Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion: Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

Identifying SARS-CoV-2 Lineage Mutation Hallmarks and Correlating Them With Clinical Outcomes in Egypt: A Pilot Study.

The SARS-CoV-2 pandemic has led to over 4.9 million deaths as of October 2021. One of the main challenges of creating vaccines, treatment, or diagnostic tools for the virus is its mutations and emerging variants. A couple of variants were declared as more virulent and infectious than others. Some approaches were used as nomenclature for SARS-CoV-2 variants and lineages. One of the most used is the Pangolin nomenclature. In our study, we enrolled 35 confirmed SARS-CoV-2 patients and sequenced the viral RNA in their samples. We also aimed to highlight the hallmark mutations in the most frequent lineage. We identified a seven-mutation signature for the SARS-CoV-2 C36 lineage, detected in 56 countries and an emerging lineage in Egypt. In addition, we identified one mutation which was highly negatively correlated with the lineage. On the other hand, we found no significant correlation between our clinical outcomes and the C36 lineage. In conclusion, the C36 lineage is an emerging SARS-CoV-2 variant that needs more investigation regarding its clinical outcomes compared to other strains. Our study paves the way for easier diagnosis of variants of concern using mutation signatures.

Removal of 3C protease from the 3ABC improves expression, solubility, and purification of the recombinant 3AB of foot-and-mouth disease virus.

During an ongoing outbreak of Foot-and-Mouth Disease Virus (FMDV), it is crucial to distinguish naturally infected from vaccinated seropositive animals. This would support clinical assessment and punctual vigilance. Assays based on 3ABC non-structural protein as an antigen are reliable for this intention. However, the insolubility and degradation of recombinant 3ABC during expression and purification are serious challenges. In this study, alternatively to expressing the recombinant 3ABC (r3ABC), we expressed the 3AB coding sequence (~672 bp) as a recombinant protein (r3AB) with a molecular mass of ~26 KDa. Analytical data from three-dimensional structure, hydrophilicity, and antigenic properties for 3ABC and 3AB exhibited the 3C protein as a hydrophobic, while 3AB as a hydrophilic and highly antigenic protein. The expressed r3AB was recovered as a completely soluble matter after merely native purification, unlike the full expressed r3ABC. Immunoreactivity of r3AB to anti-FMDV antibody in infected sera with different FMDV serotypes was confirmed by the western blot and indirect ELISA. Besides, the authentic antigenicity of purified r3AB was demonstrated through its ability to induce specific seroconversion in mice. Summarily, the removal of 3C: has influenced neither 3D structure nor antigenic properties of the purified r3AB, overcame insolubility and degradation of the r3ABC, and generated a potential superior antigen (r3AB) for herd screening of animals to any FMDV serotype.