Evaluation of machine learning algorithms and computational structural validation of CYP2D6 in predicting the therapeutic response to tamoxifen in breast cancer.

Objective: CYP2D6 plays a critical role in metabolizing tamoxifen into its active metabolite, endoxifen, which is crucial for its therapeutic effect in estrogen receptor-positive breast cancer. Single nucleotide polymorphisms (SNPs) in the CYP2D6 gene can affect enzyme activity and thus impact tamoxifen efficacy. This study aimed to use machine learning algorithms (MLAs) to identify significant predictors of Breast Cancer-Free Interval (BCFI) and to apply bioinformatics tools to investigate the structural and functional implications of CYP2D6 SNPs.

Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence.

Background: The incidence rate of oropharyngeal squamous cell carcinoma (OPSCC) worldwide is alarming. In the clinical community, there is a pressing necessity to comprehend the etiology of the OPSCC to facilitate the administration of effective treatments.

Methods: This study confers an integrative genomics approach for identifying key oncogenic drivers involved in the OPSCC pathogenesis.

In silico network pharmacology analysis and molecular docking validation of Swasa Kudori tablet for screening druggable phytoconstituents of asthma.

Traditional medicines are impactful in treating a cluster of respiratory-related illnesses. This paper demonstrates screening active, druggable phytoconstituents from a classical Siddha-based poly-herbal formulation called Swasa Kudori Tablet to treat asthma. The phytoconstituents of Swasa Kudori are identified as Calotropis gigantea, Piper nigrum, and (Co-drug) Abies webbiana.

In silico network pharmacology study on Glycyrrhiza glabra: Analyzing the immune-boosting phytochemical properties of Siddha medicinal plant against COVID-19.

Immunosenescence is a pertinent factor in the mortality rate caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The changes in the immune system are strongly associated with age and provoke the deterioration of the individual's health. Traditional medical practices in ancient India effectively deal with COVID-19 by boosting natural immunity through medicinal plants.

Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.

Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer.

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology.

Molecular docking analysis of Indole based diaza-sulphonamides with JAK-3 protein.

JAK-3 gene is a part of an important signalling pathway in oral cancer. Therefore, it is of interest to evaluate the inhibitory properties of new indole based diaza-sulphonamides compounds against JAK3 gene. Molecular docking analysis showed that among the selected compounds (1-9), the compounds 1-4 turned out to be the most potentially capable ones to be used as ant-cancer drugs.

Airway and Oral microbiome profiling of SARS-CoV-2 infected asthma and non-asthma cases revealing alterations-A pulmonary microbial investigation.

New evidence strongly discloses the pathogenesis of host-associated microbiomes in respiratory diseases. The microbiome dysbiosis modulates the lung's behavior and deteriorates the respiratory system's effective functioning. Several exogenous and environmental factors influence the development of asthma and chronic lung disease.

Machine learning algorithms and computational validation of single-nucleotide polymorphisms of antioxidant enzymes and oxidative stress markers in neonates.

To evaluate machine learning algorithms (MLAs) for predicting factors (oxidative stress biomarkers [OSBs] and single-nucleotide polymorphism of the antioxidant enzymes) for respiratory distress syndrome (RDS) and significant alterations in the liver functions (SALVs). MLAs were applied for predicting the RDS and SALV (with OSB and single-nucleotide polymorphisms in the antioxidant enzymes) with area under the curve (AUC) as the accuracy measure. The C5.

Unraveling the Dysbiosis of Vaginal Microbiome to Understand Cervical Cancer Disease Etiology-An Explainable AI Approach.

Microbial Dysbiosis is associated with the etiology and pathogenesis of diseases. The studies on the vaginal microbiome in cervical cancer are essential to discern the cause and effect of the condition. The present study characterizes the microbial pathogenesis involved in developing cervical cancer.

Psoriasis Flare-Up in a Patient Treated With Docetaxel for Metastatic Prostate Cancer.

Dermatologic toxicities, such as urticaria and mucositis, with docetaxel, have been commonly reported; however, fixed-plaque erythrodysesthesia is a rare adverse phenomenon with a reported incidence of less than 5% of patients. Docetaxel-induced psoriasis is extremely rare, and to date, very few cases have been reported in the literature. We present a literature review of psoriasis cases secondary to docetaxel and report our own case of severe docetaxel-induced psoriasis in the setting of treatment of metastatic prostate cancer.

A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.

This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022.

Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach.

The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures.

Early versus late response to daratumumab-based triplet therapies in patients with multiple myeloma: a pooled analysis of trials POLLUX, CASTOR and MAIA.

In multiple myeloma (MM), it is unclear whether early and late responders to daratumumab have similar outcomes. We pooled individual-level data from phase 3 trials and divided them into early and late response groups based on median time to response. Altogether 670 and 213 patients achieved very good partial response (VGPR) or better and minimal residual disease (MRD) negativity, respectively.

Completion Rates and Clinical Changes of Patients Seeking Non-Invasive Treatment for Low Back Pain in 13 Centres of a Sports Medicine Institute in India.

Background/aim: Low back pain (LBP) is the major cause of disability worldwide. The existing treatments are expensive and associated with complications. The present study aimed to determine the proportion of patients completing therapy and rehabilitation phases and describe the changes in self-reported symptoms and functional disability among LBP patients enrolled in a private sports medicine institute.