Search Results (332)

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Background/Objectives: Direct oral anticoagulants (DOACs) are now the guideline-recommended alternative to vitamin K antagonists (VKAs) for long-term anticoagulation in patients with non-valvular atrial fibrillation. However, accurately assessing their impact on ischemic stroke outcomes remains challenging, primarily due to uncertainty regarding anticoagulation status at the time of hospital admission. This preliminary study addresses this gap by using point-of-care testing (POCT) to confirm DOAC activity at bedside, allowing for a more accurate comparison of 90-day functional outcomes between anticoagulated and non-anticoagulated stroke patients. Methods: We conducted a retrospective cohort study of 786 ischemic stroke patients admitted to the University of Pécs between February 2023 and February 2025. Active DOAC therapy was confirmed using the ClotPro^® viscoelastic testing platform, with ecarin Clotting Time (ECT) employed for thrombin inhibitors and Russell’s Viper Venom (RVV) assays for factor Xa inhibitors. Patients were categorized as non-anticoagulated (n = 767) or DOAC-treated with confirmed activity (n = 19). Mahalanobis distance-based matching was applied to account for confounding variables including age, sex, pre-stroke modified Rankin Scale (mRS), and National Institutes of Health Stroke Scale (NIHSS) scores at admission and 72 h post-stroke. The primary outcome was the change in mRS from baseline to 90 days. Statistical analysis included ordinary least squares (OLS) regression and principal component analysis (PCA). Results: After matching, 90-day functional outcomes were comparable between groups (mean mRS-shift: 2.00 in DOAC-treated vs. 1.78 in non-anticoagulated; p = 0.745). OLS regression showed no significant association between DOAC status and recovery (p = 0.599). In contrast, NIHSS score at 72 h (p = 0.004) and age (p = 0.015) were significant predictors of outcome. PCA supported these findings, identifying stroke severity as the primary driver of outcome. Conclusions: This preliminary analysis suggests that ischemic stroke patients with confirmed active DOAC therapy at admission may achieve 90-day functional outcomes comparable to those of non-anticoagulated patients. The integration of bedside POCT enhances the reliability of anticoagulation assessment and underscores its clinical value for real-time management in acute stroke care. Larger prospective studies are needed to validate these findings and to further refine treatment strategies. Full article

Hemorrhoidal disease remains a prevalent yet often overlooked condition, affecting millions worldwide and imposing a substantial healthcare burden. Despite the availability of multiple treatment options, gaps persist in patient education, early symptom recognition, and optimal treatment selection. Recent advancements are evolving the pharmacist’s role in hemorrhoid management beyond traditional over-the-counter (OTC) and prescription approaches. The 2024 American Society of Colon and Rectal Surgeons (ASCRS) guidelines introduce updates on the use of phlebotonics, a class of venoactive drugs gaining recognition for their role in symptom management, yet largely underutilized in U.S. clinical practice. In parallel, novel clinical tools are reshaping how pharmacists engage in assessment and care. The integration of digital decision-support platforms and structured evaluation algorithms now empowers them to systematically evaluate symptoms, identify red flag signs, and optimize patient triage. These tools reduce diagnostic variability and improve decision-making accuracy. Given their accessibility and trusted role in frontline healthcare, pharmacists are well-positioned to bridge these critical gaps by adopting emerging treatment recommendations, leveraging algorithm-driven assessments, and reinforcing best practices in patient education and referral. This narrative review aims to equip pharmacists with updated insights into evidence-based hemorrhoid management strategies and provide them with structured assessment algorithms to standardize symptom evaluation and treatment pathways. By integrating these innovations, pharmacists can enhance treatment outcomes, promote patient safety, and contribute to improved quality of life (QoL) for individuals suffering from hemorrhoidal disease. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Sepsis remains a leading global cause of mortality, with delayed recognition and empirical antibiotic overuse fueling poor outcomes and rising antimicrobial resistance. This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applications in sepsis care, focusing on early detection, personalized antibiotic management, and resistance forecasting. Literature from 2019 to 2025 was systematically reviewed following PRISMA-ScR guidelines. A total of 129 full-text articles were analyzed, with study quality assessed via the JBI and QUADAS-2 tools. AI-based models demonstrated robust predictive performance for early sepsis detection (AUROC 0.68–0.99), antibiotic stewardship, and resistance prediction. Notable tools, such as InSight and KI.SEP, leveraged multimodal clinical and biomarker data to provide actionable, real-time support and facilitate timely interventions. AI-driven platforms showed potential to reduce inappropriate antibiotic use and nephrotoxicity while optimizing outcomes. However, most models are limited by single-center data, variable interpretability, and insufficient real-world validation. Key challenges remain regarding data integration, algorithmic bias, and ethical implementation. Future research should prioritize multicenter validation, seamless integration with clinical workflows, and robust ethical frameworks to ensure safe, equitable, and effective adoption. AI and ML hold significant promise to transform sepsis management, but their clinical impact depends on transparent, validated, and user-centered deployment. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies. Full article

Background/Objectives: Food allergy (FA) is a growing concern in pediatric care, requiring effective avoidance strategies and timely emergency responses. The role of caregivers is central to the daily management of FA. This study aimed to assess parental knowledge, preparedness, and behaviors regarding pediatric FA management, focusing on both prevention and emergency readiness. Methods: A cross-sectional survey was conducted from December 2024 to April 2025 through the SurveyMonkey^® platform, promoted by the Italian Society of Pediatric Allergology and Immunology (SIAIP). The anonymous, structured questionnaire was distributed online and in two Italian university hospitals. A total of 129 fully completed responses from caregivers of children with FA were analyzed. The survey explored self-perceived knowledge, symptom recognition, preventive actions, emergency preparedness, and communication practices. Results: Only 9.3% of parents considered themselves “very informed,” while 54.3% reported limited or no knowledge. Just 16.0% recognized all symptoms of an allergic reaction, and only 24.0% could distinguish mild reactions from anaphylaxis. Notably, 67.4% reported not knowing how to respond to anaphylaxis, and 83.7% did not possess an epinephrine auto-injector. Preventive measures at home were inconsistently applied, and 41.1% took no precautions when eating out. Communication with external caregivers was often informal or absent. Only 33% updated physicians regularly. Conclusions: The findings reveal significant gaps in parental preparedness and highlight critical areas for educational intervention. Enhanced caregiver training, standardized communication protocols, and improved clinical follow-up are essential to strengthen pediatric FA management and safety. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

Cancer remains a leading global health burden, profoundly affecting patient survival and quality of life. Current treatments—including chemotherapy, radiotherapy, immunotherapy, and surgery—are often limited by toxicity or insufficient specificity. Conventional chemotherapy, for instance, indiscriminately attacks rapidly dividing cells, causing severe side effects. In contrast, peptide-based therapeutics offer a paradigm shift, combining high tumour-targeting precision with minimal off-target effects. Their low immunogenicity, multi-pathway modulation capabilities, and adaptability for diagnostics and therapy make them ideal candidates for advancing oncology care. Innovative peptide platforms now enable three transformative applications: (1) precision molecular diagnostics (e.g., ¹⁸F-PSMA-1007 for prostate cancer detection), (2) targeted therapies (e.g., BT5528 and SAR408701 targeting tumour-specific antigens), and (3) theranostic systems (e.g., RAYZ-8009 and ¹⁷⁷Lu-FAP-2286 integrating imaging and radiotherapy). Despite their promise, peptides face challenges like metabolic instability and short half-lives. Recent advances in structural engineering (e.g., cyclization and D-amino acid incorporation) and delivery systems (e.g., nanoparticles and PEGylation) have significantly enhanced their clinical potential. This review highlights peptide-based agents in development, showcasing their ability to improve early cancer detection, reduce metastasis, and enhance therapeutic efficacy with fewer adverse effects. Examples like CLP002 underscore their role in personalised medicine. By overcoming current limitations, peptide drugs are poised to redefine cancer management, offering safer, more effective alternatives to conventional therapies. Their integration into clinical practice could mark a critical milestone in achieving precision oncology. Full article

Burnout syndrome is characterized mainly by three criteria (emotional exhaustion, depersonalization, and low personal accomplishment), and further exacerbated by night shift work, with profound implications for individual and societal well-being. The Maslach Burnout Inventory survey applied to 97 medical care professionals (with day and night work) revealed different scores for these criteria. Blood metabolic profiles were obtained by UHPLC-QTOF-ESI⁺-MS untargeted metabolomics and multivariate statistics using the Metaboanalyst 6.0 platform. The Partial Least Squares Discrimination scores and VIP values, Random Forest graphs, and Heatmaps, based on 99 identified metabolites, were complemented with Biomarker Analysis (AUC ranking) and Pathway Analysis of metabolic networks. The data obtained reflected the biochemical implications of night shift work and correlated with each criterion’s burnout scores. Four main metabolic pathways with important consequences in burnout were affected, namely lipid metabolism, especially steroid hormone synthesis and cortisol, the energetic mitochondrial metabolism involving acylated carnitines, fatty acids, and phospholipids as well polar metabolites’ metabolism, e.g., catecholamines (noradrenaline, acetyl serotonin), and some amino acids (tryptophan, tyrosine, aspartate, arginine, valine, lysine). These metabolic profiles suggest potential strategies for managing burnout levels in healthcare professionals, based on validated criteria, including night shift work management. Full article

Bacterial vaginosis (BV) affects 30% of women annually, but many face barriers to in-person care. Here we present real-world outcomes of remote BV diagnosis and management through self-collected vaginal microbiome (VMB) testing and telemedicine visits, focusing on symptom resolution, recurrence, and overall microbial shifts. Among the 1159 study participants, 75.5% experienced symptom resolution at four weeks when managed with our algorithm-guided treatment protocol. At a median follow-up of 4.4 months after the initial visit, 30.0% of patients experienced recurrent BV, which is lower than the typical recurrence rates seen in historical in-person cohorts. Across the entire cohort, metagenomic data demonstrated a significant increase in Lactobacillus abundance (mean of 32.9% to 48.4%, p < 0.0001) and a corresponding decrease in BV-associated taxa such as Gardnerella, Prevotella, and Fannyhessea. A PERMANOVA of pairwise Bray–Curtis distances showed significant separation between pre-and post-treatment samples (pseudo-F = 37.6, p < 0.0001), driven by an increase in Lactobacillus-dominated samples. Treatment adherence was high (a total of 78% reported perfect or near-perfect adherence), and adverse events were generally mild (in total, 22% reported vaginal irritation, and 13% reported abnormal discharge). These results demonstrate that Evvy’s at-home metagenomic platform, paired with telemedicine and a smart treatment algorithm, delivers robust clinical and microbial outcomes. This work offers a novel approach to managing bacterial vaginosis, a challenging condition characterized by persistently high recurrence rates. Full article

This review explores optical technologies for non-invasive glucose monitoring (NIGM) using aqueous humor (AH) as media, addressing the limitations of traditional invasive methods in diabetes management. It analyzes key techniques such as Raman spectroscopy, polarimetry, and mid- and near-infrared spectral methods, highlighting their respective challenges, alongside emerging hybrid approaches like photoacoustic spectroscopy and optical coherence tomography. Crucially, the practical realization of these optical methods for portable NIGM hinges on advanced instrumentation. Therefore, this review also details progress in compact NIR spectrometers. While conventional systems often lack suitability, significant advancements in on-chip technologies—including miniaturized dispersive spectrometers and various on-chip Fourier transform systems (e.g., spatial heterodyne, stationary wave integral, and temporally modulated FT systems)—utilizing integration platforms like SOI and SiN are promising. Such innovations offer the potential for high spectral resolution, large bandwidth, and miniaturization, which are essential for developing practical AH-based NIGM systems to improve diabetes care. Full article

Chronic wounds present clinical challenges due to persistent inflammation, infection, and dysregulated tissue repair, often exacerbated by the passive nature of conventional wound dressings. Recent advancements in hydrogel-based wearable technologies have transformed these biomaterials into multifunctional platforms capable of integrating real-time monitoring and targeted therapy, ushering in a new era of intelligent wound care. In this review, we show innovative diagnostic and therapeutic strategies, including wound-monitoring devices and multifunctional healing-promoted platforms, highlighting integrated closed-loop systems that dynamically adapt treatments to wound microenvironments, thus merging diagnostics and therapeutics. Challenges in fabrication engineering and clinical application are discussed, alongside emerging trends like AI-driven analytics and 3D-bioprinted technology. By bridging fragmented research, this work underscores the potential of hydrogels to enable intelligent wound management. Full article

Background: The purpose of this study was to clinically characterize neutrally satisfied patients and compare outcomes between satisfied, dissatisfied, and neutral patients. Methods: This was a secondary analysis from data collected in a multicenter longitudinal cohort study comprising total knee arthroplasty (TKA) patients using a digital care management platform. The Knee Society Score (KSS) satisfaction survey was administered at post-operative 90 days, and dissatisfaction was defined as a composite score of less than 20, satisfied as a score equal to or greater than 30, and neutral as a score of 20 up to 29. Patient-reported outcome measures (PROMs) were assessed pre-operatively and at post-operative one, three, six, and twelve months. Results: Approximately 58% of patients were satisfied (n = 1486), 29.4% neutral (n = 747), and 12.2% dissatisfied (n = 311). Neutral and dissatisfied patients were younger and more likely to be female and had lower pre-operative KSS scores compared to satisfied patients, though statistical differences were found between all groups. Pre-operative pain was significantly less in satisfied compared to neutral or dissatisfied patients. Changes in the pre-operative Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) displayed significant differences between all groups at all time points, with greater improvements in satisfied versus neutral patients and neutral versus dissatisfied patients. Similarly, satisfied patients experienced significantly greater improvements in pain and KSS scores at post-operative three months, and neutral patients improved more than dissatisfied patients. Conclusions: Neutral patients present with distinctively different clinical outcomes compared to satisfied or dissatisfied patients and should be classified separately as neutral. Full article