Celiac disease (CD) is an autoimmune disorder triggered by gluten in genetically susceptible individuals, and viral infections have been proposed as potential cofactors in its pathogenesis. This study investigated the replication of SEN virus genotypes H and D in CD patients and their association with systemic cytokine levels. A total of 276 participants were enrolled, including 192 CD patients—115 on a gluten-containing diet (GCD) and 77 on a gluten-free diet (GFD)—alongside 84 healthy controls. SEN virus detection was performed using nested PCR, and serum cytokine levels (IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ) were quantified via ELISA. SENV-H was detected in 71.4% of CD patients on a GCD, 66.7% of those on a GFD, and only 19.0% of healthy controls. In contrast, SENV-D was found exclusively in healthy individuals (23.8%) and not in CD patients. CD patients on a GCD exhibited markedly elevated cytokine levels, particularly IL-6 (76.2 ± 12.3 pg/mL), IL-8 (112.0 ± 40.2 pg/mL), and IFN-γ (50.2 ± 15.2 pg/mL), compared to GFD patients and healthy controls. Multivariate logistic regression identified IL-1, IL-2, IL-4, IL-6, IL-8, and IL-10 as significantly associated with active disease (ORs < 1, p < 0.05). Strong to very strong positive correlations were observed between SENV-H positivity and cytokine levels, with IL-6, IL-8, and IL-1 each showing correlation coefficients around 0.99. These results suggest that SENV-H may play a role in promoting or amplifying mucosal immune responses in active CD, whereas SENV-D appears unrelated. The findings highlight a potential interaction between viral replication and immune activation in celiac disease, meriting further mechanistic investigation.

Diabetes mellitus (DM) is a major non-communicable metabolic disorder characterized by persistent hyperglycemia arising from impaired insulin secretion, insulin resistance, or a combination of both. As the global burden of DM continues to rise, understanding its prevalence and associated risk factors in specific populations is critical for the development of effective prevention and management strategies. A cross-sectional study was conducted among 150 residents (75 males and 75 females) attending a tertiary healthcare facility in Mardan, Pakistan. Sociodemographic characteristics, family history, body mass index (BMI), lifestyle behaviors, dietary patterns, psychological stress, and other potential risk factors were assessed using a structured questionnaire, while venous blood samples were collected to confirm the diagnosis of DM. Overall, the prevalence of DM was found to be 34.67% (n=52), with 29.33% (n=44) previously diagnosed and 5.33% (n=8) newly identified during the investigation. A significant sex-related disparity was observed, with prevalence rates of 26.67% (n=20) in males and 42.67% (n=32) in females. Rural residents exhibited a higher prevalence (42.86%, n=33) compared to urban residents (26.03%, n=19). Several risk factors demonstrated a notable association with DM, including advanced age (>60 years: 8.67%, n=13), obesity (12.67%, n=19), low physical activity (26.67%, n=40), smoking (11.33%, n=17), unhealthy dietary patterns (27.33%, n=41), high psychological stress (17.33%, n=26), hypertension (14%, n=21), and a positive family history (27.33%, n=41). The findings indicate an upward trend in the prevalence of DM in the Mardan region. Immediate implementation of targeted interventions, including public health education, lifestyle modification, dietary counseling, and risk factor management, is essential to mitigate the increasing burden of DM in this population.

Diabetic foot ulcers (DFUs), often exacerbated by secondary bacterial infections, are a major complication of diabetes and a leading cause of morbidity. Understanding the spectrum of bacterial pathogens and their profiles of antibiotic resistance is essential for developing effective treatment strategies. This study aimed to identify bacterial isolates from the DFUs and evaluate their susceptibility to commonly used antibiotics. A total of 186 patients with the DFUs were examined at Hayatabad Medical Complex of Peshawar in Pakistan over a three-month period. Samples were collected from infected ulcer sites and cultured with standard microbiological techniques. Bacterial identification was performed with conventional methods, and antibiotic susceptibility testing was then conducted by using the Kirby-Bauer disk diffusion method. Gram-Negative bacteria were predominant, with Pseudomonas aeruginosa, Proteus mirabilis, Acinetobacter spp., Escherichia coli, Klebsiella spp., Enterobacter spp., and Streptococcus pyogenes being the most commonly isolated organisms. Gram-Positive isolates including Staphylococcus aureus and Staphylococcus epidermidis, P. aeruginosa, and Enterobacter spp. showed high sensitivity to Gentamicin, Meropenem, and Imipenem. In contrast, Acinetobacter spp. and Klebsiella spp. exhibited significant resistance, particularly to carbapenems. Staph. aureus was generally sensitive to first-line antibiotics, such as Vancomycin and Rifampicin whereas Staph. epidermidis demonstrated multidrug resistance including pan-drug resistance in some cases. These findings highlighted the complex and resistant microbial profiles of diabetic foot infections, thus emphasizing the importance of the culture-guided antibiotic therapy. The emergence of carbapenem-resistant strains underlined the requisites for continuous surveillance, judicious antibiotic use, and improved infection control strategies to aid the recovery of patients.

Accurate evaluation of tibial mechanical properties is essential for the diagnosis of bone-related disorders, the monitoring of fracture healing, and the optimization of orthopedic rehabilitation outcomes. Conventional diagnostic approaches, including dual-energy X-ray absorptiometry (DXA) and other imaging-based methods, provide valuable information but are limited by radiation exposure, invasiveness, or insufficient sensitivity to early structural changes. To address these limitations, non-invasive vibration-based techniques have been developed as promising alternatives for quantitative, real-time, and radiation-free assessment of tibial biomechanics. Methods such as resonance frequency analysis (RFA), modal analysis, acoustic emission (AE), and laser doppler vibrometry (LDV) have been applied to estimate parameters including bone stiffness, bone mineral density (BMD), and healing dynamics. By introducing controlled vibrational input and recording the tibial response, structural integrity can be characterized, and early indicators of injury, degeneration, or impaired healing can be detected. Recent advances in high-resolution sensors, signal processing algorithms, and wearable technologies have enhanced the sensitivity and applicability of these techniques, while the integration of machine learning has enabled more robust interpretation of complex biomechanical signals. Despite these advances, significant challenges remain, including inter-patient variability, soft tissue damping effects, and the absence of standardized testing protocols. Furthermore, the clinical translation of vibration-based diagnostics requires validation against established imaging modalities and the development of predictive, individualized models of bone health that integrate artificial intelligence (AI) and multimodal data. Current evidence suggests that vibration analysis has the potential to offer a non-invasive and personalized approach to skeletal monitoring. Future research should focus on addressing methodological limitations, improving standardization, and advancing the integration of vibration assessment into routine orthopedic practice and precision medicine frameworks.

The transformative potential of artificial intelligence (AI) and machine learning (ML) in healthcare has been increasingly recognized, particularly in medical image analysis and predictive modeling of patient outcomes. In this study, a novel convolutional neural network (CNN) architecture incorporating customized skip connections was introduced to enhance feature extraction and accelerate convergence during medical image classification. This model demonstrated superior performance compared with conventional architectures such as Residual Network with 50 layers (ResNet-50) and Visual Geometry Group with 16 layers (VGG16), achieving an accuracy of 96.5% along with improved precision, recall, and area under the receiver operating characteristic curve (AUC-ROC). In parallel, patient readmission risks were predicted using an optimized random forest algorithm, which, after hyperparameter tuning, attained a robust AUC-ROC value of 0.91, thereby underscoring its stability and predictive reliability. The integration of these approaches highlights the ability of AI and ML systems to deliver more accurate diagnoses, anticipate potential health risks, and recommend personalized treatment strategies, ultimately enabling faster and more precise clinical decision-making. Despite these advancements, challenges persist regarding data privacy, interpretability of AI-driven decisions, and the ethical use of patient information. Addressing these limitations will be critical for the broader adoption of AI-enabled healthcare systems. The findings of this study reinforce the role of advanced AI and ML frameworks in improving healthcare delivery, optimizing the use of limited resources, and reducing operational costs, thereby supporting more effective patient care.