Search Results (743)

Although compression therapy is widely used to improve wound healing, selecting the appropriate pressure remains a challenge in clinical practice. This work proposes an intelligent patch integrated into a bandage that allows for the simultaneous monitoring of the applied pressure and wound condition using Near-Field Communication (NFC). The proposed patch integrates a force-sensitive resistive sensor to measure pressure and a capacitive sensor to detect wound exudate through capacitance variations. Capacitance is obtained by analyzing the delay in the stepwise response of the sensor, while resistance is measured from the voltage drop across a resistive divider, which is read by a microcontroller’s analog-to-digital converter. The system is powered wirelessly through NFC energy harvesting, triggered by a mobile device that acts as a reader. The NFC module can be moved away after measurement to improve patient comfort or remain integrated into the dressing for periodic monitoring. Experimental results demonstrate pressure measurements up to 140 mmHg and exudate detection up to 200 $\mathsf{\mu }$L, confirming the feasibility of battery-free NFC smart bandages for therapeutic monitoring based on wound compression. Full article

Individuals with cystic fibrosis (CF) face significant treatment burdens, and as life expectancy has increased, there is growing emphasis on their psychosocial well-being. Prevalence data indicate that approximately one-quarter to one-third of individuals with CF and their caregivers experience clinically significant anxiety or depression. Specifically, pooled global estimates report an anxiety prevalence of 24.9% (95% CI: 20.8–28.9%) and depression prevalence of 13–33% in adults with CF, with caregivers experiencing even higher rates (anxiety: 35–38%; depression: 20–35%). Depression is independently associated with a nearly twofold increase in mortality risk and substantially higher healthcare costs, underscoring its prognostic significance. These mental health comorbidities are consistently associated with reduced treatment adherence, diminished quality of life, increased healthcare utilisation, and decreased survival. Accordingly, psychological well-being has emerged as a key patient outcome that directly shapes engagement with care and the effectiveness of long-term CF management. International CF guidelines now recommend routine mental health screening within multidisciplinary care frameworks. Evidence-based interventions include cognitive–behavioural therapy (CBT), which is endorsed as a primary treatment, although access remains limited, and stepped-care pharmacotherapy, primarily selective serotonin reuptake inhibitors (SSRIs), for moderate to severe symptoms. Telemedicine and other digital health approaches have expanded access to psychological support, with remote CBT and online programmes demonstrating feasibility and symptom improvement during the COVID-19 pandemic and beyond. The advent of CFTR modulator therapies has significantly altered clinical outcomes, enabling many patients to achieve improved lung function and daily functioning. Nevertheless, mental health challenges persist, as individuals navigate new identity shifts and anxieties despite enhanced physical health. The implementation of mental healthcare remains inconsistent; while screening rates have increased, timely follow-up and integrated psychosocial support are frequently insufficient across care centres. This narrative review highlights the ongoing need to integrate mental health management into CF care to optimise adherence, patient outcomes, and long-term survival in the current therapeutic landscape. Full article

Heart failure with preserved ejection fraction (HFpEF) is a prevalent and heterogeneous syndrome with limited therapeutic options, making accurate risk stratification essential yet challenging. Traditional tools such as the H2FPEF and HFA-PEFF scores incorporate few variables and demonstrate modest prognostic performance. Machine learning (ML) offers enhanced risk prediction by integrating multidimensional clinical, imaging, biomarker, and molecular data. This review summarizes current ML applications in HFpEF, including random forests, gradient boosting, support vector machines, and deep learning, highlighting their superior discrimination and ability to reveal phenotypic subgroups with distinct outcomes. We also address practical considerations such as interpretability, real-world validation, and integration into clinical workflows, as well as challenges related to data bias, generalizability, and regulatory requirements. Future opportunities include real-time clinical decision support, digital health integration, and interventional ML to guide personalized therapy. ML holds significant potential to advance precision care and improve outcomes in HFpEF. Full article

The objective of this study was to evaluate the efficacy of a novel digital platform (Visitrain VG, Alicante, Spain) as a visual rehabilitation tool for patients with convergence insufficiency (CI), in comparison with conventional in-office vision therapy (VT) supplemented with home reinforcement exercises. A retrospective comparative study was conducted comprising 33 patients diagnosed with CI, allocated into two groups: a digital group (DG; n = 16) receiving treatment with the aforementioned digital platform and a conventional group (CG; n = 17) undergoing conventional vision therapy. Binocular vision clinical parameters were assessed at baseline, one month, and three months of follow-up, including near point of convergence (NPC), positive fusional vergence (PFV), and binocular accommodative facility (BAF). Both groups demonstrated significant improvements following three months (p < 0.050). At the one-month evaluation, the CG showed a more rapid clinical response, with statistically significant between-group differences being observed in the NPC (p = 0.004) and near PFV (p = 0.040) compared with the DG. Nevertheless, at the three-month follow-up, no significant differences were found between the groups (p ≥ 0.060). The digital platform under investigation appears to constitute an effective therapeutic alternative to conventional vision therapy, albeit with a comparatively slower initial clinical response rate. It may be particularly indicated for patients requiring greater scheduling flexibility or those with limited access to in-office clinical care. Prospective controlled clinical trials are warranted to corroborate these preliminary outcomes. Full article

Background: Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease. Intestinal barrier impairment represents a core pathogenic mechanism and a key therapeutic target for achieving mucosal healing and sustained remission. Methods: This narrative review summarizes intestinal barrier structure, disruption mechanisms, barrier-targeted therapies, and non-invasive monitoring approaches. A reproducible literature search was conducted in PubMed, Web of Science, and ClinicalTrials.gov from 2015 to 2026. Results: Barrier disruption in UC involves genetic susceptibility, proinflammatory cytokines, zonulin-mediated tight junction injury, gut microbiota dysbiosis, decreased short-chain fatty acids and secondary bile acids, impaired autophagy, and an abnormal mucin 2 (MUC2)-dependent mucus layer. Validated non-invasive monitoring tools include fecal calprotectin/lactoferrin, intestinal ultrasound, diffusion-weighted magnetic resonance imaging (MRI), and intravoxel incoherent motion (IVIM). Emerging therapies focus on tight junction stabilization, epithelial regeneration, autophagy regulation, MUC2 restoration, and microbiota modulation. Conclusions: Intestinal barrier dysfunction drives the initiation and progression of UC. Barrier-based monitoring and targeted repair strategies improve UC management. Future studies should develop personalized therapies, precise microbiota engineering, and multi-dimensional digital evaluation systems. Full article

Rationale and objectives: Parkinson’s disease (PD) significantly affects visual function, especially convergence and eye movements, impacting tasks such as reading. The objective was to investigate preliminarily the impact of the use of digital visual training in PD patients with associated convergence insufficiency (CI). Materials and methods: Pre–post pseudo-experimental pilot study to evaluate the impact of a novel digital therapy system (video game for use on a mobile phone or tablet) in 13 patients with PD and CI, with a mean age of 67 years. A comprehensive visual assessment was performed before and after a 6-week home-based visual rehabilitation, including measurement of near point of convergence (NPC), near positive fusional vergence (PFV), oculomotor tests (NSUCO and King-Devick tests), and symptom assessments with two validated questionnaires (CISS and SQVD). Results: Treatment adherence was variable, ranging from 0.8% to 124.7%. Despite this, significant improvements were found after therapy in break (p = 0.022) and recovery points of the NPC (p = 0.007), as well as break (p = 0.003) and recovery points in near PFV (p < 0.001). In the NSUCO test, the total score improved significantly from 23.9 ± 4.2 to 26.2 ± 3.7 after therapy (p = 0.003). Furthermore, a significant reduction in the total King-Devick test time was observed, decreasing from 79.4 ± 28.8 s to 69.0 ± 21.5 s with therapy (p = 0.034). Finally, symptom questionnaire scores also decreased significantly with therapy (CISS p = 0.037, SQVD p < 0.001). Conclusions: The digital vision therapy system evaluated seems to improve oculomotor control and reduce visual symptoms associated with CI in PD patients. Studies with larger sample sizes and a control group are needed to fully validate the therapeutic effectiveness of this tool. 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

Background/Objectives: The efficacy of topical therapies in dermatology is often limited by the barrier function of the stratum corneum, which restricts drug penetration. Iontophoresis is a non-invasive transdermal delivery technique that uses a low-intensity electrical current to enhance the transport of charged and polar molecules across the skin. It has emerged as a strategy to improve local drug bioavailability while minimizing systemic exposure. We systematically reviewed the clinical evidence on the efficacy, safety, and pharmacologic performance of iontophoresis-assisted topical drug delivery in dermatologic diseases. Methods: This systematic review followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420251234877). PubMed, Embase, Web of Science, CENTRAL, and ClinicalTrials.gov were searched through 19 November 2025 without language restrictions. Records were screened against predefined eligibility criteria, and data were extracted on study design, participants, dermatologic indications, intervention/comparator, iontophoresis parameters, efficacy outcomes, and adverse events. The risk of bias was assessed using RoB 2 for randomized trials and the JBI checklist for non-randomized studies. Because of substantial clinical and methodological heterogeneity, the findings were synthesized narratively and no meta-analysis was performed. Results: Twenty-one studies published between 1990 and 2025 met the inclusion criteria, including 15 randomized and 6 non-randomized studies. Investigated conditions included psoriasis, eczema, melasma, post-inflammatory hyperpigmentation, herpes labialis, onychomycosis, chronic ulcers, systemic sclerosis-related digital ulcers, acne scarring, and actinic keratosis. Across studies, findings were mixed. The most consistent signals of benefit were observed in pigmentary disorders and infectious diseases, whereas results were more heterogeneous in inflammatory dermatoses and some studies did not show superiority over active comparators. Tolerability was generally favorable, with adverse events limited to mild, reversible local reactions such as erythema, tingling, burning, or transient irritation. No serious treatment-related adverse events were reported. Conclusions: Iontophoresis may represent a useful non-invasive delivery-enhancement strategy in selected dermatologic settings, particularly when topical efficacy is limited by anatomical or physicochemical barriers. However, heterogeneity in protocols, formulations, outcomes, and clinical indications limits direct comparison and does not support broad conclusions of efficacy across all dermatologic conditions. Larger, standardized trials are needed to clarify its therapeutic role, long-term efficacy, and indication-specific benefit. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Background: Brugada Syndrome (BrS) is a cardiac arrhythmia associated with an increased risk of ventricular arrhythmias and sudden cardiac arrest. Although the arrhythmic substrate is traditionally localized to the ventricles, atrial fibrillation (AF) is frequently observed, suggesting a shared molecular substrate between atrial and ventricular arrhythmias. C-type natriuretic peptide (CNP) and related microRNAs (miRNAs) modulate atrial and ventricular physiology, but their roles in exosomes in BrS have not been investigated. Objectives: To investigate alterations in CNP mRNA expression and changes in the expression of selected CNP-associated miRNAs implicated in AF, both analyzed in exosomes isolated from individuals with BrS and from healthy controls. Methods: Exosomes were isolated from the plasma of BrS patients without a history of overt AF and from healthy controls. In silico analyses identified CNP-targeting miRNAs implicated in AF. Exosomal CNP and CNP-related miRNAs were analyzed using Droplet Digital PCR. Results: BrS patients exhibited a significant increase in exosomal CNP mRNA expression levels compared with controls. MiR-138-5p was selectively downregulated, whereas other AF-related CNP-targeting miRNAs (miR-4443, miR-206, miR-142-5p, miR-223-5p) showed comparable levels between groups. A positive correlation between exosomal CNP and miR-223-5p and miR-4443 suggests shared regulatory pathways. Conclusions: these findings indicate that exosomal profiling may provide a more sensitive approach than conventional circulating measurements to detect molecular remodeling in BrS. The observed alterations highlight a potential shared molecular substrate between atrial and ventricular arrhythmias and may inform future studies aimed at refining diagnostics and developing targeted therapeutic strategies. Full article

Multifunctional scaffolds that combine structural support with the controlled delivery of bioactive agents remain a major challenge in tissue engineering. To extend the use of these devices in biomedicine, 3D printing is presented as an alternative that enables the manufacture of complex devices tailored to each patient, thereby solving specific problems in a timely and efficient manner. In this study, porous 3D scaffolds were fabricated via digital light processing (DLP) using a PET-RAFT resin composed of 2-(dimethylamino)ethyl methacrylate (DMAEMA) and poly(ethylene glycol) diacrylate (PEGDA₅₇₅). Sodium chloride (NaCl) was incorporated as a porogen, while insulin-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles were embedded as osteoinductive agents. The printed constructs exhibited high-resolution, reproducible trabecular-like architectures, as confirmed by micro-computed tomography (micro-CT), with interconnected pores averaging 70.7 ± 24.7 μm and a total porosity of 57.0 ± 6.98%. Thermal and chemical analyses confirmed scaffold stability and controlled degradability. Cytocompatibility assays using MC3T3-E1, C2C12, hGMSCs, and C166-GFP cells showed viability above 80% after 7 days (ISO 10993-5). Insulin-loaded nanoparticles enabled sustained release, characterized by an initial burst followed by gradual release up to 72 h. Dynamic bioreactor culture enhanced cell adhesion and RUNX2 expression, confirming the osteoinductive potential of the hybrid scaffold for advanced BTE applications. This study introduces an innovative PET-RAFT-derived resin that combines structural reinforcement with spatiotemporal regulation of insulin release, offering a potential strategy for enhanced biomaterial tissue engineering and tailored therapeutic interventions. Full article

Robot-assisted telerehabilitation (RAT) combines rehabilitation robotics with digital health workflows to extend access to upper-limb (UL) therapy after stroke. Mixed reality (MR) may support therapist–patient interaction and task visualization; however, early-stage systems require rigorous evaluation of safety and usability before deployment in the home. In a formative, mixed-methods usability study conducted in a controlled setting using a telerehabilitation workflow, six individuals post-stroke (≥3 months) and six occupational therapists (OTs) completed a single supervised session with a desktop-mounted end-effector type therapeutic robot (iTbot) integrated with Microsoft HoloLens 2. Participants performed structured passive and active UL exercises while therapists supervised and interacted with the system via the MR control interfaces. Safety was evaluated by documenting observed adverse events and safety-stop activations. Usability and user experience were assessed using the System Usability Scale (SUS), study-specific satisfaction questionnaires (reported with scale ranges), and semi-structured follow-up interviews analyzed using thematic analysis. All participants completed the session without observed adverse events or safety-stop activations. Overall usability was favorable, with a mean (SD) SUS total score of 78.3 (15.9) out of 100 (stroke: 74.2 [18.1]; occupational therapists: 82.5 [13.5]). Qualitative feedback indicated that MR was perceived as engaging and intuitive by many users, while also identifying implementation needs relevant to real-world telerehabilitation, including clearer onboarding, simplification of certain MR interactions, and improved physical interfaces (e.g., handle options). Therapists highlighted workflow considerations for remote supervision and patient independence. Together, these findings support progression to multi-session, in-home studies to quantify remote assistance needs, technical reliability, adherence, and clinical outcomes. Full article

Background/Objectives: Obesity is a chronic, relapsing disease with a widening gap between clinical need and the availability of specialist care. Artificial intelligence (AI) may enable earlier risk detection, more precise phenotyping, and scalable behavioural support across obesity treatment pathways. This narrative review synthesises contemporary AI applications across the obesity care continuum and evaluates their translational readiness. Methods: A targeted search of PubMed/MEDLINE and Google Scholar (January 2024–January 2026) was conducted, complemented by citation chaining. Evidence was synthesised across four domains: (1) risk prediction and screening, (2) environmental and behavioural determinants, (3) multimodal phenotyping and precision stratification, and (4) AI-enabled lifestyle interventions and behavioural coaching (AIBC). Results: Electronic health record (EHR)-based models demonstrate clinically useful discrimination for early risk identification. Multimodal approaches refine stratification beyond body mass index (BMI)-centric classification. AI-enabled behavioural coaching (AIBC) platforms show emerging evidence of clinically meaningful weight loss, including non-inferiority to human coaching; however, long-term effectiveness, generalisability, and equity remain insufficiently established. Conclusions: AI is positioned to become a core enabler of personalised obesity pathways. Safe translation requires external validation, bias auditing, transparent reporting, human oversight, and post-deployment surveillance aligned with clinical guidelines and regulatory expectations. 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