Search Results (194)

Drug recall is a critical regulatory mechanism implemented to protect public health by removing defective, unsafe, or non-compliant pharmaceutical products from the market. Despite stringent regulatory approval processes, issues related to manufacturing defects, contamination, labeling errors, stability failures, and post-marketing safety concerns may lead to drug recalls. Regulatory authorities across the world, including the Central Drugs Standard Control Organization (CDSCO), the United States Food and Drug Administration (US FDA), the European Medicines Agency (EMA), and other national agencies, have developed structured recall guidelines and rapid alert systems to ensure timely withdrawal of defective products. Drug recalls are typically classified based on the level of health risk and may be executed at different levels of the distribution chain, including wholesale, retail, and consumer levels. Effective recall management involves risk assessment, recall communication, product traceability, documentation, and recall effectiveness checks. Pharmacovigilance systems also play an important role in identifying adverse drug reactions and quality defects that may lead to product recalls. This review article provides a comprehensive overview of drug recall systems, including causes of recalls, regulatory frameworks in India and other countries, recall classification, recall procedures, rapid alert systems, and global recall trends. The article also discusses challenges in recall implementation and provides recommendations to strengthen drug recall systems and regulatory coordination worldwide. The review additionally summarizes major official sources of recall information, including recall alerts, safety communications, and regulatory databases maintained by the Food and Drug Administration (FDA), EMA, CDSCO, Medicines and Healthcare products Regulatory Agency (MHRA), and World Health Organization (WHO), and provides a comparative global perspective on contemporary pharmaceutical recall practices. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

This study presents a mathematical optimization framework for spoken language recognition (SLR) based on the integration of deep learning, metaheuristic optimization, and neuro-fuzzy classification components into a single recognition pipeline. The problem is formulated as high-dimensional nonlinear optimization task, where the objective is to maximize classification performance through optimal parameter selection and feature representation. Audio signals are transformed into spectrogram images, and discriminative features are extracted using an SE-DenseNet architecture. To address the challenges of hyperparameter tuning, the Golden Jackal Optimization (GJO) is employed to explore the search space and identify near-optimal configurations. The extracted features are subsequently classified using an adaptive neuro-fuzzy learning system (ANFLS), enabling improved decision boundaries under uncertainty. The proposed framework is evaluated on a dataset comprising three languages. Experimental results demonstrate an average accuracy of 99.26% and an F-score of 98.86% on the considered dataset, indicating strong in-domain performance compared with conventional machine learning and deep learning models. The results validate the effectiveness of the proposed optimization-driven formulation and highlight its potential for solving complex pattern recognition problems in high-dimensional spaces. Full article

Macrophage-derived extracellular vesicles (EVs) have emerged as promising biomimetic platforms for targeted cancer drug delivery due to their biocompatibility, immune-modulatory properties, and tumor-homing capabilities. Among macrophage subtypes, M1-polarized macrophages exhibit potent anti-tumor functions characterized by pro-inflammatory cytokine secretion, improved antigen presentation, and the ability to remodel the tumor microenvironment (TME). Utilizing these properties, M1-polarized macrophage-derived EVs serve as cell-free therapeutic systems capable of delivering bioactive cargo while simultaneously promoting anti-tumor immune responses. However, the clinical application of natural EVs is limited by low yield, heterogeneity, and challenges in large-scale production. Artificial nanovesicles (ANVs) have been developed to address these limitations, offering improved scalability, compositional control, and reproducibility. This review provides an overview of macrophage differentiation and polarization, with a focus on the immunological profile and anti-tumor mechanisms of M1-polarized macrophages. It further discusses current methodologies for EV isolation and ANV generation, along with cargo loading strategies that balance encapsulation efficiency and vesicle stability. In addition, this review also emphasizes their targeting approaches, cellular uptake pathways, and the intracellular trafficking mechanisms that influence delivery efficiency and therapeutic outcomes. Key challenges, including standardization, biological barriers, and functional consistency, are critically evaluated. Emerging strategies that integrate vesicle engineering with personalized medicine underscore the potential of these systems to advance precision oncology. Full article

BACKGROUND: Overhead athletes are at increased risk of shoulder dysfunction due to repetitive, high-velocity movements that can disrupt scapular muscle activation patterns. Whole-body vibration (WBV) has been proposed as a training modality to enhance neuromuscular activation, but its effects on scapular muscle activity and activation timing remain unclear. METHODS: This randomized controlled trial investigated the effects of WBV-assisted push-up training on scapular muscle activation and onset latency in university-level overhead athletes. Forty participants were randomly assigned to a WBV group or a control group performing identical push-up exercises without vibration for four weeks. Surface electromyography was used to assess normalized muscle activation (%MVIC) and activation latency of the upper trapezius (UT), serratus anterior (SA), and lower trapezius (LT) before and after the intervention. A 2 × 2 mixed-model ANOVA was applied for statistical analysis. RESULTS: Significant time × group interactions were found for muscle activation in LT and SA (p < 0.01). The WBV group demonstrated substantially greater increases in activations in these muscles compared with the control group, with the largest improvements observed in the serratus anterior. No statistically significant between-group differences were identified for muscle onset latency (p > 0.05). CONCLUSIONS: Adding WBV to push-up training significantly enhances key scapular muscle activation in overhead athletes but does not significantly affect muscle onset latency. WBV-assisted push-ups may act as a practical, low-load strategy to improve scapular muscle recruitment and potentially reduce the risk of sports-related shoulder injuries and pain in overhead athletes. Full article

Understanding the depth and severity of corrosion is vital for evaluating the long-term durability and economic performance of Zn-based structures. In this study, a machine learning (ML) framework was applied to forecast the corrosion depth of zinc under varying environmental circumstances. A dataset consisting of 300 samples compiled from previously published atmospheric corrosion studies under various environmental conditions was used to develop and evaluate the machine learning models. Seven ML algorithms were developed by integrating different environmental constraints such as temperature, time of wetness (TOW), SO₂ concentration, Cl⁻ concentration, and exposure time as input parameters. The models were trained using cross-validation and hyperparameter optimization to ensure robust predictive performance and minimize overfitting. The Random Forest (RF) model confirmed superior predictive performance with an R² of 96.4% and RMSE of 0.642 µm among all used models. The predictive ability of the optimized RF model was further confirmed using five new environmental systems, attaining excellent agreement with predicted values (R² = 97.9%, RMSE = 0.87 µm). Model interpretability analysis using SHAP (SHapley Additive exPlanations) discovered that exposure time and SO₂ concentration are the most significant parameters leading zinc corrosion behaviour. The developed ML framework provides interpretable insights into the influence of environmental parameters on atmospheric zinc corrosion behaviour and provides a reliable tool for forecasting corrosion depth. These findings highlight the potential of ML approaches to support corrosion mitigation strategies and accelerate materials design by reducing reliance on conventional trial-and-error experimentation. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Interfacial coupling between CoFe₂O₄ (CFO) nanoparticles and oxidatively functionalized multi-walled carbon nanotubes (MWCNTs) enables controlled modulation of structural, optical, and spin dynamic properties in CFO–MWCNT nanocomposites. The solvothermal synthesis promotes nucleation of CFO on –COOH/–OH functional groups, ensuring uniform anchoring along the nanotube surface. X-ray diffraction confirms a cubic spinel phase with lattice expansion from 8.385 Å to 8.410 Å and crystallite growth from 18 nm to 25 nm, reflecting strain transfer and partial nanoparticle coalescence at the carbon interface. The observed bandgap narrowing from 2.72 eV to 2.50 eV, confirmed via Tauc plot analysis, is attributed to localized defect states induced by charge delocalization and orbital hybridization at the interface of the CFO–MWCNT boundary. DC magnetometry reveals a reduction in saturation magnetization from 46 emu/g to 35 emu/g due to diamagnetic dilution and interfacial spin canting, while coercivity decreases from 852 Oe to 841 Oe, indicating modified pinning and domain-wall dynamics associated with exchange-coupled interfaces. Ferromagnetic resonance measurements show a resonance field shift from 3495 G to 3500 G and an increase in the Landé g-factor from 1.97 to 2.00, signifying altered spin–orbit coupling and enhanced local magnetic perturbations. The spin–lattice relaxation time increases from 1.41 ns to 1.59 ns, demonstrating suppressed phonon-mediated relaxation and improved spin coherence across the hybrid network. Spin density rises from 3.72 × 10²² to 4.58 × 10²² spins/g, confirming an increase in unpaired electrons generated by orbital asymmetry at the interface. The anisotropy field and effective magnetocrystalline anisotropy constant exhibit pronounced modulation, evidencing strengthened exchange stiffness and altered Co²⁺/Fe³⁺ superexchange pathways. These results establish CFO-MWCNT nanocomposites as tuneable platforms for spintronic logic elements, high-frequency microwave attenuation, and magneto-optical device architectures. Full article

Brain cancer detection is dependent on multiple diagnostic techniques. Histopathological diagnosis, although the most effective, requires the extraction of cancer cells, which is very risky and painful for a patient. Another popular noninvasive image-based diagnosis technique is magnetic resonance imaging (MRI). Brain diagnosis data based on MRI scans are highly sensitive and private. This study proposes a single-to-multimodal transformation technique that generates synthetic histopathological data from expert-labelled brain MRI datasets using transfer learning techniques. Furthermore, to preserve a patient’s privacy, an encryption module is used to encrypt the MRI image data and the respective histopathological notations. The Kruskal–Wallis statistical test is also used to analyze the radiogemomics dataset. The trained module is also encrypted, only to be accessed by authorized medical personnel. The transfer learning modules (CNN-based deep learning model, ViT, Resnet101, and YOLOv8) are used here and achieved $99.60\%$ accuracy. Full article

Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient processing and reliable analysis. In recent years, Generative Adversarial Networks (GANs) have emerged as transformative deep learning paradigms, demonstrating strong capabilities in data generation, augmentation, feature learning, and representation modeling. Consequently, the integration of GANs into HSI analysis has gained substantial research attention, resulting in a diverse range of architectures tailored to HSI-specific tasks. Despite these advances, existing survey studies often focus on isolated problems or individual application domains, limiting a comprehensive understanding of the broader GAN–HSI landscape. To address this gap, this paper presents a comprehensive review of GAN-based hyperspectral imaging research. The review systematically examines the evolution of GAN–HSI integration, categorizes representative GAN architectures, analyzes domain-specific applications, and discusses commonly adopted hyperparameter tuning strategies. Furthermore, key research challenges and open issues are identified, and promising future research directions are outlined. This synergy addresses critical hyperspectral data analysis challenges while unlocking transformative innovations across multiple sectors. Full article

(1) Background: Ventricular arrhythmias (VAs) are a leading cause of morbidity and mortality in ischemic and non-ischemic heart disease. While automated implantable cardioverter–defibrillators (AICDs) are standard treatment for high-risk patients, predicting future VA post-implantation remains limited. This study evaluated echocardiographic and strain parameters for predicting VA risk in AICD recipients. (2) Methods: This retrospective cohort study included patients who underwent AICD implantation at Westmead Hospital, New South Wales, Australia (January 2014–May 2024). Pre-implant transthoracic echocardiograms (TTEs) were analysed for structural and functional parameters, including left-ventricular (LV) ejection fraction (LVEF), LV global longitudinal strain (GLS), mechanical dispersion (MD), and delta contraction duration (DCD). VA events, defined as appropriate AICD shock or anti-tachycardia pacing, were identified from electronic medical records and device checks. Univariate and multivariate Cox regression analyses were performed. (3) Results: Among 242 patients, 98 experienced VA events. Increased LV end-diastolic diameter, indexed LV mass, and right-ventricular basal diameter were associated with VA events (p < 0.05), whilst LVEF and GLS were not. LV dyssynchrony was greater in affected patients (MD 69.2 ms vs. 63 ms, p = 0.036; DCD 288.8 ms vs. 246.4 ms, p = 0.010). DCD was an independent predictor of VA events (HR 1.003; 95% CI: 1.000–1.006; p = 0.022). (4) Conclusions: DCD may improve risk stratification in AICD patients. Full article

The alarming increase in the use of chemically driven pesticides for enhanced crop productivity has severely affected soil fertility, ecosystem balance, and consumer health. Inadequate handling protocols and ineffective remediation strategies have led to elevated pesticide concentrations, contributing to human respiratory and metabolic disorders in humans. In the current context, where agricultural activities and pesticide applications are intertwined, strong and sustainable remediation strategies are essential for environmental protection without sacrificing crop productivity. Various bio-inspired methods have been reported, such as phytoremediation, bioremediation, and in situ remediation; however, limited success has been observed with either single or combined approaches. Consequently, biopolymer biomanufacturing, nanoparticle-based bioengineering, and computational biology for improved understanding of mechanisms have been revisited to incorporate updated methodologies that detail the fate and action of harmful chemical pesticides in agriculture. An in silico mechanistic approach has been emphasized to understand the molecular mechanisms involved in agricultural pesticides’ degradation using nanomaterials. A roadmap has been created by integrating cutting-edge machine learning techniques to develop nature-inspired sustainable agricultural practices and contaminant disposal methods. This review represents a pioneering effort to explore the roles of wet-lab chemistry and in silico methods in mitigating the effects of agricultural pesticides, providing a comprehensive strategy for balancing environmental sustainability and agricultural practices. Full article

This study investigates the optimization of electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al₂O₃ nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz). Analysis of Variance (ANOVA) was used to statistically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were implemented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al₂O₃-enhanced dielectrics. This investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality. Full article

Rapid urbanization in semi-arid heritage cities is accelerating land use/land cover (LULC) transitions, with critical implications for local climate regulation, surface energy balance, and environmental sustainability. This study investigates Jaipur, Jodhpur, and Udaipur (Rajasthan, India) between 2018 and 2024 to assess the influence of spatio-temporal dynamics of LULC on urban surface metrics. Multi-temporal satellite datasets were used to derive the index-based built-up index (IBI), surface urban heat island intensity (SUHI), Albedo, urban thermal field variance index (UTFVI), and bare soil index (BSI). The results reveal substantial built-up expansion—most pronounced in Udaipur (+26.7%)—coupled with vegetation loss (up to −23.8% in Jaipur) and progressive albedo decline (Sen’s slope ≈ −0.002 yr⁻¹). These transformations highlight suppressed surface reflectivity and enhanced heat absorption. A key and novel finding is the emergence of a counter-intuitive surface urban cool island (SUCI) effect, whereby urban cores exhibited daytime cooling and nighttime warming relative to rural surroundings. This anomaly is attributed to the rapid heating and poor nocturnal heat retention of bare, sparsely vegetated rural soils, contrasted with the thermal inertia and shading of urban surfaces. By documenting negative SUHI patterns and explicitly linking them to LULC trajectories, this study advances the understanding of urban climate dynamics in semi-arid contexts. The findings underscore the need for climate-sensitive planning—strengthening peri-urban green belts, regulating impervious expansion, and adopting albedo-enhancing construction materials—while safeguarding cultural heritage. More broadly, the study contributes empirical evidence from climatically vulnerable yet culturally significant cities, offering insights relevant to global SUHI research and sustainable urban development. Full article