Search Results (299)

Gallic acid (GA) is a natural polyphenol abundantly found in a variety of fruits, including blackberries, apples, pineapples, strawberries, bananas, and grapes. With prominent anti-inflammatory and antioxidant properties, GA effectively mitigates inflammation and oxidative stress. Furthermore, it plays a significant role in modulating various cellular processes and biological activities, ultimately inhibiting the progression of pathogenesis. This review explores the multifaceted health benefits of GA, highlighting its role as antidiabetic, anti-obesity, anti-arthritis, hepatoprotective, cardioprotective, and neuroprotective effects. Additionally, its impact on the respiratory, digestive, and reproductive systems, along with its related pathogenesis, is described. Additionally, its role as an antimicrobial is defined primarily through mechanisms such as disruption of microbial cell membranes, inhibition of efflux pumps, and antibiofilm activity. Moreover, this review provides a novel, integrative analysis of GA by unifying its mechanistic roles across various pathogenesis. It further describes the role of GA in cancer management via the modulation of signaling pathways. In addition, it demonstrates the synergistic effects of GA when used in combination with other drugs/compounds and discusses nanoformulation approaches that improve its therapeutic efficacy. However, despite significant preclinical outcomes, the clinical application of GA is limited by a shortage of human trials, low bioavailability, and an inadequate understanding of its mechanisms of action and optimal dosage. To overcome these limitations, well-designed clinical trials, in vivo studies, and advanced nanoformulation approaches are required to enhance bioavailability, elucidate mechanisms of action, and increase knowledge of safety and long-term toxicity. Addressing these gaps will enable the full exploration of GA’s benefits in disease prevention and management. Full article

Groundwater heavy metal contamination (GHMC) has drawn significant attention in China over recent decades due to industrialization. However, effective monitoring and early warning remain global challenges because of the limited understanding of heavy metal behavior in groundwater. This study conducts a detailed comparative analysis of heavy metals and conventional indicators using a long-term, high-frequency online monitoring program. Groundwater online monitoring is an automated system for real-time, continuous collection, and transmission of indicators via sensors and IoT platforms. Conventional indicators refer to the priority parameters used to assess basic water quality, hydrological characteristics and health risks in routine monitoring. Nineteen heavy metals and ten conventional indicators were monitored simultaneously, generating approximately 1.6 million data points over three years. The time series data show that online monitoring effectively captures abnormal changes in heavy metal levels. Abnormal heavy metal fluctuations appear as sharp, isolated spikes lasting at least several hours, while conventional indicators exhibit high-amplitude variations lasting over 30 h—indicating that heavy metal changes are harder to detect in a timely manner. Long-term comparisons also reveal low consistency between heavy metals and conventional indicators, supporting the need for independent heavy metal monitoring. In contrast, strong consistency among heavy metals suggests opportunities to streamline monitoring by selecting representative elements. Monitoring frequency optimization shows that daily measurement is sufficient for heavy metals, which is slightly more frequent than the typical three-day interval for most conventional indicators. Long-term data enable reliable early warnings for both indicator types, with predictions closely matching field observations. However, heavy metal alerts are shorter and less frequent than those for conventional indicators. Integrating both types into a unified early warning system enhances its comprehensiveness, accuracy and timeliness. This study provides a solid scientific foundation for efficient GHMC monitoring and early warning in groundwater in areas under the influence of industrial activities. Full article

Structural health monitoring of steel buildings requires accurate detection and localization of bolt loosening, a critical yet challenging task due to complex joint geometries and varying environmental conditions. We propose an intelligent framework that integrates an improved YOLOv9 model with multi-view image fusion to address this problem. The method constructs a comprehensive dataset with multi-angle images under diverse lighting, occlusion, and loosening conditions, annotated with multi-task labels for precise training. The YOLOv9 backbone is enhanced with attention mechanisms to focus on key bolt features, while an angle-aware detection head regresses both bounding boxes and rotation angles, enabling loosening state determination through a threshold-based criterion. Furthermore, the framework unifies camera coordinate systems and employs epipolar geometry to fuse 2D detections from multiple views, reconstructing 3D bolt positions and orientations for precise localization. The proposed method achieves robust performance in detecting loosening angles and spatially localizing bolts, offering a practical solution for real-world structural inspections. Its significance lies in the integration of advanced deep learning with multi-view geometry, providing a scalable and automated approach to enhance safety and maintenance efficiency in steel structures. Full article

The escalating threats of zoonotic diseases, antimicrobial resistance (AMR), climate change, and environmental degradation have intensified the need for a unified health approach. One Health—integrating human, animal, and environmental health—is critical for national and global health security. India, with its high population density, biodiversity, and socio-ecological complexity, stands poised to lead in operationalizing this integrated vision. This review analyzes India’s evolving One Health ecosystem, focusing on policy development, inter-ministerial collaborations, surveillance systems, grassroots implementation, and education. Institutions like the National Centre for Disease Control (NCDC), Indian Council of Medical Research (ICMR), and Department of Biotechnology (DBT) are discussed. We propose a strategic blueprint built on integrated surveillance (One Network), cross-sectoral governance (One Nation), and field-level implementation (One Health). Highlighting successful case studies and India’s role in global platforms, the article presents a roadmap to bridge fragmented efforts into a resilient, community-driven national mission to protect human, animal, and environmental health. Full article

Peripartum depression (PPD) represents one of the most prevalent and disabling psychiatric conditions among women, yet its underlying biology remains poorly integrated across medical disciplines. Emerging evidence highlights PPD as a prototypical disorder of the heart–brain axis, where neuroendocrine changes, immune activation, and cardiovascular dysregulation converge to shape maternal vulnerability. During pregnancy and the postpartum period, abrupt fluctuations in estrogen, progesterone (P4), and placental corticotropin-releasing hormone (CRH) interact with a sensitized hypothalamic–pituitary–adrenal (HPA) axis, altering neural circuits involved in mood regulation, stress reactivity, and maternal behavior. Parallel cardiovascular adaptations, including endothelial dysfunction, altered blood pressure variability, and reduced heart rate variability (HRV), suggest a profound perturbation of autonomic balance with potential long-term implications for maternal cardiovascular health. Neuroinflammation, microglial activation, and systemic cytokine release further mediate the bidirectional communication between the heart and the brain, linking emotional dysregulation with vascular and autonomic instability. Evidence also indicates that conditions such as preeclampsia and peripartum cardiomyopathy share biological pathways with PPD, reinforcing the concept of a unified pathophysiological axis. This review synthesizes current knowledge on the neurobiological, cardiovascular, endocrine, and inflammatory mechanisms connecting PPD to maternal heart–brain health, while discussing emerging biomarkers and therapeutic strategies aimed at restoring integrative physiology. Understanding PPD as a multisystem heart–brain disorder offers a transformative perspective for early detection, risk stratification, and personalized intervention during one of the most biologically vulnerable periods of a woman’s life. Full article

Childhood malnutrition remains a pressing public health challenge in Europe, where stunting, wasting, and underweight coexist with rising rates of childhood overweight and obesity. This policy review provides a strategic roadmap for promoting healthy nutrition in early childhood by synthesizing WHO and EU guidance and proposing coordinated action across three time horizons. Short-term goals (1–3 years) include harmonizing food-based dietary guidelines, implementing universal nutrition screening in pediatric care, and strengthening breastfeeding-supportive environments. Mid-term priorities (3–7 years) focus on fiscal levers, such as sugar taxes and healthy food subsidies; reformulating children’s products; and embedding nutrition education within school curricula. Long-term strategies (7+ years) emphasize harmonized EU-wide monitoring systems, alignment of early-life nutrition with social protection policies, and sustained investment in research on the DOHaD. Through a unified, multisectoral strategy emphasizing early-life nutrition, equitable access to healthy foods, education, and robust regulation, Europe can effectively address the double burden of malnutrition and sustainably reduce childhood obesity. Full article

The widespread adoption of mobile health applications has increased the volume of sensitive personal and physiological data processed through interconnected devices. Ensuring privacy compliance in this context remains a challenge, as existing app stores and privacy labeling systems rely heavily on self-declared information. App-PI is a data-driven ecosystem designed to offer end users with tools they can easily manage and privacy researchers with structured and reliable app metadata. It is designed to automate the collection, analysis, and visualization of privacy-related metadata from mobile applications. Heterogeneous data sources are integrated into a unified repository (App-PIMD), enabling the empirical assessment of privacy risks. The data flow design is critical to ensure that the data used to assess privacy impact is of good quality, as well as the privacy indicators that end users will be offered. It is shown on a popular mHealth application, demonstrating the importance of data flow design in order to be able to obtain, from documents and files created for consumption by an operating system, a set of data and tools ready for consumption by the true recipients of health apps: people. Full article

The human gut microbiome is a complex ecosystem integral to host health, with butyrate-producing bacteria (BPB) playing a critical role in maintaining intestinal homeostasis. This scoping review explores the composition, function, and systemic influence of BPB, focusing on their metabolic product, butyrate, and its implications for gut integrity, immune modulation, and gut–brain axis (GBA) communication. Disruptions to BPB abundance, which is correlated with Western dietary patterns, food additives, and antibiotic exposure, are linked to gut dysbiosis and associated with a wide spectrum of chronic diseases, including inflammatory bowel disease (IBD), obesity, type 2 diabetes, neurodegenerative disorders, and psychiatric conditions. Butyrate supports colonocyte energy metabolism, reinforces epithelial barrier function, regulates goblet cell mucus production, and exerts anti-inflammatory effects via histone deacetylase inhibition and G-protein-coupled receptor signaling. The depletion of BPB and the resultant butyrate deficiency may represent a unifying pathophysiological mechanism underlying these conditions. Therapeutic strategies that restore BPB populations and butyrate levels, such as prebiotics, dietary fiber, and microbiota-targeted interventions, hold promise for mitigating inflammation and enhancing systemic health through microbiome modulation. Full article

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations. Full article

Background/Objectives: Colorectal cancer (CRC) is a major public health challenge in Brazil, characterized by marked regional disparities. Although national legislation mandates that treatment begin within 60 days after diagnosis, compliance remains inconsistent, particularly within the Unified Health System (SUS). This study aimed to analyze the time to treatment initiation for colon (C18) and rectal (C20) cancer in Brazil from 2013 to 2024, assessing regional inequalities, temporal trends, and factors associated with treatment delays. Methods: We conducted an ecological study using secondary data from the Ministry of Health’s PAINEL-Oncologia platform, which integrates information from SIA/SUS, SIH/SUS, and SISCAN. Records of patients diagnosed with colon and rectal cancer (ICD-10 C18–C20) were evaluated. Temporal trends were analyzed using Joinpoint regression, and factors associated with delayed treatment initiation (>60 days) were identified through multiple logistic regression models. Results: Persistent discrepancies were observed between diagnostic and treatment trends from 2013 to 2024, with the Annual Percent Change (APC) for diagnosis exceeding that for treatment, particularly among adults aged 55–69 years. The Southeast and South regions accounted for over 70% of all diagnosed cases, starkly contrasting with the less than 25% in the North and Northeast. More than 50% of patients across all clinical stages initiated treatment after the legally mandated 60-day period. Women with rectal cancer had a 28% higher risk (RR = 1.28) of being diagnosed at stage IV. Chemotherapy was the predominant initial therapeutic modality, while the need for combined chemo-radiotherapy was associated with markedly elevated risk ratios for delay (e.g., RR = 26.53 for stage IV rectal cancer). Treatment initiation delays (>60 days) were significantly associated with residence in the North/Northeast regions, female sex (for rectal cancer), advanced-stage disease, and complex therapeutic regimens. Conclusions: The study demonstrates persistent regional inequalities and highlights a substantial mismatch between diagnostic capacity and therapeutic availability in Brazil. These gaps contribute to treatment delays and reinforce the need to strengthen and expand oncological care networks to ensure equitable access and improve outcomes, particularly in underserved regions. Full article

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems. Full article

In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems. Full article

Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL monitoring approaches mainly emphasize electrical and gas-state parameters, while lightweight solutions capable of rapidly detecting and localizing impact-induced structural anomalies remain limited. To address this gap, this paper proposes an intelligent end-edge-cloud monitoring system for impact anomaly detection in GIL pipelines. Numerical simulations are first conducted to analyze the dynamic response characteristics of the pipeline under impacts of varying magnitudes, orientations, and locations, revealing the relationship between impact scenarios and vibration mode evolution. An end-tier multi-parameter intelligent sensor is then developed, integrating triaxial acceleration and angular velocity measurement with embedded lightweight computing. Laboratory impact experiments are performed to acquire sensor data, which are used to train and validate a multi-class extreme gradient boosting (XGBoost) model deployed at the edge tier for accurate impact-location identification. Results show that, even with a single sensor positioned at the pipeline midpoint, fusing acceleration and angular velocity features enables reliable discrimination of impact regions. Finally, a lightweight cloud platform is implemented for visualizing structural responses and environmental parameters with downsampled edge-side data. The proposed system achieves rapid sensor-level anomaly detection, precise edge-level localization, and unified cloud-level monitoring, offering a low-cost and easily deployable solution for GIL structural health assessment. Full article

Continuous heart rate monitoring is crucial for early cardiovascular disease detection. To overcome the discomfort and limitations of ECG in home settings, we propose a multimodal temporal fusion network (MM-TFNet) that integrates ballistocardiography (BCG) and photoplethysmography (PPG) signals. The network extracts temporal features from BCG and PPG signals through temporal convolutional networks (TCNs) and bidirectional long short-term memory networks (BiLSTMs), respectively, achieving cross-modal dynamic fusion at the feature level. First, bimodal features are projected into a unified dimensional space through fully connected layers. Subsequently, a cross-modal attention weight matrix is constructed for adaptive learning of the complementary correlation between BCG mechanical vibration and PPG volumetric flow features. Combined with dynamic focusing on key heartbeat waveforms through multi-head self-attention (MHSA), the model’s robustness under dynamic activity states is significantly enhanced. Experimental validation using a publicly available BCG-PPG-ECG simultaneous acquisition dataset comprising 40 subjects demonstrates that the model achieves excellent performance with a mean absolute error (MAE) of 0.88 BPM in heart rate prediction tasks, outperforming current mainstream deep learning methods. This study provides theoretical foundations and engineering guidance for developing contactless, low-power, edge-deployable home health monitoring systems, demonstrating the broad application potential of multimodal fusion methods in complex physiological signal analysis. Full article

The emergence and dissemination of antimicrobial resistance (AMR) are driven by complex, interconnected mechanisms involving microbial communities, environmental factors, and human activities, with climate change playing a pivotal and accelerating role. Rising temperatures, altered precipitation patterns, and other environmental disruptions caused by climate change create favorable conditions for bacterial growth and enhance the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Thermal stress and environmental pressures induce genetic mutations that promote resistance, while ecosystem disturbances facilitate the stabilization and spread of resistant pathogens. Moreover, climate change exacerbates public and animal health risks by expanding the range of infectious disease vectors and driving population displacement due to extreme weather events, further amplifying the transmission and evolution of resistant microbes. Livestock agriculture represents a critical nexus where excessive antibiotic use, environmental stressors, and climate-related challenges converge, fueling AMR escalation with profound public health and economic consequences. Environmental reservoirs, including soil and water sources, accumulate ARGs from agricultural runoff, wastewater, and pollution, enabling resistance spread. This review aims to demonstrate how the Mediterranean’s strategic position makes it an ideal living laboratory for the development of integrated “One Health” frameworks that address the mechanistic links between climate change and AMR. By highlighting these interconnections, the review underscores the need for a unified approach that incorporates sustainable agricultural practices, climate mitigation and adaptation within healthcare systems, and enhanced surveillance of zoonotic and resistant pathogens—ultimately offering a roadmap for tackling this multifaceted global health crisis. Full article