Search Results (1,220)

Large-span bridges are critical components of transportation networks. Environmental variability, material degradation, and cumulative fatigue continuously affect their long-term performance. Digital Twin (DT) technology has emerged as a promising paradigm for integrating sensing, modeling, and data analytics. Most existing DT implementations in civil infrastructure rely on dense sensor networks, assume near-complete observability, and primarily serve as passive visualization or diagnostic tools, limiting their scalability and practical applicability. This paper proposes a DT framework specifically designed for the monitoring and management of existing large-span bridges under sparse sensing conditions. The framework adopts an information-centric perspective in which limited physical measurements are complemented by full-field state reconstruction through the integration of physics-based modeling, data-driven learning, and uncertainty-aware inference. A synchronized reference configuration, termed State 0, is introduced as the initial basis for tracking structural changes over time, while allowing conditional re-baselining through a Dynamic State 0 (DS0) when verified reassessment justifies it. On this basis, the proposed DT is formulated as an adaptive and decision-oriented cyber–physical system that supports optimization-based recommendations for sensing, inspection, and maintenance planning. Full article

Background: Farm-level antimicrobial stewardship in swine production requires indication-specific knowledge of treatment patterns and the herd-level features associated with them. Methods: We analyzed questionnaire-based data collected in 2015 from 13 Hungarian swine farms covering 15,725 sows and their progeny. The survey captured production indicators, pathogen occurrence, vaccination, resistance-testing practices, drug costs, and disease-group-specific antimicrobial use. As a separate, non-mergeable descriptive temporal comparator, we also considered independent digital farm-monitoring data from three large-scale swine herds from 2022 to 2024. Results: The most frequently reported pathogens were Mycoplasma hyopneumoniae (13/13 farms), Lawsonia intracellularis (12/13), Escherichia coli (12/13), swine influenza virus (11/13), and Streptococcus suis (10/13). S. suis ranked as the leading damaging pathogen on 69% of farms. Among farms with antibiotic cost data (9/13), antibiotics accounted for a mean of 31.8% of veterinary drug expenditures. Among farms with treatment-by-indication data (8/13), the highest relative frequency of reported treatment events was linked to porcine respiratory disease complex, where doxycycline represented 38% of reported PRDC treatment events. Colistin dominated E. coli-associated diarrhea control, whereas beta-lactams were central for S. suis-related disease. In the 2022–2024 comparator dataset, enteric and respiratory disorders and arthritis remained the main recorded health problems, but corrected antimicrobial use was markedly lower in the later dataset. Conclusions: Antimicrobial use showed clear disease-group-specific patterns, supporting syndrome-focused stewardship rather than generic reduction targets. Full article

Background: Wastewater-based surveillance (WBS) has emerged as a valuable tool for population-level monitoring of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission, yet the interplay between sampling strategies and disease prevalence in shaping detection performance remains ambiguous. We investigated how grab and composite sampling influence SARS-CoV-2 ribonucleic acid (RNA) detection dynamics and predictive lag times across high- and low-prevalence communities in Selangor, Malaysia. Methods: A 28-week longitudinal study was conducted in Selangor, Malaysia, comparing grab and composite wastewater sampling in communities with high and low Coronavirus disease 2019 (COVID-19) prevalence. SARS-CoV-2 RNA in 348 samples was quantified using digital Reverse Transcription Polymerase Chain Reaction (RT-dPCR), and viral lineages were characterized by Nanopore sequencing. Detection sensitivity and lead times relative to reported cases were evaluated. Results: In low-prevalence settings, grab sampling showed higher detection sensitivity than composite sampling (92.0% vs. 70.0%), whereas both methods achieved similarly high detection in high-prevalence areas (>97.0%). Lag-time analysis indicated that grab sampling in high-prevalence settings was significantly associated with case trends at potential two-week lead (p = 0.024), while composite sampling in low-prevalence settings showed the strongest association at a potential one-week lead (p = 0.0022). Overall, lag structures varied by both sampling strategy and prevalence context. Both sampling approaches captured the replacement of Omicron sublineages (XBB.1.5, XBB.1.9.1, XBB.1.16) and identified additional circulating variants, including EG.5, that were not captured in the available clinical sequencing dataset during the same period. Conclusions: These findings reveal that local transmission intensity is associated with the utility of different sampling designs. Context-specific optimization of WBS sampling strategies enhances sensitivity, reduces detection lag, and strengthens early warning and genomic-tracking capacity in public health surveillance frameworks. Full article

Background: Generative AI (GenAI) mental health chatbots are increasingly being developed to help address persistent barriers to mental healthcare. Unlike earlier rule-based and retrieval-based systems, GenAI chatbots generate open-ended outputs that can be inaccurate and unsafe. Documented harms from general-purpose GenAI chatbots have highlighted the need for purpose-built interventions with dedicated safeguards, yet how safety is implemented in such interventions remains poorly understood. Methods: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, with a prospectively registered and peer-reviewed protocol. A systematic search of seven academic databases and search engines including MEDLINE, Scopus, PsycINFO, ACM Digital Library, IEEE Xplore, Google Scholar and Consensus was conducted in July 2025. Two reviewers independently screened records and extracted data. Safety mechanisms and risk mitigation strategies were narratively synthesised across three pre-specified domains: technical safeguards, pre-deployment safety considerations, and delivery-phase risk mitigation strategies. Results: Twenty-one studies across 11 countries were included. Most interventions incorporated at least one technical safety mechanism, most commonly fine-tuning and prompt engineering. A smaller subset implemented layered safety architectures combining retrieval systems, content filters or risk classifiers, and rule-based algorithms. Pre-deployment safeguards included clinical expert and user co-design approaches, research ethics procedures, and data privacy measures. During intervention delivery, detailed onboarding with role clarification was common, but human oversight was limited. Crisis referral protocols varied in rigour but were mostly underdeveloped, and systematic adverse event monitoring was sparse. Documented safety failures included missed suicidal ideation and provision of inaccurate clinical information. Conclusions: GenAI chatbot interventions require a robust sociotechnical approach that integrates technical safeguards with user co-design, procedural controls, and human oversight. Future research is needed to evaluate efficacy, improve safeguards and standardise safety outcome measurement. Regulatory oversight proportional to the risks these systems carry is required to enable integration into stepped or blended mental healthcare. Full article

Background: Arterial hypertension is one of the most prevalent chronic non-communicable diseases and a leading cause of cardiovascular morbidity and mortality worldwide. Its burden remains particularly high in rural and resource-limited settings, where access to healthcare is often constrained by shortages of healthcare professionals, geographical barriers, and underdeveloped infrastructure. These factors may contribute to delayed diagnosis, suboptimal disease control, and increased risk of complications. In this context, telemedicine has emerged as a useful approach to supporting hypertension management and improving access to care in rural populations. Methods: This study presents a narrative review of the literature focusing on the application of telemedicine in the management of arterial hypertension in rural populations. A structured literature search of PubMed, Scopus, and Web of Science databases was conducted for studies published between 2015 and 2025. The review included randomized controlled trials, systematic reviews, meta-analyses, and observational studies evaluating telemedicine interventions, including remote blood pressure monitoring, mobile health applications, and teleconsultations. Study selection was guided by relevance to the research objective, with particular attention to rural and resource-limited contexts. Results: Telemedicine interventions have been associated with improvements in blood pressure control, treatment adherence, and access to healthcare services. Evidence from randomized controlled trials and meta-analyses suggests modest reductions in systolic and diastolic blood pressure compared with standard care. However, a substantial proportion of the available evidence originates from studies conducted in general or mixed populations rather than exclusively rural settings. Therefore, the applicability of these findings to rural contexts remains limited and should be interpreted with caution. The effectiveness of telemedicine may vary depending on differences in healthcare infrastructure, resource availability, digital accessibility, and organizational models across healthcare systems. Integrated care approaches involving primary healthcare providers and specialist support may contribute to improved continuity of care, although their impact appears to be context-dependent. Key barriers include limited telecommunication infrastructure, digital literacy challenges, and difficulties in integrating telemedicine into routine clinical practice. Conclusions: Telemedicine may represent a useful approach to supporting hypertension management in rural populations. However, its implementation requires careful consideration of local healthcare systems, patient characteristics, and organizational context. Telemedicine should be viewed as a context-dependent strategy rather than a uniform solution. Further context-specific research is needed to evaluate the long-term clinical, organizational, and economic impact of telemedicine interventions in rural hypertension management. Full article

This paper presents a fully automated platform for real-time monitoring of fatigue-induced microstructural changes in metallic materials, using Rayleigh surface waves and Laser Doppler Vibrometry (LDV). The system integrates ultrasonic excitation, non-contact optical sensing, and high-speed signal processing in a unified LabVIEW environment. Rayleigh waves are generated via a contact transducer, while LDV captures surface vibrations with sub-nanometric velocity resolution, ensuring repeatability and eliminating coupling variability. The software automates synchronization, deterministic data acquisition, filtering, FFT analysis, and extraction of nonlinear coefficients (β₂, β₃) at high execution rates without the need for post-processing. Experimental validation under cyclic loading revealed a clear sensitivity hierarchy: the Rayleigh wave velocity remained invariant, the acoustic attenuation responded gradually, while the nonlinear parameters exhibited the earliest and steepest response to fatigue damage, confirming their superiority as early-stage indicators. The system offers low-latency timing, long-term stability, and modular design, establishing a robust data-streaming foundation that can support future integration with digital twin frameworks and machine learning models. Furthermore, the acoustic findings were successfully cross-validated using Infrared Thermography, which confirmed the critical damage transition phase. This work bridges nonlinear acoustics and software automation, providing a scalable diagnostic solution for predictive maintenance within structural health monitoring systems. Full article

Background/Objectives: Telemedicine and remote patient monitoring have emerged as promising strategies to improve outcomes in heart failure (HF), but prior meta-analyses reported conflicting results, partly due to insufficient differentiation between intervention modalities. This systematic review and meta-analysis evaluated the impact of distinct telemedicine strategies on clinically relevant outcomes in HF. Methods: Conducted according to PRISMA 2020 and a prospectively registered PROSPERO protocol (CRD420261355507), this analysis included randomized controlled trials (RCTs) comparing telemedicine-based strategies—non-invasive telemonitoring, structured remote patient management (RPM), or haemodynamic-guided monitoring—against standard care, identified through searches of PubMed/MEDLINE, Embase, and CENTRAL (inception to 15 March 2026). Random-effects meta-analyses (DerSimonian–Laird) were performed, with predefined subgroup, sensitivity, and publication bias analyses. Results: Sixteen RCTs (n = 8618) were included. Telemedicine significantly reduced all-cause mortality (RR 0.82, 95% CI 0.73–0.92; I² = 34%; GRADE: moderate), all-cause hospitalization (RR 0.79, 95% CI 0.71–0.88; GRADE: moderate), HF-related hospitalization (RR 0.68, 95% CI 0.59–0.78; GRADE: high), and composite outcomes (RR 0.75, 95% CI 0.67–0.84; GRADE: moderate). A prespecified subgroup analysis revealed a significant mechanistic gradient (p for interaction = 0.008): haemodynamic-guided monitoring conferred the largest mortality reduction (RR 0.71), followed by structured RPM (RR 0.79), whereas non-invasive telemonitoring alone did not reach statistical significance (RR 0.93; p = 0.14). Conclusions: Telemedicine-based strategies yield clinically meaningful reductions in mortality and hospitalization in HF, but benefit is contingent upon intervention intensity and physiological specificity. Haemodynamic-guided monitoring and structured RPM provide robust outcome reductions, whereas passive telemonitoring alone is insufficient. These findings support consideration of structured remote patient management and haemodynamic-guided monitoring in appropriately selected patients and settings, while implementation and comparative effectiveness research remains necessary. Full article

The growing use of quantified self-technologies (QST) in chronic disease management is linked to better self-monitoring and patient engagement. However, little is known about how patients in resource-constrained settings fund and sustain the use of QST in diabetes self-management. This study asked: “How do patients with diabetes perceive and experience the economic burden of using QST in Zimbabwe?” Using a qualitative design, 20 patients with diabetes participated in semi-structured interviews. The reflexive thematic analysis of Braun and Clarke generated three interrelated themes: technology investment costs, conventional healthcare costs, and socio-economic constraints. The findings show that the economic experience of QST adoption is context-dependent and is shaped by the financial realities of patients with diabetes and their access to technology. By focusing on patient-level cost experiences, the study adds qualitative evidence to public health debates on digital health affordability and highlights the need to assess perceived financial implications within a third-world socio-economic context. It is concluded that, although QST is available in third-world countries, sustained use depends on the financial capacity of patients with diabetes. Full article

Background: Chronic pain remains a primary driver of global disability and impaired quality of life. While digital conversational agents (CAs) have emerged as scalable tools for symptom monitoring and self-management via patient-reported outcome measures, their clinical efficacy remains poorly synthesized. This systematic review and meta-analysis aimed to evaluate the impact of CA-based interventions on PROMs in adults with chronic pain. Methods: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched from inception to 22 October 2025. Eligible studies were RCTs including adults with chronic pain and evaluating fully automated CA interventions, such as digital coaching or messaging programs. PROMs related to pain, well-being, disability, and work-related outcomes were extracted. Continuous outcomes were synthesized using standardized mean differences (SMDs) with 95% confidence intervals (CIs). Results: Five RCTs involving 572 participants were included. Interventions were self-guided, digitally delivered, and lasted 4 to 12 weeks. The overall pooled analysis suggested a potential benefit of CA-based interventions on PROMs (SMD = −0.43; 95% CI −0.55 to −0.31; p < 0.00001), although heterogeneity and risk of bias across studies warrant cautious interpretation. Improvements were observed particularly in pain intensity, although evidence for other outcomes was less consistent, with some studies reporting benefits in quality of life, fear of movement, and well-being. Conclusions: CA-based interventions may have potential as adjuncts in chronic pain management; however, the current evidence is limited and should be interpreted with caution due to heterogeneity and risk of bias across studies. These tools may represent a scalable solution for supporting remote symptom monitoring and self-management within digital health frameworks, although further high-quality evidence is required. Full article

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), biofluid interfaces (sweat, interstitial fluid, and saliva), and materials engineering strategies enabling flexible, high-performance devices. Emphasis is placed on biofluid transport dynamics, analytical performance across sampling matrices, and system-level integration with wireless communication and digital health platforms. Key challenges limiting clinical translation, including biofouling, enzymatic instability, and variability in biofluid composition, are examined—alongside emerging solutions such as antifouling interfaces, synthetic recognition elements, and multimodal sensing architectures. Finally, regulatory pathways and the role of artificial intelligence in digital nephrology are discussed. This review highlights the potential of wearable biosensors to transform CKD management through continuous monitoring, early detection, and personalized therapeutic intervention. Full article

Artificial intelligence decision systems are increasingly deployed in safety-, policy-, and human-sensitive settings where actions must satisfy feasibility constraints under incomplete information. Existing post-deployment monitoring approaches can detect observable failures, distribution shifts, or performance degradation, but they cannot, by themselves, determine whether feasibility can be guaranteed when safety-relevant latent states remain indistinguishable at decision time. This paper develops a formal framework for reliable deployment and monitoring of AI decision systems under fixed observation structures. We model deployment through latent states, observations, observation-consistent state sets, state-wise feasibility constraints, and observation-based policies. The framework characterizes when feasibility-guaranteed deployment is structurally possible, when it requires intervention, and when it is impossible without modifying the information structure. We prove that every task falls into one of three regimes: deployable and automatically measurable systems, non-automatically deployable but remediable systems, and hard non-deployable systems. We further introduce an operator-assisted review and rollback mechanism for remediable cases and show that additional data or monitoring alone is insufficient unless such measurements refine feasibility-relevant latent-state ambiguity. Empirical examples and a digital health case study illustrate how the framework supports practical deployment assessment, monitoring design, and human-in-the-loop safeguards for AI systems operating under partial observability. Full article

Artificial intelligence (AI) is becoming a central driver of change across the drug development lifecycle. However, its integration is evolving so rapidly that it remains essential to understand how these technologies are currently positioned within the field. Because reliable access to high-quality (effective and safe) drugs is essential to public health, the pharmaceutical product lifecycle (PPL) offers a coherent framework for evaluating how AI can enhance evidence and data creation across all stages. To understand where AI genuinely adds value, this review examines its contribution across the major stages of the PPL. Rather than treating drug discovery, nonclinical evaluation, clinical research, and post-marketing assessment as separate domains, we view them as a continuous chain of data, where digital technologies enhance different decision points in distinct ways. In early discovery, AI narrows the search space by integrating diverse datasets to prioritize candidates most likely to succeed. Nonclinical models increasingly rely on machine-learning systems designed to improve the human relevance of safety predictions. Within clinical trials, AI supports cohort formation, real-time monitoring, and new analytic strategies that supplement empirical evidence. Case studies from leading pharmaceutical companies illustrate that the most meaningful advances emerge when AI is embedded not as a standalone tool but as part of a broader data strategy that links information across stages. Taken together, current evidence suggests that AI is beginning to transform data generation and integration throughout the PPL. Given the accelerating pace of digital innovation, it is essential for the field to maintain continuous awareness of emerging methodologies and evolving regulatory frameworks to ensure that these technologies are implemented in a reliable, transparent, and scientifically grounded manner. Full article

Ultrasonic Guided Wave (UGW)-based Structural Health Monitoring (SHM) is a promising strategy for detecting damage to aeronautical structures, although its application is complicated by signal complexity and experimental uncertainty. This work seeks to identify damage-sensitive signal features for integration into Machine Learning (ML) frameworks, offering physics-informed indicators. The study combined experimental monitoring of damage to Carbon Fibre Reinforced Polymer (CFRP) plates and finite element models. To overcome the numerical–experimental mismatch, an ML algorithm predicted experimental characteristics from numerical data. The robustness of the model was validated by extrapolation (prediction of future damage) and generalization (prediction on unseen plates) strategies, confirming that ML can robustly correct for uncertainty. These results validate hybrid strategies that feed Digital Twin approaches to structural diagnosis and real-time forecasting. Full article

Background/Objectives: The rapid expansion of telemedicine has reshaped healthcare delivery, positioning telenursing as essential for continuity of care and patient management. This scoping review maps current evidence on telecare nursing practices, examining organizational models, professional roles, and key clinical and organizational outcomes. Methods: The review was conducted across five international databases, following the methodological framework proposed by Arksey and O’Malley, the interpretive extension by Levac et al., and the Joanna Briggs Institute guidelines, with reporting aligned to PRISMA-ScR recommendations. The search identified 1760 records, of which 1219 remained after duplicate removal. After title and abstract screening and full-text evaluation, 25 studies met the inclusion criteria. Results: Telenursing was implemented across diverse clinical contexts, particularly in chronic disease management, oncology, postoperative care, and emergency settings. Evidence indicates improvements in symptom management, therapeutic adherence, quality of life, and complication reduction, suggesting positive clinical and organizational impacts. The literature highlights the need for advanced digital, communication, and relational competencies, emphasizing the importance of targeted professional training. Cross-cutting trends include enhanced continuity of care, greater patient autonomy, improved integration between hospital and community services, and reduced healthcare costs. Conclusions: This review provides an updated overview of telenursing applications, highlighting their adaptability across clinical settings and the expanding strategic role of nurses in digital care. The findings indicate a rapidly evolving field and emphasize the need for further research to strengthen organizational frameworks, define advanced competencies, and support the sustainable integration of telenursing into healthcare systems. Full article

The digital transformation of health systems and the increasing adoption of data-driven public health strategies have intensified the need for methods capable of capturing, structuring, and analyzing information derived from clinical interactions. In the Brazilian Unified Health System (SUS), orthopedic rehabilitation and therapeutic exercise prescription rely heavily on communication between healthcare professionals and patients, particularly with regard to understanding instructions, reporting symptoms, and identifying barriers to treatment continuity. However, much of this information remains embedded in unstructured spoken interactions, limiting its use for monitoring and evaluation purposes. This study presents a prospective methodological protocol for the future development and validation of a speech analytics architecture designed to analyze verbal interactions in orthopedic rehabilitation within the SUS. The proposed framework integrates automatic speech recognition, speaker diarization, semantic processing with large language models (LLMs), biomedical entity extraction, and retrieval-grounded analytical components to generate structured indicators from clinical speech. In addition, the manuscript includes an illustrative simulation based on administrative proxy data converted into synthetic narratives in order to exemplify the expected structure of downstream analytical outputs. This simulation does not constitute validation of the full audio-based pipeline, but rather serves to clarify the proposed analytical workflow. Overall, the protocol establishes a structured methodological basis for future empirical studies aimed at evaluating the technical performance, semantic validity, and potential public health utility of speech analytics in rehabilitation monitoring, under appropriate ethical, regulatory, and data protection safeguards. Full article