Involvement of Autophagy and Oxidative Stress-Mediated DNA Hypomethylation in Transgenerational Nephrotoxicity Induced in Rats by the Mycotoxin Fumonisin B1

Fumonisin B1 (FB1), a mycotoxin produced by Fusarium verticillioides, is one of the most common pollutants in natural foods and agricultural crops. It can cause chronic and severe health issues in humans and animals. The aim of this study was to evaluate the transgenerational effects of FB1 exposure on the structure and function of the kidneys in offspring. Virgin female Wistar rats were randomly divided into three groups: group one (control) received sterile water, and groups two and three were intragastrically administered low (20 mg/kg) and high (50 mg/kg) doses of FB1, respectively, from day 6 of pregnancy until delivery. Our results showed that exposure to either dose of FB1 caused histopathological changes, such as atrophy, hypercellularity, hemorrhage, calcification, and a decrease in the glomerular diameter, in both the first and second generations. The levels of the antioxidant markers glutathione, glutathione S-transferase, and catalase significantly decreased, while malondialdehyde levels increased. Moreover, autophagy was induced, as immunofluorescence analysis revealed that LC-3 protein expression was significantly increased in both generations after exposure to either dose of FB1. However, a significant decrease in methyltransferase (DNMT3) protein expression was observed in the first generation in both treatment groups (20 mg/kg and 50 mg/kg), indicating a decrease in DNA methylation as a result of early-life exposure to FB1. Interestingly, global hypomethylation was also observed in the second generation in both treatment groups despite the fact that the mothers of these rats were not exposed to FB1. Thus, early-life exposure to FB1 induced nephrotoxicity in offspring of the first and second generations. The mechanisms of action underlying this transgenerational effect may include oxidative stress, autophagy, and DNA hypomethylation

RAW DATA FILE: In silico drug repurposing using molecular docking and dynamics to target the protein interaction between the SARS-CoV -2 S-glycoprotein and the ACE2 receptor

Vitrual screening project file with raw data and PDB structures of hit compounds in docked conformations</p

Beta blockers may be protective in COVID-19; findings of a study to develop an interpretable machine learning model to assess COVID-19 disease severity in light of clinical findings, medication history, and patient comorbidities

The coronavirus disease 2019 (COVID-19) has overwhelmed healthcare systems and continues to pose a significant threat worldwide. Predicting disease severity would enhance treatment provision and resource allocation. Although multiple studies were conducted to assess COVID-19's severity using machine learning (ML) models, few studies focus on patient medication history and comorbidities. In this study, ML algorithms were trained using a comprehensive dataset comprising medication history, comorbidities, and clinical findings. Patient data was gathered from King Fahad University Hospital (KFUH) in Saudi Arabia (IRB#: 2021-05-480). The dataset comprised 622 positive COVID-19 with 49 features. Three experiments were conducted to train four ML algorithms, including random forest (RF), gradient boosting machine (GMB), extreme gradient boost (XGBoost), and extra trees (ET). Findings revealed that GBM outperformed other models with 96.30% accuracy, 95.80% precision, 97.64% recall, and 96.69% F-score, with 23 features. Moreover, the permutation feature importance technique suggested that the five most influential features for forecasting disease severity were “CRP level”, “CO2 level”, “SrCr”, “Tocilizumab”, and “Age”. In addition, the shapley additive explanation (SHAP) recommended that the “D-Dimer level”, “CrCl”, and “Hypertension” were also influential. The development of an effective GBM model has the potential to aid medical specialists in the assessment of disease severity. While several models take into account patient presentation and laboratory findings, this study is unique in its scope, considering a far more comprehensive patient profile. The developed model was able to accurately predict features that have been clinically shown to correlate with disease severity. Of interest the model was able to identify a pattern of association between the use of certain medications such and disease severity. We report that the use of beta blockers may be associated with reduced severity, whereas the use of immune modulating drugs namely tocilizumab appeared to be associated with poor disease outcomes in this patient population