Mohamed E. Elnageeb

The arachidonate 5-lipoxygenase activating protein (ALOX5AP) regulates leukotrienes (LTs) synthesis. LTs are involved in inflammation which is implicated in cardiovascular diseases (CVDs) and stroke. Variations in ALOX5AP gene are associated with CVDs, stroke and others because of their possible effects on ALOX5AP stability and function. In this study we investigated with molecular dynamics (MD) simulation the structural impacts of L12F, A56V, G75R, and G87R variants on ALOX5AP. We employed an array of bioinformatics techniques, including SIFT, PolyPhen-2, PANTHER, SNPs&GO, PhD-SNP, i-Mutant, MuPro, MutPred, ConSurf, and GROMACS. Results showed that the L12F variant increased structural compactness, as indicated by diminished solvent accessibility, a reduced radius of gyration, and a decrease in hydrogen bonding capacity. The A56V variant destabilized the ALOX5AP, demonstrating elevated root mean square deviation (RMSD), augmented solvent-accessible surface area, and diminished ALOX5AP compactness. The G75R and G87R variants exhibited mild effects on ALOX5AP wildtype. However, simulation trajectory snapshots results indicated G75R and G87R variants induce instability leading to structural perturbations of ALOX5AP probably due to the charge of arginine introduced by the G75R and G87R mutation. The G75R and G87R variants potentially influence ALOX5AP dynamics, stability, and function. These results require further verification in future case-control and protein functional studies.

Introduction: Hepatitis B virus (HBV) is a significant cause of chronic hepatitis, leading to liver complications such as cirrhosis and hepatocellular carcinoma. Toll-like receptors (TLRs) are critical in the immune response to HBV. This study investigates the TLR3 1377 C/T polymorphism’s association with clinical outcomes in chronic hepatitis B patients. Materials and methods: A case-control study included 136 participants (66 cases and 70 controls). The patients were categorized as having chronic active or inactive hepatitis B based on serological, biochemical, and molecular parameters. TLR3 1377 C/T polymorphism was analyzed using PCR-RFLP. The correlation between TLR3 genotypes, HBeAg status, liver enzyme levels (ALT and AST), and symptomatic presentation was assessed. Results: Among the 66 cases, the CC genotype was more frequent in males with chronic active hepatitis (14 males, 5 females), but no significant gender-based difference was observed in genotype distribution. A significant association was found between the CC genotype and symptomatic presentation in chronic active cases (P = 0.015). However, no significant association was detected between TLR3 genotypes and HBeAg status (P > 0.05). Elevated ALT and AST levels were more prevalent in chronic active cases. Conclusions: The TLR3 1377 C/T polymorphism, particularly the CC genotype, may influence the clinical presentation of chronic hepatitis B, particularly in symptomatic cases. However, its impact on HBeAg status and overall chronicity appears limited. Further studies are needed to clarify the role of TLR3 polymorphisms in HBV pathogenesis and their potential as therapeutic targets.

The integration of advanced computational methods into precision medicine represents a transformative advancement in healthcare, enabling highly personalized treatment strategies based on individual genetic, environmental, and lifestyle factors. These methodologies have significantly enhanced disease diagnostics, genomic analysis, and drug discovery. However, rapid expansion in this field has resulted in fragmented understandings of its evolution and persistent knowledge gaps. This study employs a scientometric approach to systematically map the research landscape, identify key contributors, and highlight emerging trends in precision medicine. Methods: A scientometric analysis was conducted using data retrieved from the Scopus database, covering publications from 2019 to 2024. Tools such as VOSviewer and R-bibliometrix package (version 4.3.0) were used to perform co-authorship analysis, co-citation mapping, and keyword evolution tracking. The study examined annual publication growth, citation impact, research productivity by country and institution, and thematic clustering to identify core research areas. Results: The analysis identified 4574 relevant publications, collectively amassing 70,474 citations. A rapid growth trajectory was observed, with a 34.3% increase in publications in 2024 alone. The United States, China, and Germany emerged as the top contributors, with Harvard Medical School, the Mayo Clinic, and Sichuan University leading in institutional productivity. Co-citation and keyword analysis revealed three primary research themes: diagnostics and medical imaging, genomic and multi-omics data integration, and personalized treatment strategies. Recent trends indicate a shift toward enhanced clinical decision support systems and precision drug discovery. Conclusions: Advanced computational methods are revolutionizing precision medicine, spurring increased global research collaboration and rapidly evolving methodologies. This study provides a comprehensive knowledge framework, highlighting key developments and future directions. The insights derived can inform policy decisions, funding allocations, and interdisciplinary collaborations, driving further advancements in healthcare solutions.

Metabolic syndrome is a metabolic disorder characterized by hypertension, dyslipidemia, impaired glucose tolerance, and abdominal obesity. Impaired insulin action or insulin resistance initiates metabolic syndrome. The prevalence of insulin resistance is increasing all over the world. Insulin resistance results in the defective metabolism of carbohydrates and lipids, in addition to low-grade chronic inflammation. Insulin resistance is associated with metabolic syndrome, which is a risk factor for a number of pathological conditions, such as Type 2 diabetes (T2D), cardiovascular disease (CVD), nonalcoholic fatty liver disease (NAFLD), and polycystic ovarian syndrome (PCOS). Genome-wide association studies have increased our understanding of many loci linked to these diseases and others. In this review, we discuss insulin resistance and its contribution to metabolic syndrome and these diseases. We also discuss the genetic loci associated with them. Genetic testing is invaluable in the identification and stratification of susceptible populations and/or individuals. After susceptible individuals and/or populations have been identified via genetic testing or screening, lifestyle modifications such as regular exercise, weight loss, a healthy diet, and smoking cessation can reduce or prevent metabolic syndrome and its associated pathologies.

Background: Mutations in the EPOR gene can disrupt its normal signaling pathways, leading to hematological disorders such as polycythemia vera and other myeloproliferative diseases. Methodology: In this study, a range of bioinformatics tools, including SIFT, PolyPhen-2, SNAP2, SNPs & Go, PhD-SNP, I-Mutant2.0, MuPro, MutPred, ConSurf, HOPE, and Interpro were used to assess the deleterious effects of missense nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein structure and function. Furthermore, molecular dynamics simulations (MDS) were conducted to assess the structural deviations of the identified mutant variants in comparison to the wild type. Results: The results identified two nsSNPs, R223P and G302S, as deleterious, significantly affecting protein structure and function. Both substitutions occur in functionally conserved regions and are predicted to be pathogenic, associated with altered molecular mechanisms. The MDSs indicated that while the wild-type EPOR maintained optimal stability, the G302S and R223P variants exhibited substantial deviations, adversely affecting overall protein stability and compactness. Conclusions: The computational analysis of missense nsSNPs in the EPOR gene identified two missense SNPs, R223P and G302S, as deleterious, occurring at highly conserved regions, and having substantial effects on erythropoietin receptor (EPO-R) protein structure and function, suggesting their potential pathogenic consequences.

This study used bibliometric analysis to investigate the research on Attention-Deficit/Hyperactivity Disorder (ADHD) in pediatric populations between January 2014 and January 2024. The Scopus database is utilized to gather a diverse array of scholarly research on this complex ailment. Our objective was to compile a comprehensive dataset on understanding and managing ADHD by selecting specific terms such as “ADHD in Children”, “ADHD Treatment and Management”, and “Attention-Deficit/Hyperactivity Disorder”. We utilized the advanced analytical capabilities of Biblioshiny (bibliometrix R-package) and VOSviewer (VOSviewer version 1.6.19), within our methodological framework, to do network analysis. By conducting this analysis, we were able to examine patterns in publications, author affiliations, the geographic spread of research, and identify influential texts and developing research topics. The findings underscore the collaborative endeavors of medicine, psychology, and neuroscience in tackling the physiological and psychological aspects of ADHD, with a focus on interdisciplinary contributions. The extensive global impact of ADHD research is highlighted by the significant contributions made by countries including the United States, China, the UK, the Netherlands, and Canada. Our data indicates a notable shift towards holistic strategies that encompass socioeconomic, environmental, and behavioral aspects, alongside emerging practices like the utilization of non-invasive brain stimulation techniques in research. This bibliometric study offers a comprehensive view of ADHD research by identifying significant patterns and clusters of themes. It illuminates the shifts in scientific conversation over time and identifies areas that show potential for additional research. The study advocates for ongoing collaboration across various disciplines and nations, emphasizing the significance of innovative strategies to enhance the well-being of those affected by ADHD.

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (K_ATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of K_ATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the K_ATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein–protein interactions, Kir6.2 function, and case-control studies.

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

Type 2 diabetes (T2D) develops from insulin resistance (IR) and the dysfunction of pancreatic beta cells. The AKT2 protein is very important for the protein signaling pathway, and the non-synonymous SNP (nsSNPs) in AKT2 gene may be associated with T2D. nsSNPs can result in alterations in protein stability, enzymatic activity, or binding specificity. The objective of this study was to investigate the effect of nsSNPs on the AKT2 protein structure and function that may result in the induction of IR and T2D. The study identified 20 variants that were considered to be the most deleterious based on a range of analytical tools included (SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, and I-Mut). Two mutations, p.A179T and p.L183Q, were selected for further investigation based on their location within the protein as determined by PyMol. The results indicated that mutations, p.A179T and p.L183Q alter the protein stability and functional characteristics, which could potentially affect its function. In order to conduct a more in-depth analysis of these effects, a molecular dynamics simulation was performed for wildtype AKT2 and the two mutants (p.A179T and p.L183Q). The simulation evaluated various parameters, including temperature, pressure, density, RMSD, RMSF, SASA, and Region, over a period of 100 ps. According to the simulation results, the wildtype AKT2 protein demonstrated higher stability in comparison to the mutant variants. The mutations p.A179T and p.L183Q were found to cause a reduction in both protein stability and functionality. These findings underscore the significance of the effects of nsSNPs (mutations p.A179T and p.L183Q) on the structure and function of AKT2 that may lead to IR and T2D. Nevertheless, they require further verifications in future protein functional, protein–protein interaction, and large-scale case–control studies. When verified, these results will help in the identification and stratification of individuals who are at risk of IR and T2D for the purpose of prevention and treatment.