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GA₃ and NAA foliar application enhances fruit set, quality, and storage performance in ‘Fengtang’ plum by regulating endogenous hormonal balance
AI-powered risk factor analysis and development of a predictive model for lymphovascular invasion in bladder urothelial carcinoma
Cable-net deployment robot for automated protective barrier installation on the transmission lines
Programmed cell death ligand 1(PD-L1) association in metastatic and non-metastatic oral squamous cell carcinoma: clinicopathologic and immunohistochemical study
Abstract Oral Squamous Cell Carcinoma (OSCC) is considered a highly immunosuppressive malignancy largely mediated by the Programmed Cell Death 1/Programmed Cell Death Ligand 1(PD-1/PD-L1) axis. The interaction between PD-L1 expressed on tumor cells and PD-1 receptors on T-cells results in T-cell dysfunction, exhaustion, and immune evasion within the tumor microenvironment. This study aimed to evaluate PD-L1 expression in primary non-metastatic OSCC, primary metastatic OSCC, and nodal metastatic OSCC, as well as to investigate its association with different available clinicopathological parameters. Immunohistochemical staining was performed to retrospectively evaluate PD-L1 expression in 30 archival paraffin-embedded OSCC specimens retrieved from the Department of Oral Pathology, Faculty of Dentistry, and the Oncology Center, Faculty of Medicine, Mansoura University. PD-L1 immunoreactivity was evaluated using a semi-quantitative scoring system based on both the staining intensity and the percentage of positively stained cells. The percentage of immunopositive cells was scored as stated: 0 (0%); 1 (< 25%); 2 (25–50%); 3 (50–75%); and 4 (> 75%). Staining intensity was graded as follows: (0 = negative); (1 = weak); (2 = moderate); and (3 = strong). A combined immunoreactivity score was calculated by adding the percentage and the intensity for each case (range 0–7). The final score was categorized as follows: 0 (negative); 1–3 (weak); 4–7 (strong). Statistical analysis was conducted to determine significant differences and correlations between PD-L1 expression and clinicopathological parameters using the Chi-square test, Monte Carlo test, one-way ANOVA, Student’s t-test, and Fisher’s exact test. The p-value < 0.05 was considered statistically significant. PD-L1 immunopositivity was detected in all OSCC cases (100%). A statistically significant difference was observed among the different studied groups ( p < 0.001), with the strongest PD-L1 expression detected in both primary metastatic and nodal metastatic OSCC. Strong PD-L1 expression showed a significant association with patient age ( p = 0.024). Additionally, a significant correlation was identified between PD-L1 expression and the depth of tumor invasion ( p < 0.001). PD-L1 expression may have a potential role in tumor progression of OSCC.
Interpretable deep learning to predict one year glycemic control in type 1 diabetes using real world data
Targeted long-read sequencing with adaptive sampling enables the integrated genomic and epigenomic profiling of imprinting disorders
Design, development, and performance evaluation of a portable chilli seed extractor based on crop-specific engineering parameters
Prevalence and clinical-radiological features of hand osteoarthritis in individuals with knee osteoarthritis
Abstract Hand osteoarthritis (HOA) frequently coexists with knee osteoarthritis (KOA), yet its clinical and radiographic burden in ageing adults with KOA remains under-recognised. To determine the prevalence and joint-specific distribution of clinical, radiographic, and symptomatic HOA in individuals with KOA; to describe associated functional characteristics; and to assess whether HOA severity is related to KOA severity. A descriptive cross-sectional study was conducted among adults ≥ 40 years with tibiofemoral KOA (KL grades 1–4). HOA was evaluated using American College of Rheumatology criteria, hand radiographs (KL ≥ 2), and symptom-based definitions. Assessments included VAS pain, WOMAC, DASH, AUSCAN, grip strength, and pinch strength. Statistical analyses used Mann–Whitney U, Kruskal–Wallis, and χ² tests (α = 0.05). Among 108 participants (mean age 62.1 ± 11.0 years; 71.3% female), 16.7% reported hand symptoms. The prevalence of clinical, radiographic, and symptomatic HOA was 9.3%, 13.0%, and 8.3%, respectively. PIP and DIP joints were most frequently affected, with no MCP involvement. Knee KL grades clustered at grades 3–4 and were associated with significantly worse WOMAC, DASH, and AUSCAN scores. Symptomatic HOA was associated with reduced grip strength, while pinch strength remained unaffected. No significant association was observed between knee and hand KL grades. HOA is a common but under-recognised comorbidity in individuals with KOA, demonstrating predominant PIP/DIP involvement and measurable functional impact. The absence of structural correlation between knee and hand OA highlights OA as a multisite, heterogeneous degenerative condition of ageing. Routine hand assessment in KOA clinics may support earlier identification and targeted management of HOA.
Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation
Defects under insulation evaluation using convolutional neural network-based microwave technique
Enhancing brain tumor detection through deep learning and explainable AI techniques
Abstract Brain tumors are a leading cause of cancer-related mortality, and manual MRI screening remains time-consuming and observer-dependent. Deep learning (DL) offers automated detection, but clinical translation requires rigorous validation and interpretability. This study introduces a DL framework for brain tumor detection that addresses two major challenges in medical AI: limited dataset availability and lack of interpretability. Preliminary experiments identified InceptionV3 optimized with Nadam as the optimal architecture. To ensure robust validation, this model was retrained using patient-wise stratified fivefold cross-validation on 90% of the data incorporating augmentation and minority oversampling to prevent data leakage. This achieved an overall accuracy of 98.3 ± 0.9%. The final model was then trained on the entire development set using the optimal configuration, thereby leveraging all available labeled data to maximize learning capacity and enhance generalization. Performance evaluation was conducted on three levels: (i) a held out internal test set (10% of the data) for internal assessment, (ii) an external dataset of 3000 unseen images for independent validation, and (iii) quantitative explainable AI (XAI) analyses performed on both internal and external test datasets. The proposed model achieved perfect classification metrics on the internal test set, with 100% accuracy and minimal loss (0.01), and demonstrated strong generalizability on the external dataset with 96% accuracy and minimal loss (0.11). Quantitative XAI analysis demonstrated high faithfulness (Grad-CAM vs. occlusion sensitivity correlation exceeded 0.8), causal importance (top-10% occlusion drop 44% vs. 9% for random occlusion), and specificity to learned weights (Spearman correlation ≈ − 0.01). The proposed pipeline establishes a rigorous, transparent framework for data-limited medical imaging, demonstrating high diagnostic performance with clinically aligned explanations and providing a reliable foundation for trustworthy AI in brain tumor detection.
An integrated environmental modelling and decision-support framework for climate-resilient management of Nepeta persica boiss. under climate change
Physical performance and DEXA-derived body composition in adults with Parkinson’s disease participating in a community-based exercise program and community-dwelling older adults: a cross-sectional study
Design and assessment of amino acid-based gemini catanionic niosomes for dual-drug delivery of anticancer drug combination: a comprehensive computational study
Study on the properties of TiC, Nano-CaCO3, and steel fiber reinforced concrete based on RSM-BBD: optimization of mechanical properties
Continental-scale assessment of spatial food market accessibility in Africa using open geospatial data
Abstract Food market accessibility is a critical yet underexplored dimension of food systems, particularly in low- and middle-income countries. In this paper, we present a continent-wide assessment of spatial food market accessibility in Africa, integrating open geospatial data from OpenStreetMap and the World Food Programme. We compare three complementary metrics: travel time to the nearest market, market availability within a 30-minute threshold, and an entropy-based measure of spatial distribution, to quantify accessibility across diverse settings. We find pronounced disparities in accessibility: rural and economically disadvantaged populations face substantially longer travel times and reduced market availability, with some areas requiring several hours of travel. These accessibility patterns align with socioeconomic stratification, as measured by the Relative Wealth Index, and moderately correlate with food insecurity levels, assessed using the Integrated Food Security Phase Classification. Overall, results suggest that access to food markets reflects broader geographic and economic inequalities and plays a relevant role in shaping food security outcomes. Despite limitations related to incomplete and spatially heterogeneous market data coverage, this framework provides a scalable, data-driven approach for identifying relative structural market accessibility gaps, supporting equitable infrastructure planning and spatially informed food security analyses across diverse African contexts.