Unraveling the intricate physiological processes dysregulated in CHD-affected and Dan-Lou tablet-treated individuals.

Coronary heart disease (CHD), a multifactorial cardiovascular condition, arises from the accumulation of atherosclerotic plaque in the coronary arteries, resulting in compromised blood flow to the heart and complications such as angina, myocardial infarction, or heart failure. Addressing global prevalence, risk factors, and genetics is crucial for effective management. The current study aims to identify molecular biomarkers for CHD by scrutinizing the expression patterns of differentially expressed genes (DEGs), utilizing various bioinformatic tools. In this investigation, a total of 24 samples underwent examination using the GEO2R tool. These included eight samples from individuals before treatment (GSM5434123-30), eight samples from patients after Dan-Lou tablet treatment (GSM5434131-38), and eight samples from healthy control subjects (GSM5434139-46). A suite of bioinformatics tools was used to detect enriched genes within the network, namely, Cytoscape (v3.10.1) and Molecular Complex Detection (MCODE). Functional analysis of the DEGs was conducted via clusterProfiler, a R-based package, and ClueGO. 182 and 174 DEGs corresponding to untreated and treated patient sample groups were functionally annotated for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. ARF6 gene dysregulation was implicated in the myeloid cell apoptotic process (GO:0033028), regulation of actin cytoskeleton (hsa:04810), and other vital cellular functions. The myeloid cell apoptotic process (GO:0033028) was also observed to be regulated by the differential expression of the STAT5B gene. Additionally, STAT5B was found to be associated with the regulation of erythrocyte differentiation (GO:0045646). Providing targeted therapy based on the patient's idiosyncratic gene expression profiles could lead to the curing of various disorders in the near future.

Exploring the role of estrogen and progestins in breast cancer: A genomic approach to diagnosis.

Breast cancer (BC) is the most common cancer among women and a major cause of death from cancer. The role of estrogen and progestins, including synthetic hormones like R5020, in the development of BC has been highlighted in numerous studies. In our study, we employed machine learning and advanced bioinformatics to identify genes that could serve as diagnostic markers for BC. We thoroughly analyzed the transcriptomic data of two BC cell lines, T47D and UDC4, and performed differential gene expression analysis. We also conducted functional enrichment analysis to understand the biological functions influenced by these genes. Our study identified several diagnostic genes strongly associated with BC, including MIR6728, ENO1-IT1, ENO1-AS1, RNU6-304P, HMGN2P17, RP3-477M7.5, RP3-477M7.6, and CA6. The genes MIR6728, ENO1-IT1, ENO1-AS1, and HMGN2P17 are involved in cancer control, glycolysis, and DNA-related processes, while CA6 is associated with apoptosis and cancer development. These genes could potentially serve as predictors for BC, paving the way for more precise diagnostic methods and personalized treatment plans. This research enhances our understanding of BC and offers promising avenues for improving patient care in the future.

Biomarker identification of medullary thyroid carcinoma from gene expression profiles considering without-treatment and with-treatment studies-A bioinformatics approach.

Medullary thyroid carcinoma (MTC) is a neuroendocrine tumor derived from parafollicular thyroid gland cells. In both hereditary MTC and sporadic forms, genetic changes result in fundamental changes, and prognosis and mutational status are highly correlated. In this work, biomarker genes (DEGs and DEmiRNAs) for MTC will be computationally identified in order to help in their diagnosis and treatment. The gene expression profiles of two different types of studies, namely without-treatment (wo-trt) and with-treatment (w-trt), are considered for discovering biomarkers. The datasets were retrieved from the GEO database, and the DEGs and DEmiRNAs were analyzed using ExpressAnalyst and GEO2R. The functional analysis of DEGs and DEmiRNAs was performed, and most of the pathways enriched related to thyroid oncological pathways such as MAPK pathway,mTOR pathway, and PI3K-AKT Signaling pathway. Through this conclusion, the RET gene was upregulated wo-trt; the dinaciclib treatment RET gene was down-regulated computationally. To optimize the therapeutic targeting of RET, greater research into the mechanisms regulating RET transcription is necessary.

Decoding genetic and pathophysiological mechanisms in amyotrophic lateral sclerosis and primary lateral sclerosis: A comparative study of differentially expressed genes and implicated pathways in motor neuron disorders.

Motor Neuron Disorders (MNDs), characterized by the degradation and loss of function of motor neurons, are recognized as fatal conditions with limited treatment options and no known cure. The present study aimed to identify the pathophysiological functions and affected genes in patients with MNDs, specifically Amyotrophic Lateral Sclerosis (ALS) and Primary Lateral Sclerosis (PLS). The GSE56808 dataset comprised three sample groups: six patients diagnosed with ALS (GSM1369650, GSM1369652, GSM1369654, GSM1369656, GSM1369657, GSM1369658), five patients diagnosed with PLS (GSM1369648, GSM1369649, GSM1369653, GSM1369655, GSM1369659), and six normal controls (GSM1369642, GSM1369643, GSM1369644, GSM1369645, GSM1369646, and GSM1369647). The application of computational analysis of microarray gene expression profiles enabled us to identify 346 significantly differentially expressed genes (DEGs), 169 genes for the ALS sample study, and 177 genes for the PLS sample study. Enrichment was carried out using MCODE, a Cytoscape plugin. Functional annotation of DEGs was carried out via ClueGO/CluePedia (v2.5.9) and further validated via the DAVID database. NRP2, SEMA3D, ROBO3 and, CACNB1, CACNG2 genes were identified as the gene of interest for ALS and PLS sample groups, respectively. Axonal guidance (GO:0007411) and calcium ion transmembrane transport (GO:0070588) were identified to be some of the significantly dysregulated gene ontology (GO) terms, with arrhythmogenic right ventricular cardiomyopathy (KEGG:05412) to be the top relevant KEGG pathway which is affected in MND patients. ROBO3 gene was observed to have distinctive roles in ALS and PLS-affected patients, hinting towards the differential progression of ALS from PLS. The insights derived from our comprehensive analysis accentuate the distinct variances in the underlying molecular pathogenesis of ALS and PLS. Further research should investigate the mechanistic roles of the identified DEGs and molecular pathways, leading to potential targeted therapies for ALS and PLS.

Exploring the effect of disease causing mutations in metal binding sites of human ARSA in metachromatic leukodystrophy.

The arylsulfatase A (ARSA) gene is observed to be deficient in patients with metachromatic leukodystrophy (MLD), a type of lysosomal storage disease. MLD is a severe neurodegenerative disorder characterized by an autosomal recessive inheritance pattern. This study aimed to map the most deleterious mutations at the metal binding sites of ARSA and the amino acids in proximity to the mutated positions. We utilized an array of computational tools, including PredictSNP, MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, SNAP, and ConSurf, to identify the most detrimental mutations potentially implicated in MLD collected from UniProt, ClinVar, and HGMD. Two mutations, D29N and D30H, as being extremely deleterious based on assessments of pathogenicity, conservation, biophysical characteristics, and stability analysis. The D29 and D30 are located at the metal-interacting regions of ARSA and found to undergo post-translational modification, specifically phosphorylation. Henceforth, the in-depth effect of metal binding upon mutation was examined using molecular dynamics simulations (MDS) before and after phosphorylation. The MDS results exhibited high deviation for the D29N and D30H mutations in comparison to the native, and the same was confirmed by significant residue fluctuation and reduced compactness. These structural alterations suggest that such mutations may influence protein functionality, offering potential avenues for personalized therapeutic and providing a basis for potential mutation-specific treatments for severe MLD patients.

Transcriptomic analysis reveals zinc-mediated virulence and pathogenicity in multidrug-resistant Acinetobacter baumannii.

Acinetobacter baumannii is a gram-negative bacterium well known for its multidrug resistance and connection to nosocomial infections under ESKAPE pathogens. This opportunistic pathogen is ubiquitously associated with nosocomial infections, posing significant threats within healthcare environments. Its critical clinical symptoms, namely, meningitis, urinary tract infections, bloodstream infections, ventilator-associated pneumonia, and pneumonia, catalyze the imperative demand for innovative therapeutic interventions. The proposed research focuses on delineating the role of Zinc, a crucial metallo-binding protein and micronutrient integral to bacterial metabolism and virulence, to enhance understanding of the pathogenicity of A. baumannii. RNA sequencing and subsequent DESeq2 analytical methods were used to identify differential gene expressions influenced by zinc exposure. Exploiting the STRING database for functional enrichment analysis has demonstrated the complex molecular mechanisms underlying the enhancement of pathogenicity prompted by Zinc. Moreover, hub genes like gltB, ribD, AIL77834.1, sdhB, nuoI, acsA_1, acoC, accA, accD were predicted using the cytohubba tool in Cytoscape. This investigation underscores the pivotal role of Zinc in the virulence of A. baumannii elucidates the underlying molecular pathways responsible for its pathogenicity. The research further accentuates the need for innovative therapeutic strategies to combat A. baumannii infections, particularly those induced by multidrug-resistant strains.

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. The dataset contains RNA-Sequencing (RNA-Seq) samples of 46 Human papillomavirus-positive head and neck squamous cell carcinoma and 25 normal Uvulopalatopharyngoplasty cases. The differential marker selection is performed between the groups with a log2FoldChange (FC) score of 2, adjusted p-value < 0.01, and screened 714 genes. The Particle Swarm Optimization (PSO) algorithm selects the candidate gene subset, reducing the size to 73. The state-of-the-art machine learning algorithms are trained with the differentially expressed genes and candidate subsets of PSO. RESULTS: The analysis of predictive models using Shapley Additive exPlanations revealed that seven genes significantly contribute to the model's performance. These include ECT2, LAMC2, and DSG2, which predominantly influence differentiating between sample groups. They were followed in importance by FAT1, PLOD2, COL1A1, and PLAU. The Random Forest and Bayes Net algorithms also achieved perfect validation scores when using PSO features. Furthermore, gene set enrichment analysis, protein-protein interactions, and disease ontology mining revealed a significant association between these genes and the target condition. As indicated by Shapley Additive exPlanations (SHAPs), the survival analysis of three key genes unveiled strong over-expression in the samples from "The Cancer Genome Atlas". CONCLUSIONS: Our findings elucidate critical oncogenic drivers in OPSCC, offering vital insights for developing targeted therapies and enhancing understanding its pathogenesis.

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach.

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox.

BACKGROUND: Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets. METHODS: GEO Expression datasets characterizing mRNA profile changes in different host responses to poxviruses were analyzed for shared pathway identification, and then, the Protein-protein interaction (PPI) maps were built. The viral gene expression datasets of Monkeypox virus (MPXV) and Vaccinia virus (VACV) were used to identify the significant viral genes and further investigated for their binding to the library of targeting molecules. RESULTS: Infection with MPXV interferes with various cellular pathways, including interleukin and MAPK signaling. While most host differentially expressed genes (DEGs) are predominantly downregulated upon infection, marked enrichments in histone modifiers and immune-related genes were observed. PPI analysis revealed a set of novel virus-specific protein interactions for the genes in the above functional clusters. The viral DEGs exhibited variable expression patterns in three studied cell types: primary human monocytes, primary human fibroblast, and HeLa, resulting in 118 commonly deregulated proteins. Poxvirus proteins C6R derived protein K7 and K7R of MPXV and VACV were prioritized as targets for potential therapeutic interventions based on their histone-regulating and immunosuppressive properties. In the computational docking and Molecular Dynamics (MD) experiments, these proteins were shown to bind the candidate small molecule S3I-201, which was further prioritized for lead development. RESULTS: MPXV circumvents cellular antiviral defenses by engaging histone modification and immune evasion strategies. C6R-derived protein K7 binding candidate molecule S3I-201 is a priority promising candidate for treating Mpox.

Surface functionalization, particle size and pharmaceutical co-contaminant dependent impact of nanoplastics on marine crustacean - Artemia salina.

Despite a significant amount of research on micronanoplastics (MNPs), there is still a gap in our understanding of their function as transporters of other environmental pollutants (known as the Trojan horse effect) and the combined effects of ingestion, bioaccumulation, and toxicity to organisms. This study examined the individual effects of polystyrene nanoplastics (PSNPs) with various surface functionalizations (plain (PS), carboxylated (PS-COOH), and aminated (PS-NH2)), particle sizes (100 nm and 500 nm), and a pharmaceutical co-contaminant (metformin hydrochloride (MH), an anti-diabetic drug) on the marine crustacean - Artemia salina. The study specifically aimed to determine if MH alters the detrimental effects of PSNPs on A. salina. The potential toxicity of these emerging pollutants was assessed by examining mortality, hatching rate, morphological changes, and biochemical changes. Smaller nanoparticles had a more significant impact than larger ones, and PS-NH2 was more harmful than PS and PS-COOH. Exposure to the nanoparticle complex with MH resulted in a decrease in hatching rate, an increase in mortality, developmental abnormalities, an increase in reactive oxygen species, catalase, and lipid peroxidase, and a decrease in total protein and superoxide dismutase, indicating a synergistic effect. There were no significant differences between the complex and the individual nanoparticles. However, accumulating these particles in organisms could contaminate the food chain. These results highlight the potential environmental risks associated with the simultaneous exposure of aquatic species to plastics, particularly smaller PS, aminated PS, and pharmaceutical complex PS.

Investigating the Impact of First-Line Anti-Tuberculosis Drugs Encapsulated in a Eugenol-Based Nanoemulsion on Human Serum Albumin.

BACKGROUND: Eugenol exhibits broad-spectrum antibacterial and anti-inflammatory properties. However, cytotoxicity at high concentrations limits the full utilization of eugenol-based drug complexes. Formulations of multidrug-loaded eugenol-based nanoemulsions have reduced cytotoxicity; however, it remains crucial to understand how these eugenol complexes interact with primary human carrier proteins to design and develop therapeutic alternatives. Consequently, this study primarily aims to investigate the impact on Human Serum Albumin (HSA) when it interacts with eugenol-based complexes loaded with first-line anti-tuberculosis drugs. METHODS: This study used various spectroscopic such as UV-visible spectroscopy, Fluorescence spectroscopy, Fourier-transform infrared spectroscopy and computational methods such as molecular docking and 100 ns molecular simulation to understand the impact of eugenol-based first-line anti-tuberculosis drug-loaded nanoemulsions on HSA structure. RESULTS: The binding of the HSA protein and eugenol-based complexes was studied using UV-visible spectroscopic analysis. Minor changes in the fluorophores of the protein further confirmed binding upon interaction with the complexes. The Fourier-transform infrared spectra showed no significant changes in protein structure upon interaction with eugenol-based multidrug-loaded nanoemulsions, suggesting that this complex is safe for internal administration. Unlike eugenol or first-line anti-tuberculosis alone, molecular docking revealed the strength of the binding interactions between the complexes and the protein through hydrogen bonds. The docked complexes were subjected to a 100 ns molecular dynamics simulation, which strongly supported the conclusion that the structure and stability of the protein were not compromised by the interaction. CONCLUSIONS: From the results we could comprehend that the eugenol (EUG)-drug complex showed greater stability in HSA protein structure when compared to HSA interacting with isoniazid (INH), rifampicin (RIF), pyrazinamide (PYR), or ethambutol (ETH) alone or with EUG alone. Thus, inferring the potential of EUG-based drug-loaded formulations for a safer and efficient therapeutic use.

Interaction of eugenol-based anti-tuberculosis nanoemulsion with bovine serum albumin: A spectroscopic study including Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol.

Tuberculosis (TB), a deadly infectious disease, is primarily caused by the bacterium Mycobacterium tuberculosis. The misuse of antibiotics has led to the development of drug resistance, prompting researchers to explore new technologies to combat multidrug-resistant Tuberculosis (MDR TB). Phospholipid-based nanotherapeutics, such as nanoemulsions, are gaining traction as they enhance drug solubility, stability, and bioavailability. Our study focuses on the interaction between Bovine Serum Albumin (BSA) and a drug-loaded nanoemulsion based on Eugenol. This nanoemulsion incorporates Eugenol, Clove, cinnamon oil, and first-line anti-tuberculosis drugs like Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol. The primary objective is to assess the biosafety profile of the nanoemulsion upon interaction with BSA. We employed Fluorescence, UV-visible, and Fourier Transform Infrared Spectroscopy (FTIR) to analyze this interaction. UV-visible spectroscopy detected changes in hydrophobicity due to structural alterations in BSA near the tryptophan residue, leading to the formation of ground-state complexes. Fluorescence spectroscopy demonstrated that the nanoemulsion effectively quenched fluorescence originating from tryptophan and tyrosine residues. Studies using synchronous and three-dimensional spectroscopy point to a potential modification of the aromatic environment of BSA by the nanoemulsion. Resonance light scattering spectra indicated the formation of large aggregates due to the interaction with the nanoemulsion. The second derivative FTIR spectra showed an increase in the magnitude of secondary structure bands, suggesting a conformational shift. This research has significant pharmacological implications for developing safer, more targeted drug delivery systems. The information obtained from the interaction of the nanoemulsion with the blood carrier protein is vital for the future development of superior carriers with minimal adverse effects on patients. It is crucial to remember that conformational changes brought on by drug-ligand complexes attaching to carrier proteins may have negative consequences. Therefore, this study enhances the in vitro evaluation of potential adverse effects of the nanoemulsion on serum proteins.

Deciphering the Impact of Rare Missense Variants in EGFR-TKI-Resistant Non-Small-Cell Lung Cancer through Whole Exome Sequencing: A Computational Approach.

Targeted therapy revolutionizes the treatment of non-small-cell lung cancer (NSCLC), harboring molecular change. Epidermal growth factor receptor(EGFR) mutations play a crucial role in the development of NSCLC, serving as a pivotal factor in its pathogenesis. We elucidated the mechanisms of resistance and potential therapeutic strategies in NSCLC resistant to the EGFR-tyrosine kinase inhibitor (EGFR-TKI). This is achieved by identifying rare missense variants through whole exome sequencing (WES). The goal is to enhance our understanding, identify biomarkers, and lay the groundwork for targeted interventions, thereby offering hope for an improved NSCLC treatment landscape. We conducted WES analysis on 16 NSCLC samples with EGFR-TKI-resistant NSCLC obtained from SRA-NCBI (PRJEB50602) to reveal genomic profiles within the EGFR-TKI. Our findings showed that 48% of the variants were missense, and after filtering with the Ensembl variant effect predictor, 53 rare missense variants in 23 genes were identified as highly deleterious. Further examination using pathogenic tools like PredictSNP revealed 12 deleterious rare missense variants in 7 genes: ZNF717, PSPH, ESRRA, SEMA3G, PTPN7, CAVIN4, and MYBBP1A. Molecular dynamics simulation (MDS) suggested that the L385P variant alters the structural flexibility of ESRRA, potentially leading to unfolding of ERRα proteins. This could impact their function and alter ERRα expression. These insights from MDS enhance our understanding of the structural and dynamic consequences of the L385P ESRRA variant and provide valuable implications for subsequent therapeutic considerations and targeted interventions.

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates. BACKGROUND: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol. OBJECTIVES: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA. METHODS: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking and Dynamics Studies were carried out for the above-mentioned enzymes. RESULTS: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results. CONCLUSION: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

How Precise are Nanomedicines in Overcoming the Blood-Brain Barrier? A Comprehensive Review of the Literature.

New nanotechnology strategies for enhancing drug delivery in brain disorders have recently received increasing attention from drug designers. The treatment of neurological conditions, including brain tumors, stroke, Parkinson's Disease (PD), and Alzheimer's disease (AD), may be greatly influenced by nanotechnology. Numerous studies on neurodegeneration have demonstrated the effective application of nanomaterials in the treatment of brain illnesses. Nanocarriers (NCs) have made it easier to deliver drugs precisely to where they are needed. Thus, the most effective use of nanomaterials is in the treatment of various brain diseases, as this amplifies the overall impact of medication and emphasizes the significance of nanotherapeutics through gene therapy, enzyme replacement therapy, and blood-barrier mechanisms. Recent advances in nanotechnology have led to the development of multifunctional nanotherapeutic agents, a promising treatment for brain disorders. This novel method reduces the side effects and improves treatment outcomes. This review critically assesses efficient nano-based systems in light of obstacles and outstanding achievements. Nanocarriers that transfer medications across the blood-brain barrier and nano-assisted therapies, including nano-immunotherapy, nano-gene therapy, nano enzyme replacement therapy, scaffolds, and 3D to 6D printing, have been widely explored for the treatment of brain disorders. This study aimed to evaluate existing literature regarding the use of nanotechnology in the development of drug delivery systems that can penetrate the blood-brain barrier (BBB) and deliver therapeutic agents to treat various brain disorders.

Emerging strategies and therapeutic innovations for combating drug resistance in Staphylococcus aureus strains: A comprehensive review.

In recent years, antibiotic therapy has encountered significant challenges due to the rapid emergence of multidrug resistance among bacteria responsible for life-threatening illnesses, creating uncertainty about the future management of infectious diseases. The escalation of antimicrobial resistance in the post-COVID era compared to the pre-COVID era has raised global concern. The prevalence of nosocomial-related infections, especially outbreaks of drug-resistant strains of Staphylococcus aureus, have been reported worldwide, with India being a notable hotspot for such occurrences. Various virulence factors and mutations characterize nosocomial infections involving S. aureus. The lack of proper alternative treatments leading to increased drug resistance emphasizes the need to investigate and examine recent research to combat future pandemics. In the current genomics era, the application of advanced technologies such as next-generation sequencing (NGS), machine learning (ML), and quantum computing (QC) for genomic analysis and resistance prediction has significantly increased the pace of diagnosing drug-resistant pathogens and insights into genetic intricacies. Despite prompt diagnosis, the elimination of drug-resistant infections remains unattainable in the absence of effective alternative therapies. Researchers are exploring various alternative therapeutic approaches, including phage therapy, antimicrobial peptides, photodynamic therapy, vaccines, host-directed therapies, and more. The proposed review mainly focuses on the resistance journey of S. aureus over the past decade, detailing its resistance mechanisms, prevalence in the subcontinent, innovations in rapid diagnosis of the drug-resistant strains, including the applicants of NGS and ML application along with QC, it helps to design alternative novel therapeutics approaches against S. aureus infection.

Mechanistic Insights into the Binding of Boar Salivary Pheromones and Putative Molecule with Receptor Proteins: A Comparative Computational Approach.

Precise estrus detection in sows is pivotal in increasing the productivity within the pork industry. Sows in estrus exhibit exclusive behaviors when exposed to either a live boar or the steroid pheromones androstenone and androstenol. Recently, a study employing solid-phase microextraction-gas chromatography-mass spectrometry has identified a novel salivary molecule in boars, known as quinoline. This finding has intriguing implications as a synthetic mixture of androstenone, androstenol, and quinoline induces estrus behaviors in sows. Nevertheless, the precise pheromonal characteristics of quinoline remain elusive. In this study, we validate and compare the binding efficiency of androstenone, androstenol, and quinoline with porcine olfactory receptor proteins (odorant-binding protein [OBP], pheromaxein, salivary lipocalin [SAL], and Von Ebner's gland protein [VEGP]) using molecular docking and molecular dynamics simulations. All protein-ligand complexes demonstrated stability, as evidenced by the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen-bond (H-bond) plots. Furthermore, quinoline displayed higher binding efficiency with OBP, measured at -85.456 ± 8.268 kJ/mol, compared to androstenone and androstenol, as determined by molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) calculations. Conversely, quinoline exhibited a lower binding efficacy when interacting with SAL, pheromaxein, and VEGP compared to androstenone and androstenol. These findings, in part, suggest the binding possibility of quinoline with carrier proteins and warrant further investigation to support the role of quinoline in porcine chemical communication.

Microplastic interactions in the agroecosystems: methodological advances and limitations in quantifying microplastics from agricultural soil.

The instantaneous growth of the world population is intensifying the pressure on the agricultural sector. On the other hand, the critical climate changes and increasing load of pollutants in the soil are imposing formidable challenges on agroecosystems, affecting productivity and quality of the crops. Microplastics are among the most prevalent pollutants that have already invaded all terrestrial and aquatic zones. The increasing microplastic concentration in soil critically impacts crop plants growth and yield. The current review elaborates on the behaviors of microplastics in soil and their impact on soil quality and plant growth. The study shows that microplastics alter the soil's biophysical properties, including water-holding capacity, bulk density, aeration, texture, and microbial composition. In addition, microplastics interact with multiple pollutants, such as polyaromatic hydrocarbons and heavy metals, making them more bioavailable to crop plants. The study also provides a detailed insight into the current techniques available for the isolation and identification of soil microplastics, providing solutions to some of the critical challenges faced and highlighting the research gaps. In our study, we have taken a holistic, comprehensive approach by analysing and comparing various interconnected aspects to provide a deeper understanding of all research perspectives on microplastics in agroecosystems.

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. Active chemical compounds are extracted with the Chemical Entities of Biological Interest (ChEBI) database. The gene targets of each compound are identified based on the pharmacological activity using the DIGEP-Pred database. Thirty-two genes showing Pa> 0.7 is screened, and the target markers are selected after performing gene overlap evaluation with the asthma genes reported in GeneCards and DisGeNET database. Ten markers are identified, such as ADIPOQ, CASP8, CAT, CCL2, CD86, FKBP5, HMOX1, NFE2L2, TIMP1, VDR, in common, listed as molecular targets. Pharmacokinetic assessment (ADME) revealed five natural drug compounds 2-5-7-trihydroxy-2-(4-hydroxyphenyl)-2,3-dihydro-4H-chromen-4-one, (+)-catechin-3'-methyl ether, futoenone, 5-hydroxy-4',7-dimethoxyflavanone, and pinocembrin showing better druggability. Further screening delineates the target (HMOX1) and drug (pinocembrin) for molecular docking evaluation. When docked with HO-1, Pinocembrin showed a binding affinity of -8.0 kcal/mol. MD simulation studies substantiate the docking studies as HO-1 in complex with pinocembrin remains stable in the simulated trajectory. The current findings exhibit the significance of traditional medicines as potential drug candidates against asthma.

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. The anti-inflammatory and antiviral properties of Glycyrrhiza glabra are potent in fighting against COVID-19 and promote immunity boost against the severity of the infection. Athimadhura Chooranam, a polyherbal formulation containing Glycyrrhiza glabra as the main ingredient, is recommended as an antiviral Siddha herb by the Ministry of AYUSH. This paper is intended to identify the phytoconstituents of Glycyrrhiza glabra that are actively involved in preventing individuals from COVID-19 transmission. The modulated pathways, enrichment study, and drug-likeness are calculated from the target proteins of the phytoconstituents at the pharmacological activity (Pa) of more than 0.7. Liquiritigenin and Isoliquiritin, the natural compounds in Glycyrrhiza glabra, belong to the flavonoid class and exhibit ameliorative effects against COVID-19. The latter compound displays a higher protein interaction to a maximum of six, out of which HMOX1, PLAU, and PGR are top-hub genes. ADMET screening further confirms the significance of the abovementioned components containing better drug-likeness. The molecular docking and molecular dynamics method identified liquiritigenin as a possible lead molecule capable of inhibiting the activity of the major protease protein of SARS-CoV-2. The findings emphasize the importance of in silico network pharmacological assessments in delivering cost-effective, time-bound clinical drugs.