Search Results (2,349)

Background/Objectives: Occupational contact dermatitis (OCD) is the most common work-related skin disease, accounting for roughly 90–95% of occupational dermatoses and falling predominantly on the hands. It is rarely dangerous yet imposes a substantial burden through impaired quality of life, lost productivity, and premature exit from affected trades. The COVID-19 pandemic intensified this burden among healthcare workers, in whom the pooled one-year prevalence of self-reported hand eczema reaches around 27%; meta-analytic data link the increased risk principally to frequent handwashing and wet work rather than to alcohol-based hand rub. Methods: This narrative review, which follows a non-systematic, thematically organised search strategy rather than PRISMA methodology, integrates current evidence on the epidemiology, pathophysiology, diagnosis, prevention, and management of OCD, with particular emphasis on the self-reinforcing cycle linking skin barrier disruption, microbiome dysbiosis, and antimicrobial-peptide dysregulation to inflammation. Results: We critically appraise the prevention evidence, foregrounding the low certainty of the existing trial base and the tension between the randomised trials of primary and secondary prevention, which have been null, and the encouraging but uncontrolled results of structured tertiary-prevention programmes. We summarise recent therapeutic advances, including topical delgocitinib, and situate the field within the World Health Organisation’s 2025 recognition of skin diseases as a global public health priority. Established evidence and hypotheses are kept separate throughout: we additionally advance, explicitly as a conjecture rather than as a demonstrated mechanism, a conceptual trans-kingdom dialogue model in which protease-generated LL-37 fragments may modulate staphylococcal quorum sensing, and each step of that model is labelled according to whether the supporting evidence is direct, extrapolated, or as yet untested. Conclusions: We argue that the prevention failure is less one of biology than of trial design and measurement, and outline the research needed to close the gap. Full article

Tobacco use remains one of the leading preventable causes of morbidity and mortality worldwide, contributing to more than 7 million deaths annually and imposing a substantial economic burden on healthcare systems and global productivity. The addictive properties of tobacco are primarily mediated by nicotine, which exerts its effects through neuronal nicotinic acetylcholine receptors (nAChRs) within brain reward circuits. Among these receptor subtypes, α4β2-containing nAChRs play a central role in nicotine dependence by regulating dopaminergic signaling associated with reinforcement, craving, withdrawal, and relapse. Repeated nicotine exposure induces neuroadaptive changes in receptor expression and neural circuitry, contributing to the chronic and relapsing nature of addiction. Advances in addiction neuroscience and receptor pharmacology have enhanced the understanding of nicotine-mediated signaling and facilitated the development of evidence-based smoking cessation therapies. Current treatment approaches include nicotine replacement therapies, antidepressant-based interventions, and partial nAChR agonists such as varenicline and cytisine. Emerging strategies encompass subtype-selective ligands, allosteric modulators, immunotherapeutics, neuromodulation techniques, and digital health technologies aimed at improving cessation outcomes. This review summarizes the neuropharmacological mechanisms underlying nicotine addiction and critically examines current and emerging therapeutic strategies, highlighting their mechanisms of action, clinical efficacy, limitations, and future potential for tobacco cessation. Full article

Older adults represent a growing proportion of patients presenting with acute coronary syndromes (ACS), yet they remain a highly heterogeneous population in terms of biological reserve, comorbidity burden, functional status, cognitive performance, and recovery potential. Chronological age alone is an insufficient basis for invasive decision-making, as it may lead to both therapeutic nihilism and disproportionate treatment escalation. Frailty has emerged as a clinically meaningful construct that captures vulnerability to acute stressors and may refine prognostic assessment beyond traditional cardiovascular risk scores. In ACS, frailty is associated with mortality, bleeding, procedural complications, delirium, functional decline, readmission, and loss of independence. However, frailty should not be interpreted as an automatic contraindication to invasive management. Rather, it should inform proportional care by integrating ischemic risk, procedural burden, reversibility potential, patient preferences, and expected quality of recovery. This narrative review examines the role of frailty assessment in older adults with ACS, focusing on its implications for invasive decision-making. We discuss frailty tools, clinical outcomes, therapeutic bias, healthcare inequities, and patient-centered endpoints. Finally, we propose a vulnerability-based framework for cardiovascular care, in which frailty guides individualized therapeutic intensity rather than justifying age-based exclusion from evidence-based treatment. Full article

The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector. Full article

Dog-assisted services are employed in healthcare, educational, social-care, and community settings, yet systems for preparing and protecting therapy dogs remain unevenly developed. This narrative review synthesises empirical, conceptual, and governance literature on therapy dog preparation in selected European contexts and the United States. Searches were conducted across academic databases, specialist repositories, and grey-literature sources, with evidence organised thematically around governance, candidate selection, training, handler competency, welfare monitoring, health and zoonotic risk management, and career lifecycle management. The synthesis shows that therapy dog governance remains fragmented, with international guidance, voluntary certification systems, national guideline frameworks, and legally embedded models differing in scope, enforceability, welfare safeguards, handler requirements, and re-evaluation. Across preparation domains, the review identifies a need to move beyond dog-level pass–fail certification toward assessment of the human–dog team within specific intervention contexts. Reward-based and consent-oriented preparation, multimodal welfare monitoring, handler stress recognition, veterinary screening, infection control procedures, workload regulation, and retirement planning emerge as central but inconsistently standardised requirements. The review proposes a tiered lifecycle framework conceptualising therapy dog readiness as a dynamic, welfare-centred, context-dependent process from selection through active service and retirement. Future research should prioritise longitudinal welfare assessment, validated selection and handler competency tools, comparative governance evaluation, positive welfare indicators, and transparent reporting of preparation variables. Full article

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge–Fog–Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96–99.47% for accuracy and 99.08–99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments. Full article

Background: High-quality chest compressions are considered a fundamental component of effective cardiopulmonary resuscitation (CPR). Although body mass index (BMI) has been suggested to be associated with chest compression performance, the available evidence remains unclear, particularly among healthcare professionals evaluated under standardized training conditions. This study aimed to investigate the association between BMI and chest compression quality following standardized Basic Life Support (BLS) training in nurses. Methods: This prospective simulation study included 284 nurses who completed a standardized 45 min BLS training program. Chest compression performance was assessed individually using an AmbuMan^® Basic CPR manikin during two minutes of uninterrupted chest compressions. Adequate compression rate, adequate compression depth, and complete chest recoil were evaluated according to the manufacturer’s performance criteria based on current ERC and AHA guidelines. Participants were classified into three BMI groups: <18.5, 18.5–24.9, and ≥25 kg/m². Multivariable logistic regression was performed to evaluate the independent association between BMI and each chest compression quality parameter, adjusting for age, sex, nursing experience, previous BLS training, and working in an intensive care unit. Results: Participants with a BMI < 18.5 kg/m² had significantly lower rates of adequate compression rate (15.0%) and adequate compression depth (45.0%) than those in the other two BMI groups (both p < 0.001). Complete chest recoil also differed significantly among BMI groups (p = 0.004). After adjustment for age, sex, nursing experience, previous BLS training, and working in an intensive care unit, BMI remained independently associated with adequate compression rate (adjusted OR 1.32, 95% CI 1.18–1.49; p < 0.001) and adequate compression depth (adjusted OR 1.29, 95% CI 1.14–1.47; p < 0.001), but not with complete chest recoil (adjusted OR 1.12, 95% CI 0.99–1.26; p = 0.072). Conclusions: Following standardized BLS training, nurses with a BMI < 18.5 kg/m² showed lower rates of adequate compression rate and depth than those with higher BMI values, while BMI was not independently associated with complete chest recoil. These findings suggest an association between lower BMI and poorer performance in selected chest compression parameters within this simulation setting. Full article

Background/Objectives: As artificial intelligence (AI) continues to be integrated in healthcare settings, it enters clinical work as a support and a source of stress. Although AI anxiety acts as a psychological barrier to consistent engagement with AI technology, evidence on its relationships with nursing outcomes such as work engagement and task performance remains limited. This study examined the associations between AI anxiety, work engagement, and task performance among nurses using the challenge–hindrance stressor framework, considering work engagement as a hypothesized mediator. Methods: A quantitative cross-sectional correlational design was used. In December 2025, 106 nurses across various hospital units in Saudi Arabia were recruited through a non-probability strategy combining purposive, convenience, and snowball techniques. Participants completed the Artificial Intelligence Anxiety Scale, the 3-item Utrecht Work Engagement Scale, and the task performance subscale of the Individual Work Performance Questionnaire. Data were analyzed using Pearson correlations and PROCESS-based mediation analysis (Model 4), with 5000 bootstrap resamples and 95% percentile confidence intervals. Results: Work engagement statistically mediated the cross-sectional association between AI anxiety and task performance; the indirect effect was significant, whereas the direct effect was not. Higher AI anxiety was positively associated with higher work engagement ($\beta =0.29$, $p=0.002$), and higher work engagement with higher task performance ($\beta =0.45$, $p<0.001$). The total effect was significant ($\beta =0.22$, $p=0.026$), while the standardized indirect effect was 0.13 (95% CI: 0.036–0.248). Conclusions: AI anxiety may be tentatively interpreted as a possible challenge stressor, with its positive association with task performance appearing to operate indirectly through work engagement. These results indicate that strengthening AI competency and work engagement may support nurses’ task performance. Full article

Background: Acute confusional state, or delirium, is a common neuropsychiatric disorder in older adults, particularly in emergency departments. It is associated with high morbidity and mortality, as well as difficult detection. Its early identification is crucial to prevent complications and improve prognosis. This study aims to identify and analyze available strategies for the early diagnosis of delirium in geriatric patients in emergency departments. Methods: A literature review and meta-analysis were conducted between 2020 and 2026 using the PubMed, Scopus, Web of Science, CINAHL, and Cochrane databases (PRISMA 2020). To estimate the prevalence of patients with delirium in emergency departments, a meta-analysis was performed using Stats Direct statistical software (Version 4). Methodological quality was evaluated according to the Oxford Centre for Evidence-Based Medicine levels of evidence. Results: Eleven studies were included, demonstrating that delirium in emergency departments is highly prevalent among older adults, particularly those with dementia, polypharmacy, or functional impairment. The 4AT scale was the most frequently used screening tool, notable for its strong sensitivity. Hypoactive delirium emerged as the most common subtype and carried the poorest prognosis. Furthermore, its presence was associated with longer hospital stays, increased complications, and higher mortality rates, while a lack of training among healthcare staff continues to limit early detection. The meta-analysis detected no publication bias and revealed a 20.6% prevalence of delirium among patients in emergency departments. Conclusions: One in five patients attending the emergency department presents with delirium. Delirium in geriatric emergency care demands a structured clinical response grounded in prevention, early detection, and professional training. Integrating validated diagnostic tools and reinforcing the role of nursing professionals are key to improving patient outcomes and safety. Full article

Background/Objectives: Perinatal care is essential for the health and well-being of women and adolescent girls from conception through the first year postpartum. The World Health Organization (WHO) provides evidence-based recommendations aimed at improving the quality of antenatal, intrapartum, and postnatal care. This study mapped 51 WHO perinatal care recommendations against Zambian guidelines to identify areas of alignment, as well as gaps and challenges related to their implementation. Methods: The review was conducted following Joanna Briggs Institute (JBI) guidance. Published recommendations and relevant documents issued from 2016 onwards were included. National guidelines, policy documents, professional standards, and literature related to perinatal care in Zambia were identified through searches of PubMed, Google Scholar, WHO resources, and relevant institutional websites. WHO recommendations were mapped against Zambian guidelines and classified according to their level of alignment. Results: The findings demonstrated significant alignment between WHO recommendations and Zambian guidelines in perinatal healthcare. However, important implementation gaps remain; for example, only 4.7% of pregnant women attended all eight recommended antenatal care visits. Intrapartum care largely aligns with WHO standards, though there are differences in pain-relief choices. Furthermore, difficulties in adopting WHO recommendations are mostly due to health system constraints. Conclusions: Most of the Zambian guidelines align with the WHO recommendations. However, ten were classified as partly aligned, and one did not align. This small proportion of non-alignment may reflect adaptation for a national context and require further evidence-based evaluation. In addition, consolidating existing recommendations, strengthening health-system capacity, and addressing implementation gaps may support the delivery of standardized, high-quality perinatal care and contribute to improved maternal and newborn health outcomes. Full article

Frailty syndrome and Alzheimer’s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, a frailty dataset based on gait and mobility parameters and an AD dataset with clinical, functional and lifestyle variables, in order to evaluate and compare their classification performance using machine learning. Features were optimized using dimensionality reduction techniques to keep predictors of clinical significance and hyperparameter optimized Random Forest models were built to develop the best model. Evaluation was performed with Accuracy, F1-score, Matthews Correlation Coefficient and Area Under the Curve. The results showed that the models constructed on the whole AD dataset achieved maximum predictive power with an accuracy of 0.946, which was slightly increased to an accuracy of 0.948 after the selection of significant features. Diagnostic models based on frailty were able to demonstrate an ACC predictive capacity of 0.6418, and in terms of feature selection, improvements appeared in all indicators. Regarding the features derived from Alzheimer’s disease associated with geriatric frailty, they managed to surpass the ACC frailty features of 0.741 alone, suggesting some intercalation mechanisms between neurodegeneration and physical vulnerability. These findings show that machine learning algorithms accompanied by feature selection improve clinical discrimination and prediction of frailty and neurodegenerative disorders, which offers a promising aspect for geriatric assessment. The frailty models analyzed demonstrated an ACC predictive capacity of 0.6418, even though feature selection improved all indicators. Alzheimer’s disease-derived features associated with frailty outperformed features in the frailty dataset with an ACC of 0.741, suggesting the mechanism of overlap between neurodegeneration and physical vulnerability. These results support the theory of a motor-cognitive aging continuum, indicating that algorithmic machine learning techniques coupled with feature selection mainly provide computational validation for the biological intersection of neurodegeneration and physical frailty, rather than forming an independent predictive clinical model. Using these algorithms the study highlights shared pathophysiological mechanisms, providing a significant insight into systemic geriatric deterioration. Full article

Gout is one of the most common inflammatory arthritides worldwide, and its burden is rising in Southeast Asia alongside rapid demographic and socioeconomic transitions. This region, characterized by diverse healthcare systems, cultural practices, and levels of economic development, presents a unique context in which the epidemiology and management of gout are evolving. Despite the availability of effective urate-lowering therapy (ULT) and well-established treat-to-target (T2T) strategies, real-world outcomes remain suboptimal. Barriers to optimal gout care operate at multiple levels. Physician-related factors include clinical inertia and limited awareness of evidence-based recommendations. Patient-related factors include limited understanding of gout and its treatment, compounded by socioeconomic disadvantages. System-level challenges include inequitable access to healthcare services, limited health insurance coverage, and resource-strained health systems. The current evidence base in Southeast Asia remains limited and heterogeneous, raising uncertainty regarding the applicability of global data to local populations. Nevertheless, emerging studies suggest that multidisciplinary, context-oriented approaches can improve gout outcomes. The objective of this review is to explore the evolving epidemiological landscape and barriers to gout management in Southeast Asia. Full article

The rapid deployment of Internet of Things (IoT) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices are often constrained by limited processing capacity, memory, battery power, and communication bandwidth, making conventional security mechanisms difficult to deploy consistently at scale. Intrusion detection systems (IDSs) provide an important defensive layer; however, many machine-learning-based IDSs are developed under static assumptions and may experience performance degradation as traffic distributions evolve due to firmware changes, device onboarding, protocol updates, user behavior variation, or adaptive attacks. This paper presents a hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring. In the experimental pipeline, Isolation Forest is trained exclusively on benign traffic to ensure that the anomaly detector models normal behavior rather than an attack-dominated training distribution. The evaluation uses a large-scale chronologically sampled subset of the CICIoT2023 dataset containing 3,890,621 records while preserving the natural class distribution of 2.35% benign traffic and 97.65% attack traffic. The chronological 80/20 train/test split is established first at the file level, followed by systematic sampling within each split to reduce the risk of leakage across the evaluation boundary. On the 746,094-record test set, the proposed hybrid IDS achieved 99.73% accuracy, 99.89% precision, 99.83% recall, 99.86% F1-score, and a false positive rate of 4.77%. The corresponding confusion matrix contains TN = 16,683, FP = 836, FN = 1205, and TP = 727,370, yielding 95.23% specificity and 97.53% balanced accuracy. Standalone Random Forest marginally outperformed the hybrid model in raw accuracy and false positive rate; therefore, the contribution of the proposed framework is centered on deployment-oriented anomaly monitoring, drift awareness, and generalization rather than absolute superiority in static classification metrics. A leave-one-attack-family-out experiment withholding MITM-ArpSpoofing from training showed that the hybrid model detected 85.26% of the unseen attack-family samples, compared with 85.18% for Random Forest alone and 7.05% for Isolation Forest alone. These findings provide initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability. The framework is therefore positioned as a competitive IDS that combines supervised detection with anomaly monitoring and concept drift awareness for deployment-oriented IoT security. Full article

Early childhood dental caries (ECC) remains one of the most prevalent and preventable chronic diseases affecting young children globally, yet behavioral and contextual determinants of its onset during the first 1000 days of life remain poorly understood and infrequently modeled in Latin American populations. This study presents a novel application of the PRECEDE diagnostic framework to conceptualize Early Childhood Dental Caries during the first 1000 days (1000ECDC) as a behaviorally rooted, socio-ecologically conditioned health problem. Drawing on a formative longitudinal pilot study (n = 10 mother–infant dyads, Caracas, Venezuela) and a complementary narrative literature review, we develop the first PRECEDE-based conceptual model linking maternal prenatal and postnatal behaviors to dental caries risk in a Latin American context. The model identifies dietary and feeding behaviors, oral health care practices, and healthcare service utilization as primary behavioral risk factors, modulated by predisposing factors (low oral health knowledge, limited self-efficacy, cultural norms), enabling factors (socioeconomic constraints, fragmented health systems), and reinforcing factors (social norms, family influence). Exploratory pilot findings indicated that more than 60% of children had advanced dental caries lesions at 24-month follow-up, consistent with regional estimates and underscoring the urgency of early intervention. This model provides practitioners and policymakers with a structured, evidence-informed diagnostic tool to guide the design of early-life oral health promotion programs, with particular relevance for low-resource Latin American settings. Future validation through expert consensus and prospective studies is warranted. Full article

Refugees and asylum seekers in Germany face significant barriers to accessing routine dental care, leading to untreated conditions that escalate into costly emergency hospital admissions and increased public healthcare expenditures. This study evaluates the economic impact of an NGO-led dental care facility designed to address this critical gap in care for uninsured populations. Using a retrospective cost-effectiveness analysis, we compare three scenarios: (1) the NGO intervention, (2) the “status quo” reliance on emergency care, and (3) a dental clinic arm-based model. We test the hypothesis that NGO-led interventions reduce public healthcare costs by curbing preventable emergency admissions, thereby addressing systemic policy and market failures. Results demonstrate that the NGO facility is a cost-effective solution, generating a return of €0.60 for every euro invested, while the alternative scenarios yielded no financial returns. By providing equitable, preventive dental care, the NGO model reduced emergency admissions by addressing delayed treatment-seeking behaviors and structural access barriers. These findings confirm that NGO-led interventions can mitigate market failures by serving as a Pareto-improving solution, optimizing resource allocation and reducing long-term fiscal burdens. The study underscores the potential of NGOs to complement public health systems in achieving equitable and sustainable healthcare delivery. Policymakers should consider scaling such models to alleviate disparities in underserved populations while curbing avoidable costs linked to emergency care. This research contributes critical evidence for integrating NGO-led initiatives into healthcare strategies, particularly in contexts marked by fragmented access and systemic inefficiencies. Full article