Despite advances in lipid-lowering and anti-inflammatory medications, atherosclerotic cardiovascular disease (ASCVD) continues to be the leading cause of morbidity and mortality worldwide. Recent studies have identified the gut microbiota as a key modulator of cardiovascular health via the gut-heart axis. This review investigates the molecular processes by which microbial metabolites affect atherogenesis. Proatherogenic substances like trimethylamine-N-oxide (TMAO), which are produced from dietary precursors through gut microbial and hepatic metabolism, aggravate foam cell production, platelet aggregation, and vascular inflammation. Short chain fatty acids (SCFAs), such as butyrate and propionate, have been shown to protect against atherosclerosis by activating G-protein-coupled receptors, regulating gene expression, and improving endothelial function. Additionally, secondary bile acids, tryptophan derivatives, and phenylacetylglutamine have emerged as important microbial metabolites involved in vascular disease. The review also summarizes various therapeutic strategies such as use of probiotics, prebiotics, postbiotics, precision microbiome editing (using bacteriophages and CRISPR-Cas systems), and fecal microbiota transplantation (FMT) for targeting gut-heart axis. Multi-omic systems combined with artificial intelligence can now detect disease-specific microbial signatures, improving risk stratification and paving the way for precision microbiome-based therapeutics. However, challenges such as determining causality, regulatory intricacies, and inter-individual variability in host-microbiome interactions remain. Despite these obstacles, the gut-heart axis provides a disruptive paradigm in preventive cardiology by emphasizing tailored microbiome therapies as a complement to traditional ASCVD care.

Silent mating type information regulation 2 homolog (SIRT1), a key protein involved in cellular regulation, exhibits altered expression in breast cancer. The study aimed to assess serum Sirtuin 1 level, investigate SIRT1 gene polymorphisms, and examine their association with breast cancer. One hundred seventy-two breast cancer patients and 78 healthy age-matched females were included in the study. Genetic variants of the SIRT1 gene were identified using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism/Sanger Sequencing and serum SIRT1 levels using Enzyme-Linked Immuno Sorbent Assay. Serum SIRT1 levels were significantly higher in breast cancer patients [0.822 (0.72-0.96)] than controls [0.752 (0.66-0.86)], p = 0.0027. Subgroup analyses revealed notable increases in Human epidermal growth factor receptor 2-positive (p = 0.0016), hormone-positive (p = 0.0232), and Triple Negative Breast Cancer groups (p = 0.4426) compared to the control group. The SIRT1 levels were also significantly associated with breast cancer (p = 0.0252). SIRT1 exhibited an Area Under Curve (AUC) of 0.618, with 58% sensitivity and specificity at a cutoff of 0.797, showing poor discriminative ability. rs141528984 had a significant difference in allele frequency between patient group and control group (OR = 3.48, 95% CI: 1.896-6.516), p < 0.0001. There were no significant associations between SIRT1 gene polymorphisms and serum Sirtuin 1 levels in breast cancer patients. The proliferation marker Ki67 showed a significant association (p = 0.0428) with specific SIRT1 variants, rs777323664, rs757804740, and rs184282868. SIRT1 levels are elevated in breast cancer patients indicate its potential as a diagnostic marker. Mutant alleles of rs141528984 were more common in controls compared to case group, indicating its protective function. Ki67 expression was associated with rs777323, rs757804740, and rs1842828683 which may contribute to tumor aggressiveness.

India is rapidly becoming the global epicenter of type 2 diabetes (T2D), a complex disease influenced by multiple factors including diet, lifestyle, urbanization, genetics, and environmental exposures such as air pollution. The rapid pace of urbanization, coupled with growing population density, exacerbates air pollution levels in major Indian cities, with pollutants such as particulate matter (PM2.5), (PM10), nitrogen dioxide (NO2), nitrogen oxides (NOX), and carbon monoxide (CO) being significantly elevated in comparison with the rural areas. These pollutants have been implicated in the pathogenesis of T2D, by inducing insulin resistance, oxidative stress, and endothelial dysfunction leading to vascular complications of T2D. International studies also highlight a similar association between air pollution and the incidence of T2D. The multifactorial nature of the disease, combined with the myriad of contributing environmental and lifestyle factors, makes it challenging to pinpoint specific risk elements. To mitigate the impact of these combined factors, continuous monitoring of air quality is imperative. Monitoring of traffic emissions, promotion of electric vehicles (EVs), and enhancement of mass transit options can each mitigate the impact of air pollution on type 2 diabetes. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) can optimize these interventions, making them even more effective. Urban planning strategies focused on increasing green spaces, afforestation, and sustainable construction practices are essential for long term health benefits. Collectively, these solutions present a holistic approach to combating T2D and improving public health amidst the challenges posed by urbanization and environmental pollution in India.

ABSTRACT: Gestational diabetes mellitus (GDM) is a complex metabolic disorder with significant health implications for both mother and fetus, yet its molecular underpinnings remain incompletely defined. This study employed an integrative bioinformatics approach to elucidate the genetic architecture and molecular pathways involved in GDM, using a curated set of 30 GDM-associated genes from the DisGeNET database. Comprehensive analyses, including Gene Ontology, pathway enrichment, transcriptional regulation, tissue expression, metabolite interaction, and drug association studies, were conducted using R version 4.4.2 with stringent statistical controls (adjusted p < 0.05). The results revealed strong enrichment in vitamin B12 and folate metabolism pathways, implicating a critical nutritional-genetic interface. Key genes such as IL6, INSR, LEP, TNF, and CRP were linked to metabolic and inflammatory regulation, while pathways related to adipogenesis, leptin-insulin signaling, and non-alcoholic fatty liver disease emerged as central networks. Hormonal metabolites and potential therapeutic agents, including statins and anti-inflammatory drugs, were identified, and transcriptional analyses highlighted complex regulatory mechanisms. Tissue-specific findings emphasized the systemic nature of GDM, with liver, adipose, and pancreatic involvement. Collectively, this study provides a multidimensional view of GDM pathogenesis and identifies candidate biomarkers and therapeutic targets, laying the groundwork for future functional validation and precision medicine strategies.

Exosomes influence tumor progression via altered cargo, yet cross-cancer analyses of exosomal protein–metabolite associations remain scarce. We mined ExoCarta to identify cancer-specific exosomal proteins, integrated them with metabolomics data, and mapped them to pathways, revealing a protein–metabolite–pathway axis for understanding cancer metabolic reprogramming. We retrieved exosomal proteins unique to ten cancer types from the ExoCarta database. Hub genes were identified via CytoHubba and their expression was validated through expression datasets such as gene expression profiling interactive analysis (GEPIA), University of ALabama at Birmingham CANcer data analysis Portal (UALCAN), OncoDB, and TNMPlot. Functional enrichment was done using Gene Ontology, Reactome, and CancerHallmarks. Transcription factors were analyzed using Harmonizome. Proteins were mapped to metabolites using Human Metabolome Database (HMDB), Enrichr, and Appyter. Metabolite Set Enrichment Analysis was performed in MetaboAnalyst. Enrichment analysis of exosome-associated proteins across cancers revealed distinct biologic processes and molecular functions, including ion transport, protease regulation, and metabolic signaling. Hub genes specific to each cancer include—SLC5A6, ALPP (bladder); LIPG, CEL (colorectal); LYZ (liver); SDHB (pancreatic); PRKD1 (prostate); and ATP6V0D1 (ovarian) were significantly upregulated in tumors. These genes were enriched in angiogenesis, metastasis, and metabolic reprogramming pathways. Cancer-specific enrichments included lipid metabolism (PRKD1, ATP6V0D1), mitochondrial respiration (SDHB), and amino sugar/fatty acid pathways (LYZ, LIPG, CEL, SLC5A6, ALPP). Metabolite set enrichment linked them to diacylglycerol, phosphate, and ubiquinone metabolism, reinforcing their role in tumor-specific metabolic alterations and highlighting their importance. This bioinformatics analysis reveals exosomal proteins as metabolic modulators, warranting further experimental and clinical validation.

Vitiligo is a chronic, acquired pigmentary disorder characterized by the selective loss of melanocytes, leading to depigmented patches on the skin, hair, and mucous membranes. Although not life‐threatening, vitiligo imposes significant psychosocial burdens. Its multifactorial etiology involves genetic predisposition, immune dysregulation, environmental factors and oxidative stress. Recent advances in multi‐omics technologies have enabled the integration of genomic, transcriptomic, proteomic, metabolomic, and lipidomic data to elucidate the molecular networks underlying melanocyte loss. We retrieved computationally curated gene–disease associations from DisGeNET for vitiligo (CUI-C0042900) and its subtypes including progressive vitiligo (CUI-C3806428), segmental vitiligo (CUI-C1274648), and localized vitiligo (CUI-C1304469). The genes were subjected to gene set enrichment analysis via Enrichr. Our results revealed significant enrichment in immune-related processes, including cytokine production, antigen presentation via MHC class II, and inflammatory signalling mediated by tumor necrosis factor and interferon—gamma. Oxidative stress and metabolic perturbations were associated with enrichment in ROS-related functions. Enrichment of transcription factors RELA, PRDM14, and IRF8 was observed in response to the gene set associated with vitiligo. These findings underscore the convergent roles of immune-mediated damage, and oxidative stress imbalance in vitiligo. The integration of multiple gene set enrichments may enable better understanding of immune dysfunctions in vitiligo. Our results provide a basis that may be considered for future in vitro and in vivo validation studies.

Background: Gallbladder cancer (GBC) is a highly aggressive malignancy with limited therapeutic options and poor prognosis. This study integrates bioinformatics and machine learning (ML) approaches to analyze the transcriptional changes in GBC and identify potential therapeutic targets. Methods: The NCBI GEO database was used to gather the published microarray data gene expression patterns of GBC cells stimulated with chenodeoxycholic acid (CDCA), analyzed using RNA-seq to identify differentially expressed genes (DEGs). R software was used to process the data, Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for the enrichment of pathways and their function in DEGs, and string database was used to study protein-protein interactions (PPIs). ML algorithms, including support vector machine, random forest, XG boost, Least Absolute Shrinkage and Selection Operat (LASSO), Elastic Net, and neural networks, were applied to predict the outcomes based on gene expression profiles. Results: A total of 11,009 DEGs were identified, including 6663 upregulated and 4346 downregulated genes, with key downregulated genes such as PADI1, CXCL8, and MUC5AC significantly associated with immune modulation, epithelial barrier function, and tumor invasion. Pathway enrichment analysis using GO and KEGG highlighted critical pathways, including IL-17 signaling, glycosylation defects, and epithelial cell signaling, emphasizing their roles in tumor progression and immune evasion. PPI network analysis identified functional clusters with high connectivity, suggesting significant biological roles in GBC. ML techniques, including Gradient Boosting, Random Forest, and XGBoost, were employed for predictive modeling. These models achieved exceptional accuracy (area under the curve = 1.0), with LASSO and ElasticNet feature selection pinpointing critical genes driving GBC progression. Principal Component Analysis captured 50.7% of the transcriptional variability, confirming distinct gene expression profiles between CDCA-treated and control samples. Hierarchical clustering validated these findings, highlighting clear segregation of experimental conditions. Conclusion: The results support the pivotal role of FGF19-FGFR4 signaling in GBC progression and provide novel insights into transcriptional disruptions associated with the disease. The findings underscore the potential of targeting immune modulation and glycosylation pathways as therapeutic strategies in GBC. This study offers novel insights into the molecular landscape of GBC.

Diabetic foot, a serious complication of diabetes mellitus, involves delayed wound healing and an increased risk of infection and amputation. Netrin-1, a laminin-related secreted protein, plays a key role in angiogenesis, inflammation, and tissue repair, making it a relevant gene in diabetic foot pathology. This study aimed to identify and analyse single nucleotide polymorphisms (SNPs) in the Netrin-1 (NTN1) gene using bioinformatics tools to distinguish between damaging and neutral variants. Predictive algorithms such as SIFT, PROVEAN, and I- Tasser were used to assess their potential impact. Notably, SNP analysis at position 223 revealed several potentially harmful substitutions. Structural modelling indicated that these mutations could alter the protein’s size and charge, potentially disrupting its normal function.

Introduction: Gestational diabetes mellitus (GDM) is a significant pregnancy complication linked to adverse outcomes for both mother and child. Early identification of high-risk individuals is crucial for effective management and prevention for the onset/progression of the GDM. Our study aims to a) evaluate the effectiveness of a newly developed machine learning based risk factor screening tool for predicting GDM and b) to compare its predictive performance against established models and current literature. Methods: This study explored SNP data from the leptin (LEP) and leptin receptor (LEPR) genes to develop machine learning models for predicting gestational diabetes mellitus (GDM). It included data preprocessing, such as cleaning and feature selection, focusing on genetic markers, metabolic parameters, and demographic information. Various algorithms, including Logistic Regression, Decision Trees, and Random Forests, were used, and their performance was evaluated using metrics like accuracy and ROC-AUC to determine the best model for GDM prediction. Results: The newly developed screening tool demonstrated a sensitivity of 85%, specificity of 78%, positive predictive value (PPV) of 68%, and negative predictive value (NPV) of 90% in predicting GDM. Comparatively, machine learning models showed higher sensitivity (90-95%) but lower specificity (65-75%). Conclusion: The developed risk factor screening tool is a viable method for predicting GDM, with accuracy metrics comparable to advanced machine learning models and established literature. Future research should focus on refining these tools and exploring their integration into routine prenatal care to enhance early detection and intervention strategies for GDM.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a rare yet fatal condition characterized by the deposition of transthyretin amyloid fibrils in the heart. This review article synthesizes the findings of a proposed study aimed at comprehensively understanding ATTR-CM in the Indian population. The diagnosis of transthyretin amyloidosis (ATTR) cardiac disease faces several constraints that complicate early and accurate detection. One major challenge is the nonspecific nature of its clinical presentation, often mimicking other more common conditions like hypertensive heart disease or hypertrophic cardiomyopathy. This overlap can delay proper identification and lead to misdiagnoses. Additionally, the gold standard diagnostic tools, such as endomyocardial biopsy and advanced imaging techniques like cardiac MRI or scintigraphy with technetium-labeled compounds, are not always readily available, especially in resource-limited settings. Genetic testing, although essential for distinguishing hereditary from wild-type ATTR, may also be limited by access and cost. Furthermore, a lack of awareness and clinical suspicion among healthcare providers can result in under diagnosis or late diagnosis, which significantly impacts patient outcomes. The study intends to identify and analyze patients diagnosed with ATTR-CM in India to estimate its prevalence and describe patient characteristics, including gender differences and mortality rates. Moreover, it seeks to investigate the significance of early symptoms ("red flags") in identifying ATTR-CM and to develop and evaluate machine learning algorithms for its early diagnosis. Patients with ATTR-CM will be identified retrospectively using diagnosis codes and diagnostic algorithms, and compared with matched non-ATTR heart failure patients. Electronic records will be utilized for algorithm development and testing. Anticipated outcomes include providing the first statewide estimates of ATTR-CM prevalence and risk factors in India, emphasizing the disease's severity, and underlining the importance of early diagnosis, particularly among female patients, to facilitate effective treatment and disease progression prevention. Additionally, the study aims to demonstrate the utility of machine learning algorithms in early disease identification and detecting missed diagnoses. This review highlights the paucity of studies examining the prevalence of ATTR cardiomyopathy in India and underscores the need for machine learning algorithms for early detection, offering valuable insights into addressing this critical healthcare challenge.

Objective: The main objective of this study was to analyze the serum levels of Nuclear factor-kappa B (NF & kcy;B) in breast cancer as compared to age-matched healthy individuals. It also aimed to investigate variations in NF & kcy;B levels across stages, types of breast cancer and also its correlation with Neutrophil-Lymphocyte Ratio (NLR) and Platelet-Lymphocyte Ratio (PLR). Methodology: Blood samples of 40 female breast cancer patients and 33 healthy controls were collected in anticoagulant and plain tubes. Hematological parameters were evaluated with an automatic analyzer, and NF & kcy;B levels were measured using ELISA. Statistical analysis was performed with GraphPad Prism 8 software. Results: Significant elevation of serum NF & kcy;B levels (p < 0.0001) was observed in breast cancer patients when compared to the healthy females. Subtype analysis indicated higher NF & kcy;B levels in Triple Negative Breast Cancer (TNBC), suggesting a potential role in the aggressiveness of this subtype. Correlation analysis with hematological parameters, including NLR and PLR, showed weak associations. However, Receiver Operating Characteristic (ROC) analysis indicated the promising role of NF & kcy;B as a diagnostic biomarker for breast cancer with a sensitivity of 75 % and specificity of 74.8 % at a cutoff value of 0.447 ng/ml. Conclusion: Serum NF kappa B levels were significantly elevated in breast cancer patients when compared to healthy individuals, suggesting its potential as a diagnostic biomarker for the disease. While weak correlations were observed with hematological parameters like NLR, and PLR in breast cancer patients, the study highlights the promising diagnostic potential of NF kappa B and its significant association with breast cancer.

This module aims to equip healthcare professionals with a comprehensive understanding of gene and gene-modified cell therapies for Rare Diseases, enabling their integration into clinical practice. In India, research and clinical focus on gene therapy for Rare Diseases are limited, despite the significant burden of these conditions and the lack of effective treatments. This knowledge sharing module addresses this critical gap by exploring innovative therapeutic approaches like gene therapy. The knowledge sharing module's novelty lies in its comprehensive methodology, including literature review, stakeholder consultation, and rigorous evaluation. It aims to make substantial contributions to the management of Rare Diseases in India by leveraging existing knowledge while addressing specific gaps in understanding and practice. The module is organized into ten sections, covering key topics such as the molecular basis of Rare Diseases, clinical applications of gene therapy, safety and efficacy considerations, regulatory and ethical issues, and emerging technologies. Learners will engage with multimedia content, interactive activities, case studies, and expert insights to acquire the skills needed to navigate gene therapy research and clinical implementation. The module's effectiveness should be assessed through pre- and post-intervention evaluations, focusing on awareness, knowledge, behavior change, and clinical practice improvement. Data should be collected via surveys, questionnaires, clinical audits, and focus group discussions, with results analyzed using statistical and thematic methods. The knowledge sharing module aims to advance medical understanding, enhance patient care, and foster interdisciplinary collaboration. By elucidating genetic determinants and clinical applications, it seeks to improve diagnosis, treatment, and management of Rare Diseases, ultimately leading to better patient outcomes.

Objective: To find the association of leptin-receptor gene(LEPR) polymorphism with gestational diabetes mellitus (GDM) and its role in altered leptin levels, insulin resistance, and dyslipidemia in GDM. Design & Setting: This prospective cross-sectional study was conducted in Justice KS Hegde Hospital, Mangalore, India. 100 GDM patients and 100 gestational age and BMI matched normal glucose tolerant pregnant women were recruited as cases and controls. Method: Genotyping of leptin-receptor (LEPR)Gln223Arg was performed by PCR-RFLP. Fasting blood sugar, leptin, insulin, C-peptide, and lipid profile were performed. Various insulin-resistance models were constructed using suitable formulae. Results: No significant association was found between leptin-receptor gene polymorphism and leptin levels, insulin-resistance in GDM. However, Odd’s ratio showed that individuals with A allele were at 1.25 times higher risk of developing GDM. HOMA-B cells significantly varied among Lep-R genotypes, values being double in AA genotype, compared to AG, ten times higher in AA compared to GG. The value was four times higher in AG compared to GG. Conclusion: It could be concluded from the study that, there are no significant association between leptin receptor, LEPR Gln223Arg alleles and gestational diabetes, leptin levels, and insulin resistance. However, subjects with the ‘G’ allele for LEPR at higher risk of hyperleptinemia. C–peptide based insulin resistance models were elevated in GDM patients. The study can establish a cycle of gene polymorphism altering leptin levels, which in turn can alter insulin secretion and insulin resistance, contributing to dyslipidemia of pregnancy and gestational diabetes.

Objective of the study was to identify differentially expressed genes (DEGs) and miRNAs as potential genetic markers to differentiate cancerous versus non-cancerous hepatic tissues as well as to compare the DEGs in those patients with and without IL-28B gene polymorphisms using bioinformatics tools. Microarray data (GSE41804) of liver tissue with/without cancer, deposited by Hodo et al8 was obtained from NCBI GEO database. Genotyping of patients with HCC associated with chronic hepatitis C was carried out to determine the association between the IL-28B genotype (rs8099917) and clinical outcome. The gene expressions were analysed using integrated bioinformatics. R software was used to process the data: Gene ontology and the KEGG database for the enrichment of pathways, STRING database for protein-protein interactions, TARBASE software to obtain miRNAs of DEGs and STITCH for drug-gene interactions. Polymorphisms of IL-28B gene were analysed by SIFT and Polyphen softwares. A total of 50,810 genes were extracted from which 250 differentially expressed genes (DEGs) were downloaded. Top 20 highly significant DEGs (p<0.00035) were analysed in detail. DEGs in cancerous tissues with major allele (TT of rs8099917) showed two upregulated genes (SPINK1, VAMP4) and five downregulated genes (MGMT, TREH, PXDC1, BGGAT1 and FREM2) as compared to non-cancerous tissues. In case of tissues with minor alleles (TG + GG), proportion of DEGs was equal. In cancerous tissues with major allele, ITGA1 and TNFAIP8L were upregulated, GPR84 gene was downregulated as compared to cancerous tissues with minor alleles. hsa-miR-335-5p, hsa-miR-940, has-miR-23a-3p etc. were the predominant miRNAs. String analysis showed 189 nodes,430 edges, average node degree 4.55, protein enrichment value p<1.0e. The study demonstrates the efficacy of bioinformatics analytic approaches in identifying probable pathogenic genes for HCC associated with hepatitis C. The interaction network identified miRNAs, hsa-miR-335-5p, hsa-miR-940 and genes TREH and TRIM16 as the key genetic markers for HCC.

The aim of the study was to compare sirtuin 1 serum levels in non-insulin dependent diabetics and diabetic nephropathy patients, and evaluate the pattern of polymorphism of SIRT 1 gene in these patients, and find the relation between polymorphism of SIRT1 gene and sirtuin1 serum levels in diabetic nephropathy patients and those with various stages of diabetic nephropathy. Methodology: 108 type-2 diabetic patients without complications as controls and 108 diabetic nephropathy patients as the case group were included in the study. SIRT 1 expression was measured by ELISA, and SIRT1 gene polymorphism was analyzed using the PCR-RFLP method. Results: The mean serum sirtuin 1 level were significantly lower in diabetic nephropathy patients compared to controls (p=0.000). The distribution of genotypes did not conform to Hardy-Weinberg equilibrium. The frequency of the wild-type genotype (AA) was higher in the case group, while the mutant allele (AG+GG) was more prevalent in controls. The distribution of genotypes did not conform to Hardy-Weinberg equilibrium (chi-square =7.203, p=0.027). There was no significant association observed between SIRT1 gene polymorphism and serum sirtuin 1 level in diabetic nephropathy patients(p=0.001). Additionally, no significant difference was found in serum sirtuin 1 level between different stages of diabetic nephropathy based on albuminuria testing and estimated glomerular filtration rate (eGFR)(p=0.33). Conclusion: Patients with diabetic nephropathy exhibited significantly lower serum sirtuin 1 level compared to controls, suggesting a potential role of sirtuin 1 in the pathogenesis of DN. We also conclude that serum SIRT 1 expression may be used as a diagnostic marker. The results indicate a need for further research to better understand the role of SIRT1 in diabetic nephropathy and its potential as a biomarker or therapeutic target for this condition.

Traumatic Brain Injury (TBI) is a multifaceted form of acquired brain damage caused by external force, leading to both structural and functional alterations in the brain. This study investigates the relationship between APOE gene polymorphism and injury severity (GCS) following TBI. Conducted as a cross-sectional study over three years at a tertiary care hospital in Mangalore, the research included TBI patients and healthy controls. Ethical protocols and convenient sampling were followed, with blood samples collected within 48 hours for genetic polymorphism analysis. The study analysed the frequencies of APOE gene genotypes among TBI patients and healthy controls. The most common genotype in TBI patients was e3/e3 (71.8%), followed by e3/e4, e2/e3, e2/e4, e2/e2, and e4/e4. Among healthy controls, e3/e3 was also the most prevalent genotype (64.34%), with no e4/e4 genotype instances. APOE (p=0.77) gene polymorphisms showed no significant deviations from Hardy-Weinberg equilibrium, indicating genetic stability. APOE3 was identified as the common wild-type allele, while APOE2 and APOE4 were classified as mutant alleles. The study assessed the association between APOE gene polymorphism and Glasgow Coma Scale (GCS) scores on admission using various models, including additive, e2 vs. non-e2, e3 vs. non-e3, and e4 vs. non-e4. The findings revealed no significant association between APOE gene polymorphisms and injury severity as measured by GCS scores. The APOE gene polymorphism may not play a significant role in determining injury severity.

Introduction The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function. Methods nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221). Results Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type. Conclusions Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.

Diabetic nephropathy (DN) is the microvascular complication of type-2 diabetes that leads to end-stage renal disease. The angiotensin-converting enzyme gene that is a part of the renin-angiotensin-aldosterone system is considered one of the candidate genes responsible for the genetic predisposition of DN. To compare serum angiotensin converting enzyme (ACE) levels, the pattern of ACE gene polymorphism in patients with type-2 diabetes with or without nephropathy, to find the association between ACE gene polymorphism and various stages of DN, the association between serum ACE levels and various stages of DN. We enrolled 108 patients diagnosed with type-2 diabetes and 108 DN patients. Serum levels were estimated by ELISA and gene polymorphism was performed with gene sequencing. It was found that the serum ACE levels were higher in DN patients than in type-2 diabetics although the p-value was not significant. There was no significant association between serum ACE levels and various stages of nephropathy. The wild (GG genotype) distribution was more predominant in type-2 diabetic patients than in DN patients. There was a significant difference in the frequency of genotype with various stages of nephropathy and estimated glomerular filtration rate which was statistically significant (p = 0.0018). In summary, the study did not find a significant association between serum ACE levels and DN, nor did it observe a substantial impact of ACE gene polymorphism on serum ACE levels in DN patients. The findings also indicated that the ACE gene polymorphism might not have a direct influence on albuminuria stages. However, the wild-type genotype showed a trend toward protection against albuminuria development, while the carrier patients had a higher prevalence of severe albuminuria. Further research with larger sample sizes and longitudinal studies may provide deeper insights into the role of serum ACE levels and ACE gene polymorphism in the development and progression of DN.

The proposed knowledge sharing module, "Advancements in ER+/HER2- mBC: Novel Therapies and Unmet Needs," aims to enhance the understanding of emerging therapies and address the unmet needs in the treatment of ER+/HER2- metastatic breast cancer (mBC). The primary objectives include increasing knowledge of novel therapeutic agents, discussing differentiated mechanisms of action, addressing current unmet needs, and examining the relevance of biomarker testing in treatment plans. A comprehensive needs assessment revealed significant gaps in knowledge and practice among healthcare providers, with a 30% gap in understanding emerging therapies and a 25% gap in applying biomarker testing. The target audience includes oncologists, healthcare providers, clinical researchers, and academics, with approximately 100 direct beneficiaries and thousands of indirect patient beneficiaries. The four-day knowledge sharing module will feature keynote presentations, panel discussions, breakout sessions, expert interviews, and interactive workshops, focusing on emerging therapies, unmet needs, biomarker testing, and dissemination of findings. Apollo Hospitals Education and Research (AHER) will support the knowledge sharing module with its expertise, infrastructure, and collaborative networks, ensuring high-quality content and impactful outcomes. The project will be evaluated through pre- and post-knowledge sharing module surveys, participant feedback, and knowledge assessments, aiming for a 30% improvement in understanding novel therapies and a 25% improvement in biomarker testing application. The educational materials will be made available online for broader access, and findings will be disseminated through professional networks and publications.

Introduction:The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function. Methods:nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221). Results:Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type. Conclusions:Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.