Background:Globally, stroke is a major contributor to disability and a leading cause of death. Stroke is more frequent in underdeveloped countries, where ischemic stroke is one of the most common kinds. Therefore, it is imperative to unravel the processes of ischemic stroke in more depth and develop novel therapeutics to combat the condition. Polyphenols provide a significant preventive role against multiple diseases, including cancer, cardiovascular disorders, atherosclerosis, brain dysfunction, and stroke. Methods:In the current investigation, computational tools including Swiss Target prediction, DisGeNET, SwissADME, pkCSM, Cytoscape, InterActiVenn, STRING database, and DAVID database were utilized to identify the signaling pathways, putative targets, along with associated genes of the polyphenols for stroke prevention. Results:This study revealed the possible interactions between the disease targets for Stroke and the selected plant-based polyphenols. Docking results also exhibited the strong to moderate affinity of the selected ligands (Apigenin, Ellagic acid, Ferulic acid, Kaempferol, Genistein, Luteolin, Naringenin, and Quercetin) towards the selected disease target. Conclusion:This study highlights the neuroprotective role of selected polyphenols through the PI3K/Akt pathway. Further studies are required to investigate additional molecular mechanisms between the polyphenols and their derivatives against pathological targets of Stroke.
Purpose:Alzheimer's disease (AD) is a progressive neurodegenerative disorder with limited treatment options, necessitating the development of safer and more effective therapies. The potential of alkaloids derived from the genus Erythrina as acetylcholinesterase (AChE) inhibitors is being investigated to enhance acetylcholine levels in the brain, which is crucial for the treatment of AD. The objective of this study is to identify Erythrina alkaloids with strong inhibitory capacity against AChE and favorable pharmacokinetic profiles. Materials and Methods:A multi-step computational approach was employed, beginning with the virtual screening of 143 Erythrina alkaloid structures using molecular docking against the human AChE crystal structure. The binding affinities were compared with the known AChE inhibitor, galantamine. The top alkaloid, 8-oxoerymelanthine (128), was subjected to further analysis through molecular dynamics simulations, with the objective of evaluating its stability and interactions. In silico ADMET predictions were conducted to assess the pharmacokinetic properties. The applicability of Lipinski's Rule of Five was applied to evaluate oral drug-likeness. Results:8-Oxoerymelanthine (128) exhibited the highest binding affinity and remarkable stability in molecular dynamics simulations. The toxicity predictions indicated a low risk of mutagenicity, hepatotoxicity, and cardiotoxicity. Pharmacokinetic assessments indicated good absorption, moderate blood-brain barrier penetration, and favorable metabolic and excretion profiles, supporting its potential as an orally active drug candidate. Conclusion:8-Oxoerythmelanthine (128) exhibits strong potential as an AChE inhibitor with a favorable balance of efficacy, safety, and pharmacokinetic properties. These results warrant further investigation in preclinical and clinical studies to validate its therapeutic potential and safety for Alzheimer's disease treatment.
Purpose:The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages. Patients and Methods:We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases). To clean and obtain the data ready for modelling, preprocessing steps such as feature standardization and missing value removal were used. Several cutting-edge classifiers were employed for the prediction analysis. In addition, we experimented with the dataset's missing values using the histogram gradient boosting (HGB) model. Results:The feature ranking method demonstrated the ability to distinguish cancer patients from healthy individuals based on hematological features such as WBCs, red blood cell (RBC) counts, and platelet (PLT) counts, in addition to age and creatinine level. The random forest (RF) classifier, followed by linear discriminant analysis (LDA) and support vector machine (SVM), achieved the highest prediction accuracy (ranging from 0.69 to 0.72 depending on the scenario and method investigated), reliably distinguishing between malignant and benign conditions. The HGB model showed improved performance on the dataset. Conclusion:After investigating a number of machine learning methods, an efficient screening platform for non-invasive cancer detection is provided by the integration of haematological indicators with proper analytical data. Exploring deep learning methods in the future work, could provide insights into more complex patterns within the dataset, potentially improving the accuracy and robustness of the predictions.
Antimicrobial resistance (AMR) represents today a major challenge for global public health, compromising the effectiveness of treatments against a multitude of bacterial infections. In recent decades, artificial intelligence (AI) has emerged as a promising technology for the identification and development of new antibacterial agents. This review focuses on AI methodologies applied to discover new antibacterial candidates. Case studies that identified small molecules and peptides showing antimicrobial activity and demonstrating efficiency against pathogenic resistant bacteria by employing AI are summarized. We also discuss the challenges and opportunities offered by AI, highlighting the importance of AI progress for the identification of new promising antibacterial drug candidates to combat the AMR.
Background:Prostate cancer (PCa) development largely depends on increased levels of oxidative stress (OS) and a deficient anti-oxidative system. Identifying genes associated with oxidative stress is critical in order to direct PCa therapy and future research. Methods:The TCGA and GTEx databases provided the bulk RNA-seq data, and the GEO database provided the single-cell data GSE141445. Utilizing reactive oxygen species (ROS) markers, single-cell analysis and cluster identification related to oxidative stress were conducted using the R packages "Seurat" and "AUCell". The differentially expressed genes (DEGs) in normal and PCa samples were identified with the "limma" R package. LASSO regression analysis was used to build a recurrence score (RS) model. The R packages "maftools" and the CIBERSORT method were employed to explore genetic mutation and the infiltrating immune cell, respectively. LAMP5 was chosen for further investigation after random forest analysis was performed. Results:The RS model for PCa was found to be an independent predictor. The tumor immune microenvironment and the frequency of gene mutations differed significantly between the high- and low-risk score groups. Further investigation revealed that downregulation of LAMP5 in PC-3 and DU145 cell lines suppressed cell proliferation and invasion, as demonstrated by the results of in vitro experiments. Conclusion:We successfully created a robust RS model. The result of the study indicates that LAMP5 could contribute to cell proliferation and invasion in PCa.
