Natural flavonols: actions, mechanisms, and potential therapeutic utility for various diseases.

Background: Flavonols are phytoconstituents of biological and medicinal importance. In addition to functioning as antioxidants, flavonols may play a role in antagonizing diabetes, cancer, cardiovascular disease, and viral and bacterial diseases. Quercetin, myricetin, kaempferol, and fisetin are the major dietary flavonols. Quercetin is a potent scavenger of free radicals, providing protection from free radical damage and oxidation-associated diseases. Main body of the abstract: An extensive literature review of specific databases (e.g., Pubmed, google scholar, science direct) were conducted using the keywords "flavonol," "quercetin," "antidiabetic," "antiviral," "anticancer," and "myricetin." Some studies concluded that quercetin is a promising antioxidant agent while kaempferol could be effective against human gastric cancer. In addition, kaempferol prevents apoptosis of pancreatic beta-cells via boosting the function and survival rate of the beta-cells, leading to increased insulin secretion. Flavonols also show potential as alternatives to conventional antibiotics, restricting viral infection by antagonizing the envelope proteins to block viral entry. Short conclusion: There is substantial scientific evidence that high consumption of flavonols is associated with reduced risk of cancer and coronary diseases, free radical damage alleviation, tumor growth prevention, and insulin secretion improvement, among other diverse health benefits. Nevertheless, more studies are required to determine the appropriate dietary concentration, dose, and type of flavonol for a particular condition to prevent any adverse side effects.

Deep transfer learning approaches for Monkeypox disease diagnosis.

Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.

Catabolic profiling of selective enzymes in the saccharification of non-food lignocellulose parts of biomass into functional edible sugars and bioenergy: An in silico bioprospecting.

Objectives: The research aims to analyze the catabolic strength of different hydrolytic enzymes in assessing the biological conversion potential of lignocellulose parts of agricultural biomass wastes into functional edible sugars and biofuels. Materials and Methods: The enzymes' hydrolytic properties-versatile peroxidase, manganese peroxidase, and lignin peroxidase were used to identify their complexing strength with the lignin substrate, whereas endoglucanase cel12A, acidocaldarius cellulase, and Melanocarpus albomyces endoglucanase were tested on the cellulose gel substrate. Because the biodegradation properties are heavily influenced by the "enzyme-substrate complexing energy level," proper molecular optimization and energy minimization of the enzymes and substrates were carried out, as well as the identification of the enzyme's active sites prior to complexing.comprehensive molecular dynamic simulation was run to study their-alpha carbon, root-mean-square deviation (Å), molecular surface area (Å2), root-mean-square fluctuation (Å), radius of gyration (nm), hydrogen bonds with hydrophobic interactions, and solvent accessible surface area (Å2) values for 50 ns. The simulated data mining was conducted using advanced programming algorithms to establish the final enzyme-substrate complexing strength in binding and catalysis. Results: Among the lignin-degrading enzymes, versatile peroxidase shows promising catalytic activity with the best docking pose and significant values in all the dynamic simulation parameters. Similarly, Melanocarpus albomyces endoglucanase shows the best activity in all aspects of molecular docking and dynamics among the cellulose-degrading enzymes. Conclusion: The lignin content of biomass wastes can be degraded into cellulose and hemicellulose using lignin-degrading enzymes. The cellulose can be further degraded into glucose and xylose sugars following the cellulose-degrading enzyme activity. These sugars can be further degraded into biofuel through anaerobic fermentation. Systematic bioconversion of the lignocellulosic components can ensure sustainable biomass management, creating an alternative food and energy source for human beings to face the challenges of global hunger where the enzymes can pave the way.

Renoprotective Effects of Mangiferin: Pharmacological Advances and Future Perspectives.

Both acute and chronic kidney diseases substantially contribute to the morbidities and mortality of patients worldwide. The existing therapeutics, which are mostly developed from synthetic sources, present some unexpected effects in patients, provoking researchers to explore potential novel alternatives. Natural products that have protective effects against various renal pathologies could be potential drug candidates for kidney diseases. Mangiferin is a natural polyphenol predominantly isolated from Mangifera indica and possesses multiple health benefits against various human ailments, including kidney disease. The main objective of this review is to update the renoprotective potentials of mangiferin with underlying molecular pharmacology and to highlight the recent development of mangiferin-based therapeutics toward kidney problems. Literature published over the past decade suggests that treatment with mangiferin attenuates renal inflammation and oxidative stress, improves interstitial fibrosis and renal dysfunction, and ameliorates structural alteration in the kidney. Therefore, mangiferin could be used as a multi-target therapeutic candidate to treat renal diseases. Although mangiferin-loaded nanoparticles have shown therapeutic promise against various human diseases, there is limited information on the targeted delivery of mangiferin in the kidney. Further research is required to gain insight into the molecular pharmacology of mangiferin targeting kidney diseases and translate the preclinical results into clinical use.

Molecular identification, characterization, and antagonistic activity profiling of Bacillus cereus LOCK 1002 along with the in-silico analysis of its presumptive bacteriocins.

Objectives: This research aimed to isolate, identify, and characterize a new strain of Bacillus cereus through different molecular biology approaches so that it could be further studied for therapeutic purposes against selective enteric pathogens. Materials and Methods: Pure isolates of B. cereus were prepared from buffalo yogurt samples in REMBA medium. Initially, the morphological, physiological, and biochemical properties were studied accordingly. Following the tests, the molecular identification for the strain identification was conducted through plasmid DNA extraction, PCR, agarose gel electrophoresis, and 16S rRNA sequencing up to 1.37 kb. Afterward, the antibiotic sensitivity [Epsilometer test (E-Test)] and antifungal activity were tested considering different concentrations. Being classified from the aforementioned tests, a comprehensive antimicrobial activity test was conducted using the cell-free-supernatant (CFS) of the test strain against selective enteric pathogens in humans in vitro. Besides, the different clusters of genes were identified and characterized for understanding the presumptive bacteriocins present in the CFS of the strain in silico, where molecular string properties were calculated. Finally, the evolutionary relationship among diversified bacteriocins synthesized by different Bacillus strains was studied to predict the CFS-containing bacteriocins of the new strain. Results: Purified isolates of B. cereus were Gram-positive rods and showed significant tolerance (p < 0.0001) to different concentrations of pH, phenol, bile salt, and NaCl. 16S rRNA revealed the strain as LOCK 1002, which was strongly sensitive to all the antibiotics used and resistant to selective antifungal agents. The CFS of B. cereus LOCK 1002 was found to be a very promising antagonist to all the enteric pathogens used in the culture condition. Two gene clusters were predicted to be interconnected and responsible for different presumptive bacteriocins. Conclusion: The newly identified LOCK 1002 can be a very potent strain of B. cereus in use as an antimicrobial agent for having different bacteriocin coding gene clusters.