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Volume 3, Issue 2, 2025

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The pervasive integration of plastic materials into contemporary society has yielded substantial societal and economic advantages, yet has concurrently precipitated growing toxicological concerns with significant implications for human health. This study critically examines the multifaceted health impacts associated with chronic exposure to microplastics and plastic-derived chemical additives, including phthalates, bisphenol A (BPA), flame retardants, and heavy metals. Through a comprehensive synthesis of recent toxicological and epidemiological evidence, the mechanisms through which these contaminants disrupt endocrine regulation, impair immune homeostasis, and compromise cellular function are elucidated. Cumulative exposure has been linked to heightened incidences of hormone-related disorders, carcinogenesis, metabolic syndromes, and neurodevelopmental abnormalities. Recent advances in analytical detection techniques have confirmed the systemic distribution and bioaccumulation of microplastic particles across human organs. Environmental vectors—such as air, water, soil, and food contamination—serve as major conduits of microplastic exposure, amplifying indirect toxicological risks through trophic transfer and persistent environmental deposition. Despite the mounting evidence of harm, current regulatory frameworks remain fragmented and insufficiently stringent, reflecting a lag between scientific understanding and policy enforcement. Addressing these deficiencies requires a paradigm shift from reactive risk management toward proactive prevention, encompassing the development of biodegradable materials, reinforcement of global monitoring systems, and the establishment of harmonized exposure thresholds. The synthesis presented herein highlights the urgent necessity of redefining plastic consumption and waste management practices to safeguard both human and ecological health, advocating for integrative strategies that align environmental sustainability with public health protection.

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Diabetic foot infections (DFIs) are a major cause of morbidity and lower-limb amputations among individuals with diabetes mellitus. Inappropriate empirical antibiotic use contributes to treatment failure and elevated amputation risk. This observational study, conducted across six hospitals in Khyber Pakhtunkhwa (KP), Pakistan, involved 341 patients with clinically diagnosed DFIs. The objectives were to evaluate antibiotic efficacy, treatment outcomes, and risk factors for amputation, and to develop a visual risk stratification model correlating antibiotic response with amputation risk. The cohort exhibited a significant male predominance (64.5%, p = 0.003), with the highest prevalence among patients aged 41–50 years (36.7%). Most participants were insulin-independent (92.7%, p < 0.0001). Infection severity was mild in 28.7%, moderate in 47.8%, and severe in 23.5% of cases. Clinical outcomes included complete recovery (39.3%), improvement (31.7%), progression (19.9%), and amputation (9.1%). High-efficacy antibiotics included Levofloxacin (Levaquin, 100%), Colistin (100%), and Linezolid (Zyvox, 86.6%), whereas Ceftriaxone (Cefzone, 33.3%), Ampicillin/Sulbactam (Penro, 38.3%), and Clindamycin (Cleocin HCl, 26.6%) demonstrated limited therapeutic benefit. The visual stratification model showed that exposure to low-efficacy antibiotics significantly increased amputation risk. Logistic regression identified severe baseline infection (odds ratio (OR) ≈3.2), poor glycemic control (OR ≈ 1.9), and treatment with low-efficacy antibiotics (OR ≈ 2.8) as independent predictors of unfavorable outcomes. This study highlights the need for region-specific antibiotic stewardship, continuous resistance surveillance, and evidence-based treatment protocols. The proposed visual model offers a practical framework for guiding empirical therapy and reducing amputation rates in DFI management.

Open Access
Research article
Hybrid Deep Learning Architecture for Automated Chest X-ray Disease Detection with Explainable Artificial Intelligence
b. vivekanandam ,
kambala vijaya kumar ,
jagadeeswara rao annam
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Available online: 05-08-2025

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Deep learning (DL) has increasingly been adopted to support automated medical diagnosis, particularly in radiological imaging where rapid and reliable interpretation is essential. In this study, a hybrid architecture integrating convolutional neural network (CNN), residual networks (ResNet), and densely connected networks (DenseNet) was developed to improve automated disease recognition in chest X-ray images. This unified framework was designed to capture shallow, residual, and densely connected representations simultaneously, thereby strengthening feature diversity and improving classification robustness relative to conventional single-model or dual-model approaches. The model was trained and evaluated using the ChestX-ray14 dataset, comprising more than 100,000 X-ray images representing 14 thoracic disease classes. Performance was assessed using established metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). A classification accuracy of 92.5% was achieved, representing an improvement over widely used machine learning (ML) and contemporary DL baselines. To promote transparency and clinical interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated, enhancing clinician confidence in model decisions. The findings demonstrate that DL-based diagnostic support systems can reduce diagnostic uncertainty, alleviate clinical workload, and facilitate timely decision-making in healthcare environments. The proposed hybrid model illustrates the potential of advanced feature-integration strategies to improve automated radiographic interpretation and underscores the importance of explainable artificial intelligence (XAI) in promoting trustworthy deployment of medical artificial intelligence (AI) technologies.

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Drug-therapy problems (DTPs) are a major concern in cardiovascular disease (CVD) management, particularly among older adults exposed to polypharmacy, drug-drug interactions (DDIs), non-adherence, unnecessary drug therapy, and adverse drug reactions (ADRs). This prospective observational study, conducted in the cardiology unit of Saidu Group of Teaching Hospital (SGTH), Swat, Pakistan, evaluated 350 inpatients admitted from January to March 2022. Data were analyzed using SPSS v26 and GraphPad Prism v5.01, employing chi-square tests, t-tests, and binary logistic regression (p < 0.05). A total of 323 patients (92.29%) experienced at least one DTP, accounting for 1,252 events. DDIs were the most common DTP, followed by unnecessary drug therapy and ADRs. Age was a significant predictor, with the highest odds of DTPs observed in patients aged 61–70 years; a slight, non-significant increase was noted among females and those with polypharmacy (>5 medications). DTP frequency correlated positively with age and the number of prescribed medications, particularly among individuals aged 31–70 years. The findings indicate a high burden of preventable DTPs in CVD management, dominated by DDIs and unnecessary drug therapy. The incorporation of multidisciplinary medication-review teams, strengthened clinical decision support systems (CDSS), and routine prescription audits is recommended to mitigate DTPs and enhance the safety and precision of cardiovascular pharmacotherapy.

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Microplastics, commonly defined as plastic particles smaller than 5 mm, have emerged as pervasive contaminants across natural and anthropogenic systems, constituting a complex global stressor with environmental, economic, and public health implications. Since the term microplastic was first introduced in 2004, an expanding body of research has revealed the extensive diversity and abundance of these particles, which originate from both the fragmentation of larger plastic debris (secondary microplastics) and the intentional production of microscopic polymers (primary microplastics) for use in cosmetics, industrial abrasives, and synthetic textiles. Despite substantial scientific attention, the absence of a universally accepted classification framework–particularly with respect to size ranges, polymer composition, and source attribution–continues to hinder harmonized monitoring and regulatory action. Microplastics have been detected in marine and freshwater environments, terrestrial soils, atmospheric fallout, and remote regions, demonstrating their capacity for long-range transport through hydrological, atmospheric, and biogeochemical processes. Ecologically, exposure has been shown to impair feeding behavior, induce physical obstruction, and compromise reproductive success across multiple trophic levels, from planktonic organisms to higher vertebrates. Chemically, microplastics function as dynamic carriers for persistent organic pollutants, heavy metals, and microbial assemblages. Human exposure has been increasingly documented through dietary intake, drinking water consumption, and inhalation of airborne particles. Effective mitigation of microplastic pollution will require coordinated international policy frameworks, advances in materials innovation and waste management, standardized analytical methodologies, and sustained public engagement to address both sources and impacts of microplastic contamination.

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