). However, the AI-enhanced diagnostic group had a slightly higher treatment cost. Conclusion: AI-enhanced diagnostic tools significantly enhance the accuracy of treatment planning in orthodontic cases, leading to reduced treatment time, fewer appointments, and increased patient satisfaction.

Cryopreservation alters buffalo sperm kinematics and mitochondrial parameters, acrosome and intra-cellular calcium status.

BACKGROUND: Little is known about the effects of different seasons on the cryopreservation success of buffalo sperm in terms of kinematics and sperm functional parameters. OBJECTIVE: To study the effect of three seasons (winter, comfort and summer) and cryopreservation on sperm kinematics and functional properties in buffalo bulls. MATERIALS AND METHODS: Semen ejaculates (n = 90) collected during three seasons i.e. winter (n = 30), comfort (n = 30), summer (n = 30) were evaluated for sperm kinematics and functional properties. RESULTS: Sperm kinematics with respect to total (TM), progressive (PM) and rapid motility (RM) was higher (P < 0.05) in fresh sperm compared to sperm that had been frozen-thawed. Similarly, all kinematic parameters [viz. average path velocity (VAP), straight linear velocity (VSL), curvilinear velocity (VCL), beats cross frequency (BCF), lateral head displacement (ALH), linearity (LIN) and straightness (STR)] were higher (P < 0.01) at the fresh stage. With respect to season, frozen-thawed semen TM (57.67 ± 115 %), PM (50.2 ± 1.15 %) and RM (51.6 ± 1.19 %) were higher (P < 0.01) when using sperm collected during winter. The stage of cryopreservation (i.e., equilibration and freeze-thawing) also showed significant effects (P < 0.01) on mitochondrial superoxide positive status (MSPS), mitochondrial membrane potential (MMP), acrosome status and intra-cellular calcium status. CONCLUSION: The season of sperm collection and cryopreservation have significant effects on buffalo bull sperm kinematics and functional properties. Doi.org/10.54680/fr24410110612.

"Heyde's enigma" in a patient with congenital annular aortic stenosis and its therapeutic challenges.

Heyde's syndrome is described as angio-dysplastic gastrointestinal (GI) bleeding in elderly patients with degenerative severe calcific aortic stenosis (AS), resulting in anaemia. It was first reported by Edward C. Heyde in 1958 and thus carried his name. Although this condition is considered to develop in 10-20% of severe AS, it is a less familiar entity in clinical practice. With the rising geriatric population in the communities, there is a proportionate increase in the incidence of AS and accompanying Heyde's syndrome. Heyde's syndrome has also been associated with hypertrophic cardiomyopathy, left ventricular assist device, ventricular septal defect, and patent ductus arteriosus. This article reports a case of Heyde's syndrome associated with congenital annular AS, successfully treated by aortic root enlargement and valve replacement. Supplementary Information: The online version contains supplementary material available at 10.1007/s12055-023-01636-y.

Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells.

The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates. BACKGROUND: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol. OBJECTIVES: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA. METHODS: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking Studies (MDS) were carried out for the above-mentioned enzymes. RESULTS: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results. CONCLUSION: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

Relationship of emotional intelligence and capability of answering higher-order knowledge questions in physiology among first-year medical students.

Emotional intelligence (EI) has a positive correlation with the academic performance of medical students. However, why there is a positive correlation needs further exploration. We hypothesized that the capability of answering higher-order knowledge questions (HOQs) is higher in students with higher EI. Hence, we assessed the correlation between EI and the capability of medical students to answer HOQs in physiology. First-year undergraduate medical students (n = 124) from an Indian medical college were recruited as a convenient sample. EI was assessed by the Schutte Self-Report Emotional Intelligence Test (SSEIT), a 33-item self-administered validated questionnaire. A specially designed objective examination with 15 lower-order and 15 higher-order multiple-choice questions was conducted. The correlation between the examination score and the EI score was tested by Pearson's correlation coefficient. Data from 92 students (33 females and 59 males) with a mean age of 20.14 ± 1.87 yr were analyzed. Overall, students got a percentage of 53.37 ± 14.07 in the examination, with 24.46 ± 9.1 in HOQs and 28.91 ± 6.58 in lower-order knowledge questions (LOQs). They had a mean score of 109.58 ± 46.2 in SSEIT. The correlation coefficient of SSEIT score with total marks was r = 0.29 (P = 0.0037), with HOQs was r = 0.41 (P < 0.0001), and with LOQs was r = 0.14 (P = 0.19). Hence, there is a positive correlation between EI and the capability of medical students to answer HOQs in physiology. This study may be the foundation for further exploration of the capability of answering HOQs in other subjects.NEW & NOTEWORTHY This study assessed the correlation between emotional intelligence (EI) and the capability of medical students to answer higher-order knowledge questions (HOQs) in the specific context of physiology. The finding reveals one of the multifaceted dimensions of the relationship between EI and academic performance. This novel perspective opens the door to further investigations to explore the relationship in other subjects and other dimensions to understand why students with higher EI have higher academic performance.

Hypertension treatment cascade among men and women of reproductive age group in India: analysis of National Family Health Survey-5 (2019-2021).

Background: Only a proportion of adults with hypertension are diagnosed and receive recommended prescriptions despite the availability of inexpensive and efficacious treatment. We aimed to estimate the prevalence of different stages of hypertension treatment cascade among the reproductive age groups in India at the national and state levels. We also identified the predictors of different stages of the hypertension treatment cascade. Methods: We used the nationally representative data from National Family Health Survey (NFHS)-5. We included all the males (15-54 years) and females aged 15-49. Socio-demographic factors, anthropometric measurements, habits, comorbid conditions, and healthcare access stratified the stages of the hypertension treatment cascade among hypertensives. We used multinomial logistic regression to identify the determinants of the treatment cascade levels. Findings: We had data from 1,267,786 individuals. The national prevalence of hypertension was 18.3% (95% CI: 18.1%-18.4%). Men (21.6%, 95% CI: 21.5%-21.7%) were found to have a higher prevalence as compared to women (14.8%, 95% CI: 14.7%-14.9%). Among hypertensive individuals, 70.5% (95% CI: 70.3%-70.7%) had ever received a BP measurement ("screened"), 34.3% (95% CI: 34.1%-34.5%) had been diagnosed prior to the survey ("aware"), 13.7% (95% CI: 13.5%-13.8%) reported taking a prescribed anti-hypertensive drug ("under treatment"), and 7.8% (95% CI: 7.7%-7.9%) had their BP under control ("controlled"). Males, illiterates, poor, never married, residents of rural areas, smokers/tobacco users, and alcoholic users were less likely to be in any of the treatment cascades. Interpretation: The prevalence of hypertension in India is high. The "Rule of half" of hypertension does not apply to India as the proportion of people screened, aware of their hypertension status, treated, and controlled are lower than 50% at each stage. Program managers must improve access to hypertension diagnosis and treatment, especially among men in rural areas and populations with lower household wealth. Funding: None.

Interactions between the protein barnase and co-solutes studied by NMR.

Protein solubility and stability depend on the co-solutes present. There is little theoretical basis for selection of suitable co-solutes. Some guidance is provided by the Hofmeister series, an empirical ordering of anions according to their effect on solubility and stability; and by osmolytes, which are small organic molecules produced by cells to allow them to function in stressful environments. Here, NMR titrations of the protein barnase with Hofmeister anions and osmolytes are used to measure and locate binding, and thus to separate binding and bulk solvent effects. We describe a rationalisation of Hofmeister (and inverse Hofmeister) effects, which is similar to the traditional chaotrope/kosmotrope idea but based on solvent fluctuation rather than water withdrawal, and characterise how co-solutes affect protein stability and solubility, based on solvent fluctuations. This provides a coherent explanation for solute effects, and points towards a more rational basis for choice of excipients.

Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios.

The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoost) for simulating streamflow in the Lower Godavari Basin, India. Monthly rainfall, temperatures, and streamflow from 1982 to 2020 were used for training and testing. Kling Gupta Efficiency (KGE) was deployed to assess the ability of the boosting algorithms. It was observed that all the boosting algorithms had shown good simulating ability, having KGE values of AdaBoost (0.87, 0.85), CatBoost (0.90, 0.78), LGBoost (0.95, 0.93), NGBoost (0.95, 0.95), and XGBoost (0.91, 0.90), respectively, in training and testing. Thus, all the algorithms were used for projecting streamflow in a climate change perspective for the short-term projections (2025-2050) and long-term projections (2051-2075) for four Shared Socioeconomic Pathways (SSPs). The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3/s), whereas vice-versa for the remaining four. The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual algorithms.

A destabilizing Y891D mutation in activated EGFR impairs sensitivity to kinase inhibition.

EGFR tyrosine kinase inhibitors (TKIs) have transformed the treatment of EGFR-mutated non-small cell lung carcinoma (NSCLC); however, therapeutic resistance remains a clinical challenge. Acquired secondary EGFR mutations that increase ATP affinity and/or impair inhibitor binding are well-described mediators of resistance. Here we identify a de novo EGFR Y891D secondary alteration in a NSCLC with EGFR L858R. Acquired EGFR Y891D alterations were previously reported in association with resistance to first generation EGFR TKIs. Functional studies in Ba/F3 cells demonstrate reduced TKI sensitivity of EGFR L858R + Y891D, with the greatest reduction observed for first and second generation TKIs. Unlike other EGFR mutations associated with TKI resistance, Y891D does not significantly alter ATP affinity or promote steric hindrance to inhibitor binding. Our data suggest that the Y891D mutation destabilizes EGFR L858R, potentially generating a population of misfolded receptor with preserved signaling capacity but reduced sensitivity to EGFR inhibitors. These findings raise the possibility of protein misfolding as a mechanism of resistance to EGFR inhibition in EGFR-mutated NSCLC.

Genome-wide selection signatures address trait specific candidate genes in cattle indigenous to arid regions of India.

The peculiarity of Indian cattle lies in milk quality, resistance to diseases and stressors as well as adaptability. The investigation addressed selection signatures in Gir and Tharparkar cattle, belonging to arid ecotypes of India. Double digest restriction-site associated DNA sequencing (ddRAD-seq) yielded nearly 26 million high-quality reads from unrelated seven Gir and seven Tharparkar cows. In all, 19,127 high-quality SNPs were processed for selection signature analysis. An approach involving within-population composite likelihood ratio (CLR) statistics and between-population FST statistics was used to capture selection signatures within and between the breeds, respectively. A total of 191 selection signatures were addressed using CLR and FST approaches. Selection signatures overlapping 86 and 73 genes were detected as Gir- and Tharparkar-specific, respectively. Notably, genes related to production (CACNA1D, GHRHR), reproduction (ESR1, RBMS3), immunity (NOSTRIN, IL12B) and adaptation (ADAM22, ASL) were annotated to selection signatures. Gene pathway analysis revealed genes in insulin/IGF pathway for milk production, gonadotropin releasing hormone pathway for reproduction, Wnt signalling pathway and chemokine and cytokine signalling pathway for adaptation. This is the first study where selection signatures are identified using ddRAD-seq in indicine cattle breeds. The study shall help in conservation and leveraging genetic improvements in Gir and Tharparkar cattle.

Distinct interactions stabilize EGFR dimers and higher-order oligomers in cell membranes.

The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase with important roles in many cellular processes as well as in cancer and other diseases. EGF binding promotes EGFR dimerization and autophosphorylation through interactions that are well understood structurally. How these dimers relate to higher-order EGFR oligomers seen in cell membranes, however, remains unclear. Here, we used single-particle tracking (SPT) and Förster resonance energy transfer imaging to examine how each domain of EGFR contributes to receptor oligomerization and the rate of receptor diffusion in the cell membrane. Although the extracellular region of EGFR is sufficient to drive receptor dimerization, we find that the EGF-induced EGFR slowdown seen by SPT requires higher-order oligomerization-mediated in part by the intracellular tyrosine kinase domain when it adopts an active conformation. Our data thus provide important insight into the interactions required for higher-order EGFR assemblies involved in EGF signaling.

Benzothiazole derivatives as p53-MDM2 inhibitors: in-silico design, ADMET predictions, molecular docking, MM-GBSA Assay, MD simulations studies.

Breast cancer stands as the most prevalent malignancy among the female populace. One of the pivotal domains in the therapeutic landscape of breast cancer revolves around the precise targeting of the p53-MDM2 inhibitory pathway. The advent of p53-MDM2 inhibition in the context of developing treatments for breast cancer marks a significant stride. In the quest for enhancing the efficacy of p53-MDM2 inhibition against breast cancer, a new series of benzothiazole compounds (B1-B30) was designed through in-silico methodologies in the present work. Using Schrodinger Maestro, the compounds underwent molecular docking assessments against the p53-MDM2 target (PDB: 4OGT). Compared to reference compounds, B25 and B12 exhibited notably elevated glide scores. Extensive in-silico studies, including ADMET and toxicity evaluations, were performed to predict pharmacokinetics, drug likeness, and toxicity. All compounds adhered to Lipinski criteria, signifying favorable oral drug properties. The MM-GBSA analysis indicated consistent binding free energies. Molecular dynamics simulations for B25 over 200 ns assessed complex stability and interactions. In summary, these compounds exhibit potential for future cancer therapy medication development.Communicated by Ramaswamy H. Sarma.

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol.

AIMS: To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury. BACKGROUND: Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects. OBJECTIVE: To evaluate the role of machine learning algorithms (MLAs) and bioinformatics tools to delineate the predictor SNPs as well as to understand their molecular dynamics. METHODS: A cross-sectional study was undertaken by recruiting critically ill patients with chronic renal failure and administering intravenous paracetamol as a standard of care. Serum concentrations of paracetamol and the principal metabolites were estimated. Following SNPs were evaluated: CYP2E1*2, CYP2E1_-1295G>C, CYP2D6*10, CYP3A4*1B, CYP3A4*2, CYP1A2*1K, CYP1A2*6, CYP3A4*3, and CYP3A5*7. MLAs were used to identify the predictor genetic variable for acute liver failure. Bioinformatics tools such as Predict SNP2 and molecular docking (MD) were undertaken to evaluate the impact of the above SNPs with binding affinity to paracetamol. RESULTS: CYP2E1*2 and CYP1A2*1C genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that CYP1A2*3 was highly deleterious in all the tools. MD revealed binding energy of -5.5 Kcal/mol, -6.9 Kcal/mol, and -6.8 Kcal/mol for CYP1A2, CYP1A2*3, and CYP1A2*6 against paracetamol. MD simulations revealed that CYP1A2*3 and CYP1A2*6 missense variants in CYP1A2 affect the binding ability with paracetamol. In-silico techniques found that CYP1A2*2 and CYP1A2*6 are highly harmful. MD simulations revealed CYP3A4*2 (A>G) had decreased binding energy with paracetamol than CYP3A4, and CYP3A4*2(A>T) and CYP3A4*3 both have greater binding energy with paracetamol. CONCLUSION: Polymorphisms in CYP2E1, CYP1A2, CYP3A4, and CYP3A5 significantly influence paracetamol's clinical outcomes or binding affinity. Robust clinical studies are needed to identify these polymorphisms' clinical impact on the pharmacokinetics or pharmacodynamics of paracetamol.