IntroductionGastric Cancer (GC) is a major global health problem with high incidence and mortality, making it necessary to find new prognostic biomarkers. Glycoprotein Non-Metastatic Melanoma Protein B (GPNMB) has been linked to tumor growth in several cancers, but its role as a prognostic marker in GC remains unclear. MethodsTo explore GPNMB’s expression, immune microenvironment association, and prognostic value in GC, this study integrated bioinformatics (TCGA, GEO, GEPIA) and Immunohistochemistry (IHC) on 73 paired GC/adjacent tissues, with semi-quantitative scoring and statistical analyses (chi-square, Mann-Whitney U, log-rank, multivariate Cox regression). ResultsGPNMB was significantly overexpressed in GC tissues vs. adjacent tissues (IHC: 57 ± 53 vs. 38 ± 39, p = 0.018; consistent with TCGA/GEO). High GPNMB correlated with abnormal immune/stromal scores (F = 6.55, Pr(>F) = 0.000249), increased immunosuppressive cell infiltration, decreased effector T cells, advanced M stage (distant metastasis rate: 24.2% vs. 5.0%, p = 0.017), shorter overall survival (log-rank p = 0.018), and was an independent prognostic factor (HR = 1.52, 95% CI:1.03–2.24, p = 0.035; multi-dataset HR:1.446–1.7, p < 0.05). Females showed a higher proportion of GPNMB (36.4% vs. 17.5% males), but this difference was not significant (p = 0.068). DiscussionOur study confirms GPNMB’s overexpression in gastric cancer via integrated bioinformatics and IHC, linking it to immune microenvironment dysregulation, distant metastasis, and poor prognosis-reinforcing its role as an independent prognostic biomarker and potential immunotherapy target, while noting limitations of small single-center IHC cohort, lack of experimental validation for relevant signaling pathway involvement, and reliance on algorithmic immune infiltration analyses. ConclusionGPNMB is overexpressed in GC, drives progression (especially metastasis), and shortens survival by regulating the immune microenvironment to exacerbate immune escape. It serves as an independent GC prognostic marker for risk stratification and a potential target for combined immunotherapy.
IntroductionPaper money has served as a fomite and vector for centuries, potentially harboring pathogenic microorganisms due to contamination with nosocomial infections, such as biofilm-forming, multidrug-resistant bacteria. This study was conducted to examine environmental contamination of Iraqi current currency notes with bacteria, biofilm-forming genes, and antimicrobial-resistant profiles. MethodsIn this cross-sectional study, 32 Iraqi currency notes were obtained from six commercial sectors (October 2024–February 2025). Bacterial identification was performed using standard methods and the VITEK-2 automated system. Antimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method (CLSI 2024 criteria). PCR amplification was used to detect the icaA gene. Statistical analysis was performed using the chi-square and Fisher's exact tests (p-value < 0.05). ResultsAll 32 currency notes (100%) were positive for contamination, yielding a total of 50 bacterial isolates. Overall, Staphylococcus species were the most frequently isolated organism (n = 29, 58%), with S. epidermidis representing the most common isolate (n = 19, 38%). High rates of resistance were recorded for oxacillin (86.2%) and erythromycin (79.3%). In terms of multi-drug resistance, fifteen (51.7%) isolates were multi-drug-resistant (MDR). The icaA gene was present in 18 of 29 (62.1%) Staphylococcus isolates and was significantly associated with robust biofilm production (p-value < 0.001). DiscussionThe presence of multidrug resistance and the biofilm-associated gene IcaA among the isolates indicated an apparent public health risk. This reflects the highly active, community-wide circulation of antimicrobial-resistant and highly persistent pathogens that survive both antibiotics and host defenses during everyday interactions. ConclusionThe significant burden of these high-risk bacteria on Iraqi currency underscores the urgent need to raise hygiene levels at the community level and to develop effective control strategies to prevent money from serving as a vehicle for pathogen transmission.
Introduction Artificial intelligence (AI) has rapidly emerged as a transformative force in biomedical research, driving advances in data interpretation, diagnostics, and therapeutic strategies. The objective of this study was to conduct a large-scale bibliometric analysis of AI-related biomedical publications indexed in PubMed between 1950 and 2024, with the aim of identifying temporal trends, global research contributions, and thematic focus areas. Methods A retrospective bibliometric analysis was performed on 76,722 PubMed-indexed articles. Data were retrieved using a Python-based pipeline integrating National Center for Biotechnology Information (NCBI) Entrez Programming Utilities and Biopython. Metadata-including PubMed Identifier (PMID), title, abstract, authors, publication date, journal, Medical Subject Headings (MeSH) terms, Digital Object Identifier (DOI), and country of origin-were extracted, cleaned, and compiled into a structured dataset. Articles were analyzed for completeness, publication patterns, geographic distribution, journal outlets, and thematic focus using MeSH keywords. Results AI-related publications remained scarce until 2015, after which output expanded exponentially, reaching 20,135 articles in 2024. The United States contributed the largest share (32.5%, n = 24,958), followed by England (22.5%) and Switzerland (n = 13,936). Thematic analysis revealed machine learning (n = 16,281), deep learning (n = 12,297), and neural networks (n = 7,117) as dominant areas, while AI ethics appeared in only 110 publications. Metadata completeness varied, with notable gaps in MeSH indexing (41.6%), abstracts (9%), and Digital Object Identifiers (DOI) (2.6%). Research was widely disseminated across more than 6,000 journals, with Sensors , Scientific Reports , and Public Library of Science ONE ( PLOS ONE) as leading outlets. Discussion The findings highlight AI’s transition from a peripheral topic to a core pillar of biomedical science, with rapid growth driven by technological advances and global health demands. Despite widespread adoption across multiple disciplines, gaps remain in ethical engagement and equitable global representation. Metadata inconsistencies also pose challenges for systematic synthesis and bibliometric analyses. Conclusion AI has become a central component of biomedical research, characterized by exponential growth, interdisciplinary adoption, and global expansion. However, limited attention to AI ethics and persistent disparities in research representation underscore the need for targeted policy, funding, and governance strategies. Continued bibliometric monitoring is essential to ensure responsible and inclusive integration of AI into clinical and research practice.
Introduction/Objective Quantitative whole-body MRI relies on accurate delineation of multiple anatomical structures, yet manual labeling is slow and variable. We evaluate AISHANet, a deep learning model for multistructure 3D segmentation, and compare it with SegResNet, UNETR, and UNet using the same dataset split and evaluation protocol. The task covers 14 muscle groups and both lungs. Methods The dataset includes 100 whole-body DIXON T1 axial volumes acquired on a 3T Philips scanner (one volume per patient) with reference annotations produced by five expert radiologists. We used 80 volumes for training, 12 for validation, and 8 for testing. Performance was assessed with Dice Similarity Coefficient (DSC), directed Hausdorff distance, Sensitivity, ROC AUC, and F1-score. Metrics were computed per patient, macro-averaged across the 16 structures, and summarized as mean ± standard deviation (SD) across test patients. Results AISHANet obtained the highest overall scores, with a mean DSC 0.871 ± 0.017, directed Hausdorff distance in millimeters 24.11 ± 10.92, sensitivity 0.894 ± 0.047, ROC AUC 0.947 ± 0.024, and F1-score 0.871 ± 0.061. The best performance was observed in larger muscle groups (gluteus, thighs, calves), where DSC exceeded 0.88. Discussion While AISHANet consistently outperformed the baselines, performance decreased in anatomically challenging regions (abdomen and back), which are affected by lower contrast and thinner structures in axial views. Across models, we observed different failure modes: SegResNet tended to produce smoother masks, UNETR reduced isolated false positives, and UNet showed higher sensitivity to anatomical variability. Conclusion Under a controlled, single-protocol comparison, AISHANet provided the highest overall accuracy for multistructure whole-body MRI segmentation in this dataset. Remaining errors in low-contrast and anatomically complex regions motivate future work on improving robustness and validating performance across additional imaging settings, including other MRI scanner manufacturers and additional MRI sequence types.
Introduction The accurate detection of somatic Copy Number Variations (CNVs) is a challenging task in cancer genomics. This study addresses the significant variability in performance and the lack of consensus among computational tools for somatic CNV calling. Methods We conducted a comprehensive benchmark evaluation of four widely used tools - CNVkit, Sequenza, Facets, and ASCAT. Their performance was assessed in terms of recall, precision, reproducibility, and inter-tool concordance using an orthogonally validated real-world dataset derived from the HCC1395 cell line. Results Our analysis revealed considerable differences in tool performance. Facets and Sequenza showed the most balanced accuracy and the highest reproducibility. In contrast, we observed poor consensus among tools, particularly for amplifications, where pairwise concordance values were frequently below 0.6. CNVkit showed high sensitivity for deletions but exhibited critically low and unstable performance for amplifications. Discussion The results show that tool selection is a primary source of variability in CNV studies, which can significantly impact downstream biological interpretation. The high discordance rates, especially for amplifications, highlight the inherent limitations and the risk of false negatives when relying on a single algorithm. Conclusion We conclude that for reliable somatic CNV detection, tools such as Facets or Sequenza are required. We also recommend adopting a consensus-based approach to reduce error rates and improve the quality of findings from individual algorithms.
