Neuronal developmental disorder is a class of diseases in which there is impairment of the central nervous system and brain function. The brain in its developmental phase undergoes tremendous changes depending upon the stage and environmental factors. Neurodevelopmental disorders include abnormalities associated with cognitive, speech, reading, writing, linguistic, communication, and growth disorders with lifetime effects. Computational methods provide great potential for betterment of research and insight into the molecular mechanism of diseases. In this study, we have used four samples of microarray neuronal developmental data: control, RV (resveratrol), NGF (nerve growth factor), and RV + NGF. By using computational methods, we have identified genes that are expressed in the early stage of neuronal development and also involved in neuronal diseases. We have used MeV application to cluster the raw data using distance metric Pearson correlation coefficient. Finally, 60 genes were selected on the basis of coexpression analysis. Further pathway analysis was done using the Metascape tool, and the biological process was studied using gene ontology database. A total of 13 genes AKT1, BAD, BAX, BCL2, BDNF, CASP3, CASP8, CASP9, MYC, PIK3CD, MAPK1, MAPK10, and CYCS were identified that are common in all clusters. These genes are involved in neuronal developmental disorders and cancers like colorectal cancer, apoptosis, tuberculosis, amyotrophic lateral sclerosis (ALS), neuron death, and prostate cancer pathway. A protein-protein interaction study was done to identify proteins that belong to the same pathway. These genes can be used to design potential inhibitors against neurological disorders at the early stage of neuronal development. The microarray samples discussed in this publication are part of the data deposited in NCBI’s Gene Expression Omnibus (Yadav et al., 2018) and are accessible through GEO Series (accession number GSE121261).

Artificial intelligence (AI) has emerged as a transformative force in personalized healthcare and precision medicine over the past decade. AI techniques like machine learning, deep learning, and natural language processing make possible the study of huge quantities of heterogeneous patient records from electronic health records, genomic profiles, wearable devices, and clinical trials. This allows for more accurate disease prediction, personalized treatment planning, and tailored drug discovery. Key areas of impact include AI-driven biomarker discovery, virtual drug screening, de novo drug design, and pharmacogenomics. The integration of AI is revolutionizing multiple aspects of precision medicine, from identifying novel therapeutic targets to optimizing clinical trial design and drug dosing. AI algorithms can detect subtle patterns in complex biological data, predict drug-target interactions, and simulate molecular behaviour to accelerate the typically costly and time-consuming drug development process. However, challenges remain around data quality, privacy, algorithmic bias, and equitable implementation. Ethical considerations regarding genetic discrimination and informed consent also need to be carefully addressed. This review examines the current applications, challenges, and future directions of AI in advancing patient-specific therapies and drug development.

Acrylamide, a neurotoxic compound formed during high-temperature cooking processes, poses significant health risks, particularly regarding neurotoxicity. This study investigates the association between acrylamide exposure and neurotoxic effects, utilizing RNA sequencing (RNA-Seq) to elucidate the underlying molecular mechanisms. Gene expression profiles of neuronal cell models exposed to acrylamide was retrieved from ENA database with project id: PRJNA545942. RNAseq analysis was done to identify significant dysregulation of genes involved in critical cellular processes, including oxidative stress response, apoptosis, and neuroinflammation. Results revealed an upregulation of inflammatory pathway genes and a downregulation of neuroprotective factors, indicating a shift towards a pro-apoptotic and inflammatory state in response to acrylamide exposure. Among the upregulated genes, HMGSC1 and TUBB5 were particularly significant for their association with neurotoxicity pathways. TUBB5 plays a crucial role in neuronal migration and axonal guidance; abnormalities in this gene are severe neurodevelopmental disorders. Findings indicate that acrylamide exposure activates pathways linked to neuronal cell death and impaired neuronal function, providing a clearer understanding of its neurotoxic potential. This research emphasizes RNA-Seq as a valuable tool for investigating acrylamide-induced neurotoxicity and contributes to risk assessment frameworks for chemical exposures. By advancing the understanding of acrylamide's impact on neuronal health, this study lays the groundwork for future studies to mitigate its neurotoxic effects and protect public health.

Objective: Parasites of the genus Plasmodium cause a great deal of morbidity and mortality worldwide, largely in regions with limited access and indication to the tools necessary to control mosquito populations and to treat human infections of malaria. Five species of this class of eukaryotic pathogens cause different human diseases, with Plasmodium falciparum alone infecting approximately 500 million people per year and resulting in approximately one million deaths. Materials and Methods: The two genes encoding the Shewanella-like protein phosphatases of P. falciparum, SHLP1 and SHLP2, are conserved among members of Plasmodiidae family. SHLP is frequently found in asexual blood stages and expressed at all stages of the life cycle of parasite. SHLP deletion results in a reduction in microneme formation, ookinetes (zygote) development, and complete ablation of oocyst formation, thereby blocking transmission of parasite. Structure modeling of SHLP protein can be helpful in understanding the active site and binding site information and hence can be used for drug designing and for therapeutics against malaria. Study of SHLP and its variants was carried out using UniProtKB database. Homology modeling was performed using Schrödinger software, and the modeled structure was verified using Ramachandran plot. Ten antioxidants were searched in PubChem database for docking and comparative analysis. Docking was carried out against SHLP-modeled protein, and the ligand–protein interaction map was analyzed. Effective role of resveratrol was studied against SHLP protein using docking method to identify protein–ligand interaction scheme and bond formation. Results: SHLP protein was modeled and docking was carried out to identify the binding sites and interaction with the SHLP protein. Docking study suggested that resveratrol has a strong interaction with SHLP protein and can be used as a potential ligand for drug designing. Conclusion: SHLP plays a crucial role in ookinetes and microneme development in Plasmodium; hence ligand, which can interact and inhibit SHLP protein, can be a potential drug against malarial parasite development. We studied the binding of antioxidant, such as resveratrol, with this protein-using docking method and it was found that resveratrol as an antioxidant can bind with the target SHLP protein.

Introduction: Breast cancer is the swelling structures in the breast cells. After skin cancer, breast cancer is the most broadly accepted malignancy analysed in ladies in the United States. Breast cancer can happen in both males and females, yet it's undeniably more normal in ladies. Indications of mammary glands cancer may involve a ball or lumps in the breast, an alteration of breast shape, scratching of the skin, fluid coming out from areola, a recently reversed areola, or a red or textured fix of skin. In those with removed lay out of the illness, there can be bone agony, swollen lymph hubs, windedness, or yellow skin. There are certain microorganisms that are involved in breast cancer and have function thereof. The study of these microorganisms can be done through metagenomics study and comparing with normal samples. Methods: For current study pairwise metagenomics data for breast cancer sample was retrieved from ENA site. Four FASTQ files were retrieved with the Accession no PRJEB25419 and complete metagenomics analysis was done using GALAXY Server. Tools like FASTQC, Trim galore, KRAKEN2, Krona pie chart etc. were used for classification and identification of microorganisms in the samples. Results: The taxonomic and functional analysis of the metagenome of breast cancer identifies different organisms like. Actinobacteria, Bifidobacterium cuniculi, Murnine leukemia virus, Human endogenous retrovirus, Pezizomycotina. Different microbes and their percentage in the breast cancer have been predicted that are 4% of Nematocera, 2% of Brachycera, 3% of Galegeae, 2% of Nelumbo nucifera. Conclusion These results give insights into the metagenome of breast cancer that can be used for clinical perspectives. Detailed study is required to establish the association of these microbes with development and progression of breast cancer. Keywords: - Breast cancer, Metagenomics, Galaxy, ENA Database, Krona pie chart.