Mahsa University

We read with great interest the recent Danish Medical Journal article Five major challenges for medical bibliometrics , which provides a timely overview of limitations affecting the interpretation of bibliometric indicators in medical science.The discussion of database accuracy is particularly relevant, as the authors demonstrate that major bibliometric databases produce markedly different coverage for identical publications, raising questions about the reliability of database-derived metrics for academic evaluation and funding decisions.The author's example, showing wide variation in reference coverage across major databases, underscores how bibliometric indicators may depend as much on database choice as on scientific output itself .However, while the authors appropriately emphasise inter-database differences in coverage, we would like to highlight a complementary source of variation: the structural characteristics of bibliographic data within databases and the preprocessing required to analyse them -particularly evident in PubMed-based bibliometrics.PubMed remains an indispensable resource for biomedical bibliometrics owing to its free public accessibility and discipline-specific coverage .However, PubMed-based studies face two important methodological limitations: first, author affiliations are stored as unstructured free-text strings with inconsistent formatting and geographic descriptors ; and second, the preprocessing steps required to clean and interpret these strings are frequently insufficiently documented, creating opacity in analytical workflows .Consequently, for medical bibliometrics, discrepancies attributed solely to database coverage may in part reflect differences in affiliation preprocessing practices, underscoring the need for explicit reporting of data-cleaning and geographic disambiguation procedures.Such opacity has direct consequences: bibliometric results derived from PubMed depend critically on how affiliation data are cleaned, parsed, and interpreted, yet these analytical choices remain largely invisible in published work.Discrepancies attributed to database limitations may therefore partly reflect undocumented preprocessing decisions rather than differences in database coverage

Background: Emergency department nurses, operating within a demanding environment, face significant psychological risks, including professional burnout and empathy exhaustion, which often co-occur and exacerbate each other. Current interventions overwhelmingly target individual nurses, such as through resilience training, while neglecting systematic, organizational-level solutions. Objective: This study proposes an organizational intervention framework grounded in Work Embeddedness Theory. By integrating principles from Conservation of Resources Theory and Empathy Cost Theory, it aims to develop a practical pathway for nursing managers to address the dual crises of burnout and empathy depletion at their systemic roots. Method: Employing a theory-driven design, we constructed a core intervention logic: enhancing organizational embeddedness (via connection, matching, and sacrifice) → supplementing psychological resources → improving mental health outcomes. Empathy Cost Theory was specifically incorporated to fortify interventions within the “connection” dimension. Concrete modules, steps, and mechanisms were delineated for each dimension, forming a structured organizational pathway. Results: We developed an integrated intervention pathway featuring three modules and six specific measures: (1) Strengthening Connection (structured buddy systems; post-traumatic debriefings) to build support networks; (2) Optimizing Match (dynamic skill-task alignment; values clarification workshops) to enhance control and meaning; (3) Increasing Sacrifice (tiered honor/benefit systems; visualizing exit costs) to foster commitment. Each measure’s implementation protocol and theoretical rationale are specified. Conclusion: This study provides a theoretically grounded, actionable organizational intervention pathway. It shifts the management paradigm from repairing individuals to designing supportive systems, offering a blueprint for creating work environments that intrinsically protect and replenish nurses’ psychological resources.

<title>Abstract</title> Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer strongly linked to RIO Kinase 3 (RIOK3), which promotes progression by stabilizing and phosphorylating Focal Adhesion Kinase (FAK). Advances in protein structure prediction, particularly AlphaFold2, have significantly enhanced our understanding of protein dynamics, aiding in the identification of potential inhibitors for targeted therapies. This study used structure-based virtual screening, molecular dynamics simulations, ADMET/toxicity prediction, and in vitro validation to identify potential inhibitors of RIOK3 for PDAC treatment. The 3D structure of RIOK3 was predicted using AlphaFold2 and docked with FDA-approved drugs via AutoDock Vina. Pharmacokinetic and pharmacodynamic properties were assessed with SwissADME, and in vitro validation was performed using MTT assays to assess cell viability and growth inhibition. Four top-scoring compounds were identified, with binding energies between − 11.3 and − 10.4 kcal/mol. Venetoclax showed the most stable complex with RIOK3, followed by Conivaptan and Irinotecan. Drospirenone showed weaker binding. Molecular dynamics simulations and MM/GBSA analysis supported the stability of these complexes. SwissADME and ProTox-II confirmed that the compounds met drug-likeness criteria but exhibited distinct pharmacokinetic and toxicity profiles. In vitro MTT assays showed concentration-dependent growth inhibition in PANC-1 cells, with Conivaptan having the lowest IC₅₀ value. This study identifies RIOK3 as a promising therapeutic target for PDAC, with Venetoclax, Conivaptan, Drospirenone, and Irinotecan as repurposable candidates for further research. Further studies should include biochemical assays, expanded cytotoxicity profiling in multiple PDAC cell lines, and in vivo evaluations to validate RIOK3-targeted therapies for PDAC treatment.

Children’s songs constitute a powerful yet often underexamined medium of moral socialisation in early childhood education. In contemporary Muslim societies, children are increasingly exposed to a plural media environment in which Islamic children’s songs coexist with Western-produced musical content, particularly through digital platforms. While music-based learning has been widely recognised for its cognitive, linguistic, and emotional benefits, comparatively little attention has been given to how different cultural traditions of children’s songs shape moral and religious value formation. This study addresses that gap by offering a qualitative comparative analysis of Islamic and Western children’s songs and examining teachers’ perceptions of their influence on Islamic moral development among young learners in Malaysia. Guided by theories of moral education, socio-cultural learning, and Islamic pedagogy, the study employs thematic content analysis of selected songs alongside survey data from primary and preschool teachers. The findings indicate that Islamic children’s songs consistently integrate moral instruction with religious consciousness, framing ethical behaviour as an expression of faith and accountability to God, whereas Western children’s songs primarily promote general social values within a secular and individualistic framework (Al-Ghazālī, 2004; Barrett, 2016; Lickona, 1991). Teachers reported that moral internalisation was strongest when songs were emotionally engaging and pedagogically mediated through explanation and repetition (Hallam, 2010; Ilari &amp; Sundara, 2009). The study contributes to scholarship on music education and moral development by highlighting the culturally situated nature of musical pedagogy and emphasising the need for intentional mediation of children’s media in Islamic educational contexts.

This systematic review and meta-analysis aimed to investigate the association between ABCG2 polymorphisms and allopurinol response in gout patients. A comprehensive search of Scopus, PubMed, Web of Science, EBSCOhost, and the Clinical Pharmacogenomic Resource was conducted for observational studies up to May 2025. Two authors independently screened studies, extracted data, and assessed study quality using the Newcastle-Ottawa Scale. Pooled odds ratios with 95% confidence intervals were calculated using a random-effects model across codominant, dominant, and recessive genetic models. Statistical heterogeneity was evaluated using the I2 statistic, and sensitivity analyses were performed. Six studies met the inclusion criteria for the review, and five were included in the meta-analysis. A significant association was observed between rs2231142 with poor allopurinol response, but not rs10011796. Sensitivity analyses confirmed the robustness of the findings for rs2231142. The findings suggest that ABCG2 rs2231142 influences allopurinol response. These results underscore the potential of rs2231142 as a predictive genetic marker for allopurinol efficacy and may inform the development of pharmacogenomic guidelines for gout management.This systematic review and meta-analysis was not registered in any protocol registry.

Despite decades of research, Alzheimer’s disease (AD) remains without a curative therapy. While amyloid- and tau-centered approaches have dominated the field, failures of monotherapeutic strategies underscore the need for a broader system-level understanding. Here, this review critically revisits the principal hypotheses of AD pathogenesis, including the amyloid cascade, tauopathy, neuroinflammation, cholinergic dysfunction, oxidative and mitochondrial stress, metal dyshomeostasis, autophagy–lysosomal failure, genetic susceptibility, and infectious triggers. This review synthesizes molecular and cellular evidence from human genetics, neuropathology, and experimental models, correcting common misconceptions and emphasizing interactions between pathways. Neuroinflammation is increasingly recognized as a central hub linking amyloid, tau, and vascular factors, while mitochondrial and lysosomal dysfunctions emerge as amplifiers of proteotoxic stress. Genetic studies highlight apolipoprotein-E ε4 (APOE ε4) as the strongest common risk allele, but also implicate genes involved in endosomal trafficking, lipid metabolism, and immune regulation. Taken together, AD is best understood as a multi-hit disorder in which converging processes, rather than a single driver, dictate disease initiation and progression. This narrative review proposes a systems neurobiology framework that integrates these mechanisms and identifies key points of convergence amenable to therapeutic targeting and biomarker development. Finally, this reappraisal aims to inform future research directions and guide the rational design of multi-target interventions.

As a biofilm-mediated disease, dental caries is primarily attributed to the activity of Streptococcus mutans and Streptococcus sobrinus, key contributors to enamel mineral loss under acidic conditions. Current broad-spectrum antimicrobials disrupt the oral microbiota and carry undesirable side effects, prompting interest in targeted, microbiome-friendly alternatives. This study evaluated the antimicrobial, anti-cariogenic, and cytotoxic properties of pomegranate (Punica granatum) derived compounds, corilagin, ellagic acid, gallocatechin, kaempferol-7-O-glucoside, punicalagin, punicalin, and rutin against cariogenic S. mutans and S. sobrinus, and the commensal S. gordonii. Antibacterial activity was assessed using disc diffusion, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) methods. Anti-virulence effects were evaluated through glycolytic pH drop and cell surface hydrophobicity assays. Cytotoxicity was determined using the brine shrimp lethality assay. Punicalagin, punicalin, and ellagic acid showed strong, selective bactericidal activity against S. mutans and S. sobrinus, with low MICs and MBC/MIC ratios, while sparing S. gordonii. These compounds significantly suppressed acid production, maintaining pH above the critical demineralisation threshold, and reduced surface hydrophobicity in cariogenic strains without affecting the commensal. Most compounds exhibited low toxicity (LC₅₀ > 500 µg/mL), indicating a favourable safety profile. Overall, punicalagin and punicalin demonstrated dual antimicrobial and anti-virulence activity with selective targeting of cariogenic pathogens. These findings support the potential inclusion of punicalagin and punicalin in oral care formulations aimed at preventing dental caries while preserving beneficial oral microbiota. As this is a preliminary screening study, the results should be interpreted cautiously, and further biofilm and host-cell assays are needed to confirm translational potential.

Network pharmacology is a powerful approach for studying complex drug–target interactions and biological pathways. However, existing tools often require extensive manual intervention and lack integrated analysis capabilities. Here, we present NeXus v1.2, an automated platform for network pharmacology and multi-method enrichment analysis including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) that addresses these limitations. NeXus v1.2 enables the seamless integration of multi-layer biological relationships, handling complex interactions between genes, compounds, and plants while maintaining analytical rigor. The platform implements three enrichment methodologies: Over-Representation Analysis (ORA), GSEA, and GSVA, circumventing limitations associated with arbitrary threshold-based approaches. NeXus v1.2 was validated using multiple datasets spanning 111 to 10,847 genes, demonstrating robust scalability and performance across dataset sizes. The platform was initially tested using a representative dataset comprising 111 genes, 32 compounds, and 3 plants, showing consistent performance in processing various relationship patterns, including shared compounds between plants and multitargeted genes. The processing time for this dataset was 4.8 s with peak memory usage of 480 MB. Large-scale validation with datasets up to 10,847 genes confirmed scalability, with linear time complexity and completion times under 3 min. NeXus v1.2 automatically generates comprehensive visualizations, including network maps, enrichment analyses, and relationship patterns, while maintaining the biological context of interactions. The tool successfully processed and analyzed enrichment patterns across multiple functional domains, generating publication-quality visualization outputs at 300 DPI resolution. The platform demonstrated enhanced automation in handling incomplete relationship data and maintaining analytical integrity across different biological layers. Compared to manual workflows requiring 15–25 min, NeXus v1.2 reduced the analysis time to under 5 s (>95% reduction) while ensuring the comprehensive coverage of biological relationships. NeXus v1.2 provides improved automation and integration for network pharmacology analysis, offering an efficient and user-friendly platform for complex biological network analysis. Its modular architecture enables the future integration of AI technologies and expansion into various therapeutic applications.