Search Results (31,152)

Occupational health and safety (OHS) malpractices may increase the risk of occupational injuries and are a significant public health problem in the iron and steel industries, often leading to disability and death. Information on OHS practices related to occupational injuries in the iron and steel industries is limited in many countries. This study aimed to assess OHS practices in relation to occupational injuries among workers in the iron and steel industries in Tanzania. A cross-sectional study was conducted among 321 production line workers. Data were collected through interviews, using a structured OHS questionnaire and the modified International Labour Organization (ILO) injury assessment tool. A total of 209 workers had experienced an occupational injury in the past year. According to univariate regression analyses, the safety practice variables ‘safety inspections’ and ‘use of personal protective equipment’ were significantly associated with occupational injury. However, after adjusting for sociodemographic and organizational factors, including years of work experience, work section, daily working hours, and shift work, these associations were no longer statistically significant. Working in the rolling mill, having less than four years of work experience, working more than 10 h per day, and performing shift work were significant predictors of occupational injuries. These factors attenuated the associations between safety practices and occupational injuries. The severity of injuries was found to be related to the accessibility of PPE. The findings suggest that work experience and organizational characteristics play a critical role in shaping occupational injury risk, highlighting the need for multifaceted interventions that integrate effective safety practices, work experience and organizational measures to improve worker safety in Tanzania’s iron and steel industry. Full article

Climate change has been identified by the World Health Organization (WHO) as “the single greatest health threat to humanity”. To investigate this subject, we conducted a narrative review of the literature about sustainability in the OR, with the aim of exploring the existing knowledge and identifying prevailing challenges in the field. A combined automated and manual database search of the literature was performed using Medline (PubMed), Scopus, Ovid, and the Cochrane Library, covering publications available up to and including 21 May 2025. The significance of environmental sustainability is recognized across all healthcare systems. To fulfil the United Nations Sustainable Development Goals, the healthcare sector must undergo a transformation towards more eco-friendly practices, which will also affect clinical decision-making. Net-zero is an internationally agreed goal for avoiding worsening global heating in the second half of the 21st century, as it aims to balance the quantities of greenhouse gases released into and removed from the atmosphere, achieving carbon neutrality. Nevertheless, the operating room (OR) is among the most significant contributors to environmental pollution, with the major carbon hotspots determined by the use of energy, procurement and disposal of consumables and the waste of water. In response to this challenge, leading medical societies, surgical teams, government bodies, and industry stakeholders endorse Green Surgery (GS) by taking steps to address healthcare sustainability and its impact on climate change. Therefore, a movement toward “greening” healthcare, or improving the environmental footprint of healthcare has been built. Full article

Background: Antimicrobial resistance represents a growing global health challenge, necessitating the development of effective non-antibiotic antimicrobial approaches. Antimicrobial photodynamic therapy (aPDT) has emerged as a promising localized antimicrobial strategy owing to its broad-spectrum activity, biofilm-targeting capability, and low propensity to induce resistance. However, its clinical translation remains restricted by limited photosensitizer (PS) performance, insufficient light penetration, oxygen dependency, biofilm-associated barriers, and the lack of standardized treatment protocols. Methods: This narrative review summarizes recent strategies developed to enhance the efficacy and translational potential of aPDT, including PS engineering, nanomaterial- and non-nanomaterial-based delivery systems, advanced light-source technologies, hypoxia-modulating approaches, and synergistic therapeutic strategies. In addition, current challenges associated with regulatory approval, manufacturing scalability, treatment standardization, and clinical implementation are discussed. Results: Recent advances have transformed aPDT from a conventional PS–light–oxygen system into a multifunctional antimicrobial platform. Emerging approaches improve bacterial targeting, biofilm penetration, reactive oxygen species generation, oxygen utilization, and therapeutic precision. Nevertheless, many advanced systems remain at the preclinical stage due to complexity, cost, safety concerns, and insufficient clinical validation. Conclusions: aPDT should be considered a targeted therapeutic option for accessible, localized, and biofilm-associated infections rather than a replacement for systemic antimicrobial therapy. Future clinical translation will depend on balancing technological innovation with biosafety, scalability, and protocol standardization. Strategies integrating intelligent PS design, oxygen regulation, and clinically feasible synergistic approaches may provide promising pathways toward the broader application of aPDT in antimicrobial management. Full article

Despite the fact that smoking is still a significant global public health concern, current mathematical models of smoking dynamics primarily depend on conventional numerical solvers. A particular five-compartment smoking dynamics model has not yet been solved using deep neural network (DNN) techniques. In order to fill this research gap, this work creates a unique DNN framework that can simulate nonlinear smoking dynamics in a computationally efficient manner. The Levenberg–Marquardt backpropagation technique is used to improve a dual-hidden-layer network consisting of 20 radial basis activation function (RBAF) neurons and 40 log-sigmoid activation function (LSAF) neurons. With a minimum mean squared error (MSE) of $1.865\times {10}^{-6}$ and a coefficient of determination $R^2$ equal to or near unity across all model variables, the trained DNN offers instantaneous predictions while maintaining superior accuracy, in contrast to traditional numerical methods that necessitate the explicit re-solving of differential equations for each parameter change. Crucially, our DNN-based framework is appropriate for automated public health decision-support systems since it functions independently and does not require human intervention during the prediction phase. Key smoking behaviors, such as initiation, quitting efforts, relapse dynamics, and long-term recovery patterns, are successfully replicated by the framework, while relapse dynamics are captured through the recovered-to-potential smoker pathway, consistent with the original model formulation. These findings show that the proposed DNN approach not only closes the methodological gap in the application of deep learning to smoking dynamics but also offers a dependable and computationally effective tool for quick evaluation of intervention scenarios, supporting evidence-based public health decision making without compromising accuracy. Full article

Background/Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a type 2 inflammatory condition that significantly impairs health-related quality of life (HRQoL), particularly sleep quality. Beyond mechanical nasal obstruction, type 2 cytokines (IL-4, IL-13) are thought to directly disrupt sleep architecture. Dupilumab, an IL-4Rα antagonist, is approved for severe CRSwNP, but its specific effect on sleep quality remains under investigation. This systematic review aims to evaluate the impact of dupilumab on sleep quality in patients with severe, uncontrolled CRSwNP. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Google Scholar, and Web of Science up to June 2026. We included adult studies (≥1 month of dupilumab 300 mg every 15 days) reporting sleep outcomes using validated tools (Epworth Sleepiness Scale [ESS], Pittsburgh Sleep Quality Index [PSQI], Insomnia Severity Index [ISI], or the SNOT-22 sleep domain). Risk of bias was assessed using ROBINS-I and RoB 2 tools. Results: Seven studies (n = 2164 patients) met inclusion criteria. Across observational and post hoc RCT analyses, dupilumab consistently improved the SNOT-22 sleep domain (mean reduction up to −7.02 points at 24 weeks; p < 0.001), PSQI, ESS, and ISI scores. One study reported a decrease in poor global sleep quality from 88.9% at baseline to 5.7% at 12 months. However, most observational studies had a serious risk of bias due to unaddressed confounding and lack of blinding. No polysomnographic data were reported. Conclusions: Dupilumab was associated with significant improvements in patient-reported sleep quality in CRSwNP patients across seven included studies. However, the evidence is limited by the absence of objective sleep measures, the serious risk of bias in most observational studies, and the lack of comparative head-to-head data. High-quality randomized controlled trials with prespecified sleep endpoints and polysomnographic assessments are needed before definitive conclusions can be drawn. Full article

Globally, school meals have formed a social safety net for improving children’s nutrition, health, and educational attainment. In Aotearoa New Zealand, government funded lunches for children attending schools in low-income neighbourhoods were introduced in 2020, amidst rising poverty levels and consequent household food insecurity. The aspirations encapsulated within the scheme’s indigenous Māori name, ‘Ka Ora, Ka Ako’ (‘to be well, healthy and safe; to be ready to learn’) have lost focus since reforms to the lunches in 2024 prioritised cost savings, resulting in widely publicised quality issues. This present study draws from a critical multi-level investigation into the issue of hungry children in New Zealand schools, prior to the introduction of ‘Ka Ora, Ka Ako’. Narrative analysis identified how food in schools has been conceptualised within the context of a settler colonial charity model and neoliberal austerity. Applying a critical community psychology lens highlights how burnt and meagre lunches offered to children from food insecure households are a concrete product of historically grounded, unequal relations between groups. A community psychology approach reimagines state-funded food in schools as a public good, arguing for equitable rights to nutrition, including recognition of Māori rights to food sovereignty. Full article

Background: As population aging accelerates, pharmacological treatments provide limited benefits for cognitive decline. Exercise and technology-assisted exercise have thus emerged as important non-pharmacological approaches for supporting cognitive health in older adults. However, comparative evidence on the relative effectiveness of different intervention modalities across cognitive outcomes remains limited. Objective: This study used a network meta-analysis to systematically compare and rank the effects of traditional and non-traditional exercise interventions on cognitive function in older adults. Methods: This systematic review and network meta-analysis was registered on PROSPERO (CRD420261278685). PubMed, Embase, the Cochrane Library, Web of Science, and Scopus were searched from inception to May 2026. Randomized controlled trials enrolling participants aged 60 years or older were included. Interventions comprised aerobic, resistance, mind–body, finger, and multicomponent exercise, as well as virtual reality-based interventions, artificial intelligence-assisted exercise, and wearable exoskeleton training. Primary outcomes included global cognition, executive function, memory, attention, and activities of daily living. A random effects network meta-analysis was applied to estimate standardized mean differences with 95 percent confidence intervals, and intervention rankings were derived using SUCRA values. Results: A total of 70 randomized controlled trials involving 5573 older adults were included. All active interventions demonstrated overall cognitive benefits compared with usual care or non-active control conditions. Mind–body exercise showed the highest probability of improving global cognitive performance. Virtual reality-based interventions were particularly effective for executive function, memory, and activities of daily living, while artificial intelligence-assisted exercise showed favorable rankings for attention outcomes. Mind–body exercise showed favorable effects on global cognition, whereas several technology-assisted interventions ranked highly for selected cognitive and functional outcomes. Conclusions: Both traditional and non non-traditional exercise interventions improve cognitive function in older adults, although their effects differ across cognitive domains. Technology-assisted approaches appear more effective for enhancing specific cognitive functions, while traditional interventions such as mind–body exercise are more advantageous for preserving overall cognitive performance. These findings highlight the potential value of stage specific and combined intervention strategies for promoting cognitive health in aging populations. Full article

Mpox remains endemic in the Democratic Republic of the Congo (DRC), where co-endemic infections and non-communicable comorbidities may influence disease severity and clinical outcomes. However, data on associated factors of adverse outcomes among hospitalised adults with mpox in African settings remain limited. This study aimed to describe the clinical profile of adults hospitalised with mpox in South Kivu Province, eastern DRC, and to explore associations between selected comorbid conditions, including HIV infection, malaria, and hyperglycaemia, and mpox disease severity, mortality, and hospitalisation duration. We conducted a multicentre retrospective observational study among adults admitted to five mpox treatment centres in South Kivu Province, DRC, between January 2024 and December 2025. Demographic, clinical, and laboratory data were extracted from routine hospital registers. Mpox disease severity (available only for a subset of participants) was classified according to WHO criteria. Participants included adults with suspected, probable or laboratory-confirmed mpox according to WHO case definitions in use during the study period. In addition to mpox disease severity, outcomes of interest included in-hospital mortality and duration of hospitalisation. Multivariable regression models were used to explore associations between selected comorbidities and clinical outcomes after adjustment for age and sex. Among 652 hospitalised adults included in the analysis, the median age was 26 years (IQR 21.0–35.0), and 383/652 (58.7%) were female. Among patients with recorded severity data (n = 104), moderate or severe mpox was documented in 88/104 (84.6%) patients. Overall mortality was 14/645 (2.2%). HIV infection was identified in 5 of the 71 participants tested (7.0%). Among participants with available test results, HIV infection appeared to be associated with higher mpox severity and mortality. Increasing age, but not HIV infection, malaria, or glycaemic status, was associated with longer hospitalisation. Because laboratory investigations were performed in only a subset of participants and missing data were substantial, these findings should be interpreted cautiously and considered exploratory. Prospective studies incorporating systematic laboratory testing and standardised clinical data collection are needed to better define factors associated with severe mpox in endemic African settings. Full article

In August 2024, the Africa Centres for Disease Control and Prevention (Africa CDC) declared mpox a Public Health Emergency of Continental Security (PHECS), and the World Health Organization (WHO) followed with a second Public Health Emergency of International Concern (PHEIC), in response to the rapid expansion of clade Ib monkeypox virus (MPXV) across eastern Democratic Republic of the Congo (DRC) and neighboring countries. Both declarations have since been lifted (September 2025 and January 2026), yet clade Ib transmission and severe outcomes in pregnant women and children persist. We argue that closing these emergency mechanisms marks not the end of the epidemic but the start of a harder phase: sustaining surveillance, care, and research amid shrinking donor support, including the dissolution of the United States Agency for International Development (USAID). We contend that integrating mpox into existing HIV, sexually transmitted infection (STI), and reproductive health platforms is the most realistic route to durable routine care, while pregnancy, paediatric disease, severe cases, and zoonotic spillover still need dedicated pathways. Sustainable management ultimately depends on domesticated financing, stronger institutions, genuine community engagement, and action on the ecological drivers of spillover. Full article

Background: Fractures of the lower extremity are commonly associated with impaired mobility, whereas injuries of the upper extremity primarily affect limb-specific function. Wearable devices allow the objective and continuous monitoring of physical activity and may therefore provide additional insight into postoperative recovery processes under real-world conditions. This exploratory study aimed to compare postoperative trajectories of daily step count following surgical treatment of upper versus lower extremity fractures and to explore possible associations with general health status and return to work. Methods: In this prospective observational study, 38 patients with upper extremity fractures and 55 patients with lower extremity fractures were included. Daily step counts were recorded postoperatively. Patient-reported general health (PROMIS Global Health) and work status at three months were assessed. Results: During the early postoperative period, patients with lower extremity injuries recorded lower daily step counts than patients with upper extremity injuries. Within three months, 89% of patients with upper extremity injuries and 49% of patients with lower extremity injuries had returned to work. Conclusions: In this exploratory cohort, postoperative step count trajectories differed between surgically treated upper and lower extremity fractures during the early postoperative period. Return-to-work rates at three months also differed between groups. Interpretation of these findings is limited by the real-world use of heterogeneous consumer wearable devices and the exploratory study design. These findings should be considered preliminary and hypothesis-generating. Full article

Urban Heat Island (UHI) effects are increasingly acknowledged as a critical urban climate challenge with far-reaching consequences for human health and overall well-being. However, the conceptual structure, temporal evolution, and intellectual landscape of studies examining the relationships between UHI and human well-being remain fragmented, thereby constraining the development of integrated knowledge frameworks needed to guide future research, urban adaptation strategies, and evidence-based policy interventions. To this end, a total of 4857 studies were retrieved from the Scopus and Web of Science databases and screened following the PRISMA guidelines. These studies were then analyzed using Bibliometrix and VOSviewer. The results reveal a rapid and exponential growth in scientific output, particularly after 2010, with the output reaching its highest level in recent years. These outputs were shaped mostly in China and the United States with a well-established international collaboration network, while the Global South remain significantly underrepresented in scientific productions. We also found that studies are primarily structured around four dominant research clusters: urban heat island, thermal comfort, land surface temperature, and climate change. Furthermore, early studies predominantly focused on urban surface properties and built-environment characteristics, while recent research has increasingly shifted toward human health impacts, thermal stress, heat vulnerability, and well-being. Emerging research directions further highlight growing interest in nature-based solutions for mitigating UHI effects, alongside the application of advanced technologies such as machine learning and remote sensing for high-resolution urban climate assessment. Overall, our findings indicate a transition toward a more integrated urban climate–health–well-being research framework, while simultaneously revealing persistent geographical and conceptual gaps, particularly across the Global South. We therefore advocate for increased empirical research, stronger international collaboration, and context-specific urban adaptation strategies to better safeguard human well-being under intensifying urban heat conditions. Full article

Monitoring the surface thermal state of thermal protection structures (TPS) is essential for the safe operation and health assessment of hypersonic vehicles. However, direct sensor deployment on heated surfaces suffers from poor survivability, while many model-based approaches require prescribed thermophysical properties and boundary conditions. This study proposes an AutoRegressive with eXogenous input (ARX)-based surface temperature reconstruction method using deep-embedded sensors. By identifying a local equivalent dynamic mapping among internal temperature responses, the method reconstructs the near-surface temperature through virtual-point recursive extrapolation without requiring explicit thermophysical-property or boundary-condition information. Numerical simulations show high accuracy under adiabatic, natural-convection, and forced-convection rear-surface boundaries, with the global RRMSE remaining within 1.09%. Further analyses demonstrate tolerance to moderate temperature- and space-dependent variations in equivalent thermophysical properties; simulations using realistic LI-900 thermal conductivity variations confirm its applicability under different effective conductivity conditions. Single-sided heating experiments with two sensor-embedding layouts validate the method, yielding RRMSE values of 1.74% for the 5-point model and 4.27% for the 9-point model in the main validation case. The applicable conditions are further clarified through analyses of internal temperature-difference SNR, recursive error accumulation, and sensor-layout constraints. The proposed method provides a low-cost and long-life solution for online TPS surface temperature monitoring. Full article

Background/Objectives: Depression is a leading cause of disability among middle-aged and older adults, with its burden markedly amplified by multimorbidity. In rapidly aging China, distinct multimorbidity phenotypes interact with depressive symptoms through complex, non-linear pathways that traditional models struggle to capture. Interpretable machine learning offers high predictive accuracy alongside clinically transparent explanations, yet few studies have applied this approach to large national Chinese cohorts while stratifying by empirically derived multimorbidity patterns. The aim of this study was to identify the pattern-specific determinants of depression among middle-aged and older adults with multimorbidity using explainable machine learning techniques based on data from the China Health and Retirement Longitudinal Study (CHARLS). Methods: In this cross-sectional analysis of the China Health and Retirement Longitudinal Study (CHARLS) 2020 data, 14,981 participants were divided into a multimorbidity group (≥2 of 12 physician-diagnosed chronic conditions; n = 8265) and a matched control group (0 or 1 condition; n = 6716). Depressive symptoms were defined as a CES-D-10 score ≥ 10 (binary outcome). Forty-four candidate predictors were screened via LASSO regression, the Boruta algorithm, and univariate logistic regression. Fifteen supervised machine learning models were trained separately for each group using SMOTE for class imbalance, nested 5-fold cross-validation for hyperparameter tuning, and an independent 20% test set for external validation. Model performance was ranked by the composite AB Score (equal weighting of AUROC and Brier score). The champion models were interpreted globally via SHAP summary plots, directional waterfall diagrams, and novel impact-intensity network diagrams. The same pipeline was repeated across four literature-derived multimorbidity patterns within the multimorbidity group. Results: Depressive-symptom prevalence was 42.96% (3551/8265) in the multimorbidity group versus 27.01% (1814/6716) in the control group. The Gaussian process classifier achieved the highest AB Score (0.7782) in the multimorbidity group (external AUROC of 0.7545, Brier score of 0.1981); the generalized additive model was optimal in the control group (AB Score 0.7856, external AUROC 0.7387, Brier score 0.1676). Across both groups, the top contributors by mean absolute SHAP were IADL disability, abnormal sleep duration, ADL disability, rural residence, and lower education level. Pattern-specific SHAP networks revealed clinically meaningful heterogeneity: cardio-metabolic clusters were driven by marital status and headache; digestive–joint clusters by IADL disability and shoulder–knee pain synergy; respiratory clusters by gender and multisite pain; and cardiovascular–digestive clusters by sleep duration and vigorous activity. Conclusions: Interpretable machine learning models demonstrated clinically useful discrimination and excellent calibration while revealing universal drivers (functional disability and sleep disturbance) and cluster-specific synergistic interactions underlying depressive symptoms in multimorbid Chinese adults. These phenotype-tailored insights provide a practical roadmap for precision screening and targeted interventions in aging populations. Full article

Background/Objectives: Maternal, perinatal, and neonatal mortality remain major global health challenges, causing approximately 2.5 million neonatal deaths annually, particularly in low- and middle-income countries (LMICs). Although Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), is increasingly used to support healthcare decision-making, a comprehensive synthesis of its application to prevent prenatal, preterm birth, and neonatal deaths is lacking. This study systematically reviews the current state of research in this field. Methods: A Structured Literature Review (SLR) was conducted following a combined methodological framework integrating Massaro’s protocol and PRISMA 2020 guidelines. Searches were performed in Scopus, IEEE Xplore, and Google Scholar using domain-specific keywords. From 459 identified publications, 46 peer-reviewed studies published between 2018 and 2024 were selected through a four-step filtering and quality assessment process. Bibliometric and thematic analyses were performed. Results: ML techniques accounted for 71.7% of the selected studies, whereas DL approaches represented 28.3%. Neonatal death prediction was the most frequently investigated outcome (34.7% of publications). Most studies originated from Europe (39.1%) and North America (30.4%), while research from Latin America and Sub-Saharan Africa was scarce despite the high mortality burden in these regions. Key barriers included non-standardized clinical records, limited interoperability of health information systems, and the underrepresentation of LMIC populations in training datasets. Conclusions: AI shows significant potential for reducing maternal and neonatal mortality through predictive analytics. However, important geographical and methodological gaps remain. Future research should prioritize inclusive datasets and predictive frameworks adapted to resource-constrained healthcare settings. Full article

Background: Depression and anxiety disorders remain among the leading contributors to global disability and represent a major public health challenge. Although evidence-based psychotherapies are available, access to treatment remains limited due to structural, economic, geographical, and workforce-related barriers. Digital mental health interventions have emerged as scalable approaches to reducing this treatment gap, with artificial intelligence (AI)-guided cognitive behavioral therapy (CBT) representing a rapidly developing and clinically relevant extension of digital psychotherapy. Objective: This review aims to synthesize current evidence on digital and AI-guided CBT interventions for depression and anxiety, with a focus on clinical utility, scalability, mechanisms of change, safety considerations, and public health relevance. In addition, the review proposes a clinically oriented conceptual framework for understanding the role of AI-guided CBT within contemporary digital psychiatry. Methods: A focused narrative review was conducted using PubMed, Scopus, and Google Scholar databases, covering publications from 2010 to 2025. Relevant peer-reviewed studies, systematic reviews, meta-analyses, and conceptual papers addressing digital CBT, AI-assisted CBT, conversational agents, symptom monitoring, and digital mental health implementation were identified and analyzed qualitatively. Results: Existing evidence suggests that internet-delivered CBT, mobile applications, and AI-based conversational agents may reduce depressive and anxiety symptoms, particularly in individuals with mild to moderate conditions. However, the evidence base remains heterogeneous, with limitations including short follow-up periods, variability in intervention quality, reliance on self-reported outcomes, and insufficient data on long-term effectiveness, safety, and real-world implementation. Emerging concepts such as digital therapeutic alliance, continuous symptom monitoring, adaptive intervention delivery, and AI-driven personalization may represent key factors influencing engagement and clinical outcomes. Conclusions: AI-guided CBT represents a promising but still evolving component of modern mental health care. These technologies have the potential to improve accessibility, optimize resource allocation, and support stepped-care and hybrid models of treatment. Future research should prioritize rigorous clinical validation, long-term outcome evaluation, transparent safety protocols, ethical governance, and integration into real-world health systems. AI-guided CBT should not be understood as a replacement for clinicians, but as a complementary and scalable extension of evidence-based psychotherapy. Full article