Search Results (9,586)

Psychosis presents significant treatment challenges, and standard Cognitive Behavioral Therapy for psychosis often faces limitations due to patient engagement issues and reliance on subjective self-reporting. The integration of Virtual Reality (VR), physiological biosensors, and artificial intelligence offers a transformative opportunity to address these challenges. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. A thorough literature search was performed across seven databases. Twelve randomized controlled trials involving 1504 participants were included to assess VR-assisted CBT, VR treatment, and AVATAR therapy. Meta-analyses showed that VR interventions significantly decreased auditory verbal hallucinations (pooled SMD = −0.24, p = 0.0011) and paranoid thoughts (SMD = −0.26, p < 0.0001) compared to control conditions. This review supports integrating multi-modal biosensors to collect real-time, objective physiological data. Such integration enables the development of AI-driven, closed-loop systems that dynamically adjust the virtual environment based on the patient’s physiological state. VR-assisted therapies effectively reduce positive symptoms of psychosis. Incorporating biosensors is a crucial step toward a data-driven approach for personalized, closed-loop psychiatric care. Future efforts should focus on large-scale clinical trials, biomarker validation, and robust ethical frameworks to ensure safe and effective implementation. Full article

Fetal health assessment is a crucial aspect of prenatal care, aimed at the early detection of potential complications to ensure optimal outcomes for both mother and child. Traditional methods, such as the visual analysis of cardiotocography (CTG) data by healthcare professionals, are valuable but often subjective and time-consuming. This work investigates the application of machine learning techniques, with a focus on ensemble learning, to enhance the accuracy and efficiency of fetal health classification based on CTG data. Genetic Algorithm (GA) is employed for optimal feature selection, identifying the most discriminative subset of CTG attributes to improve model performance and reduce computational complexity. We employ a combination of advanced machine learning models, including AdaBoost, Gaussian Naïve Bayes, Decision Tree, k-nearest neighbors (KNN), and Logistic Regression. The top two models were selected based on comprehensive performance metrics using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. These models were then integrated through ensemble learning approaches, such as stacking, Particle Swarm Optimization (PSO) weighted averaging, and soft voting, to improve prediction reliability. Our proposed stacking ensemble model achieves a remarkable accuracy of 97.9%, demonstrating its potential as a robust, data-driven tool for fetal health monitoring and the early identification of at-risk pregnancies. The results indicate that machine learning can effectively complement traditional fetal health assessment methods by providing an objective framework to support clinical decision-making. Full article

Background. Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing interest in its applications within pediatric dentistry. Given the unique clinical, developmental, and behavioral characteristics of pediatric patients, AI-based systems may offer valuable support in improving diagnosis, prevention, and treatment planning. Methods. A narrative review was conducted to synthesize current evidence on AI applications in pediatric dentistry. A comprehensive search strategy, including predefined keywords and free terms, was applied across multiple databases (Embase, Scopus, PubMed, and Web of Science) up to 1 January 2026. Reviews addressing AI-based technologies in pediatric dental care were selected and analyzed. Results. The available literature indicates that AI is being progressively applied across multiple domains of pediatric dentistry, although with varying levels of evidence. More extensively investigated areas include diagnostic imaging, caries detection, orthodontic assessment, and growth evaluation, where AI systems—particularly those based on machine learning and deep learning—have demonstrated high accuracy and reproducibility. Other emerging fields, such as remote monitoring, behavioral management, preventive strategies, and patient education, show promising potential but remain less explored. Overall, AI-based tools appear to enhance diagnostic support, enable early detection of oral conditions, and contribute to more personalized and efficient clinical workflows. Conclusions. AI represents a rapidly evolving adjunct in pediatric dentistry with the potential to improve clinical decision-making, preventive care, and patient management. Despite encouraging results, further validation in real-world settings, along with careful consideration of ethical, legal, and data-related challenges, is required to support its responsible integration into routine clinical practice. Full article

Riemerella anatipestifer (RA) is the primary causative agent of infectious serositis in ducks, causing significant economic losses. In this study, a rapid and visual loop-mediated isothermal amplification (LAMP) assay targeting the conserved region of the ompA gene was developed. Specific primers and a FAM-labeled probe were designed, and amplification products were visualized using phenol red-based colorimetric detection and a lateral flow dipstick (LFD) system. Among the five candidate primer sets, primer set 2 was selected because it showed the highest amplification efficiency and specificity, with no cross-reactivity detected against 12 common waterfowl pathogens. Under optimal conditions, the phenol red-based LAMP assay yielded visible results after incubation at 65 °C for 30 min, while the LAMP-LFD assay required an additional 3~5 min probe hybridization step, with detection limits of 7.76 × 10² copies/μL for the phenol red-based method and 7.76 × 10⁰ copies/μL for the LAMP-LFD method. Thirty clinical samples suspected of RA infection were analyzed using conventional PCR and the developed visual LAMP assays. The positive detection rates obtained with the LAMP-LFD and phenol red-based LAMP methods were 63.3% and 60%, respectively, showing high concordance with conventional PCR (56.7%). In conclusion, the LAMP assay integrating phenol red visualization and lateral flow dipstick detection is rapid, sensitive, and easy to perform, and both detection formats show potential for point-of-care or on-site applications, and can be used for the early diagnosis and detection of RA. Full article

Background/Objectives: The digital transformation of healthcare has become a key component of building resilient and sustainable health systems. However, the long-term sustainability of digital health technologies depends not only on user acceptance but also on the institutional governance conditions that shape how these technologies are implemented and utilised in practice. This study examines how institutional factors shape the sustainable utilisation patterns of Internet of Things (IoT) technologies in regulated healthcare environments, with hospital IoT-based asset management systems, a mature and widely deployed use case in China’s public hospitals, providing the empirical context for the investigation. Methods: Drawing on institutional theory and the Technology Acceptance Model (TAM), we conceptualise user perceptions as behavioural micro-foundations through which institutional conditions influence technology utilisation. A survey of 293 healthcare professionals from two large public hospitals in China was analysed using Structural Equation Modelling (SEM), incorporating mediation and Multi-Group Analysis (MGA). Results: The results demonstrate that technical compatibility (TC) significantly enhances perceived ease of use (PEU) (β = 0.40), while organisational support (OS) positively influences both perceived usefulness (PU) (β = 0.35) and PEU (β = 0.30). Conversely, regulatory compliance (RC) negatively affects PU (β = −0.25) and PEU (β = −0.20), revealing a tension between accountability requirements and operational efficiency. The model explains between 58% and 67% of the variance in key constructs. Conclusions: Overall, the findings indicate that sustainable utilisation patterns depend on alignment between technological capabilities and institutional governance conditions, with user perceptions operating as behavioural micro-foundations through which institutional effects are transmitted. By integrating institutional theory with technology acceptance research, this study contributes a governance perspective for understanding sustainable digital transformation in healthcare systems and provides practical insights for designing interoperable, compliant, and supportive digital health infrastructures to enhance hospital operational efficiency and quality of care. Full article

Despite the demand for personalized wound care, integrating diagnostics and therapeutics into a unified platform remains a significant challenge. To address this, we developed a 3D-printed theranostic hydrogel using vat photopolymerization, enabling precise, multifunctional wound management. The hydrogel matrix, composed of poly(acrylamide-co-hydroxyethyl acrylate) and carboxymethyl cellulose, was chemically crosslinked with poly(ethylene glycol) diacrylate. Bromocresol purple was integrated into the photosensitive resin to enhance printing fidelity and serve as a diagnostic indicator, providing a distinct colorimetric shift upon skin infection. For controlled drug delivery, graphene oxide (GO) and levofloxacin were incorporated into the system. The 3D-printed hydrogel demonstrated superior swelling capacity (>600%), ideal for absorbing wound exudate. A semi-quantitative linear colorimetric response was observed across varying pH levels, allowing for clear differentiation between healthy healing skin (pH 4.0–6.0) and infected conditions (pH 7.0 and above). Furthermore, the hydrogel exhibited infection-stimulated therapy, with a cumulative levofloxacin release of 92.63% at pH 8, significantly higher than in acidic conditions. Moreover, the incorporation of GO further optimized the delivery profile by tuning absorption and release rates. Synergizing real-time monitoring and on-demand therapeutic action, this 3D-printed system offers a scalable, robust solution for future-ready, personalized wound management. Full article

Objective: This study investigates the optimization of surface EMG (sEMG) classification for seven transtibial movements using short analysis windows (64 ms) suitable for real-time control of below-knee prostheses. Methods: We systematically evaluated feature engineering strategies, dimensionality reduction techniques, and classification approaches using linear Support Vector Machines on four-channel sEMG data from the transtibial region. We compared amplitude-based versus derivative-based time-domain features, integrated frequency-domain features, and implemented multi-window majority voting with 50% overlap. Results: Evaluated across nine subjects (four male, five female), the optimized system achieves a population-level accuracy of $70.16\%\pm 7.09\%$ with multi-window majority voting (per-subject range: 60.71–78.57%), with voting consistently improving accuracy over single-window classification by $+7.06$% on average. We demonstrate that PCA provides zero benefit for linear classifiers when all features are retained. Documented failed approaches include adaptive windowing and spectral entropy features. Conclusion: Careful feature engineering combining time-domain (MAV2, RMS, VAR, MAX, LOG, IEMG) and frequency-domain features (MPF, MF, band powers) with multi-window voting substantially recovers accuracy losses from aggressive window reduction while maintaining sub-100 ms latency suitable for prosthetic control. This work provides a validated methodology across multiple subjects for optimizing EMG classification latency–accuracy trade-offs, demonstrates that PCA is unnecessary for linear classifiers with well-engineered features, and documents negative results to guide future prosthetic control research. Full article

The skin serves as a primary defensive barrier to protect the body from environmental contaminants, infections and trauma. Unfortunately, skin barrier’s structural and functional integrity can be compromised, disrupted or impaired due to a combination of internal and external factors, making it vulnerable and often leading to a wide range of skin conditions characterized by dryness, heightened sensitivity, and increased susceptibility to damage and infections. In addition, the integrity of the skin barrier tends to deteriorate progressively with age. As people age, their skin naturally changes and can also be compromised by a plethora of factors that reduce its strength and resilience. The aging skin becomes thinner and more sensitive, coinciding with a variety of structural–functional alterations, decreased levels of natural moisturizing factor (NMF), lipid content and hydration, increased transepidermal water loss (TEWL), altered skin surface pH (pHss) and microbiome diversity. All these age-related skin integrity alterations make the skin drier, flakier, itchy, and fragile, and more susceptible to damage and breakdown, thus diminishing its ability to effectively protect, repair and heal efficiently. Identifying skin integrity issues before they progress will foster positive outcomes through effective preventive measures. Hence, it is important to understand the impact of skincare formulations on skin integrity in compromised aging skin. A well-considered, evidence-based approach to skincare can provide cleansing, moisturizing and protective benefits, while aiding the reduction in skin integrity issues like dry and itchy skin, sensitive skin, bruising, skin tears, pressure injuries (PIs), lower leg ulcers and moisture-associated skin damage (MASD). Managing skin integrity in compromised aging skin begins with gentle skin cleansing, adequate moisturization and protective barrier care to ensure the skin’s function is maximized. Full article

Background: This study develops a transparent, decision-focused framework that integrates the World Health Organization’s defined daily dose (DDD) standard with the planned-backorder economic order quantity (EOQ) model to manage nursing and pharmacy workflows within a unified economic and operational scale. Method: Demand was expressed in DDD per year, and process-based costs were monetized according to National Health Service (NHS) workflow steps, where the holding cost was computed as H = r × cu and the delay cost B was derived from the target fill rate via a closed-form shadow-price relationship. The model was calibrated for a typical NHS acute-care hospital with 600 beds (D ≈ 130,305 DDD/year). Results: Calibration resulted in an ideal order quantity of 7554 DDD, an inter-order interval of 21 days, and a minimum annual total cost of £451. In the national conceptual scenario, the fill rate is about 99.4%, and the minimum annual total cost is £26,366. At this optimum, cost components are symmetrically balanced, with order cost and combined holding–delay cost contributing equally. Conclusions: This repeatable framework, based on the DDD scale, enhances management visibility regarding the cost–service balance, thereby confirming the policy’s robustness. Full article

Sepsis remains a leading cause of preventable morbidity and mortality worldwide, and adherence to the Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock Early Management Bundle (SEP-1) remains modest and variable across institutions. Simultaneously, controversy persists regarding fixed-volume fluid resuscitation mandates, particularly given the increasing emphasis on individualized, physiology-guided management. Artificial intelligence (AI) has emerged as a potential strategy to address both operational and clinical gaps in sepsis care. This review examines the current state of SEP-1 implementation, key barriers to compliance, and ongoing debates surrounding early fluid administration. We then discuss contemporary evidence on AI-enabled tools designed to accelerate bundle processes and support personalized fluid management. Early warning systems, natural language processing-augmented models, and telemedicine-integrated platforms have demonstrated improvements in process measures such as time-to-antibiotics and bundle component completion when embedded within defined clinical workflows. Reinforcement learning, causal machine learning, and predictive models offer promise for individualized fluid strategies, although most data remain retrospective and hypothesis-generating. Successful integration will require prospective validation, clinician-in-the-loop oversight, governance frameworks, and continuous monitoring for safety, equity, and model drift. AI should augment—rather than replace—clinical judgment to improve reliability, timeliness, and personalization in sepsis management. Full article

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by fibro-inflammatory lesions of the biliary tree. In the absence of available, effective medical therapies, many patients progress to liver failure, making PSC one of the leading indications for liver transplantation (LT), despite its rarity. While LT in PSC is associated with good overall short- and long-term survival, post-transplant outcomes are limited by recurrent PSC (rPSC), which affects up to one quarter of PSC recipients with a significant risk of graft loss and re-transplantation. The risk of rPSC reflects a complex interaction between donor and recipient factors including associated inflammatory bowel disease (IBD), and long-term exposure to immunosuppression. Therefore, post-transplant management requires an individualized multidisciplinary approach and tailored immunosuppressive regimens aimed at balancing the risk of rejection and rPSC with the risk of infection and malignancy. Optimal control of IBD has emerged as a key modifiable determinant of rPSC risk and post-transplant outcomes. In addition, patients with PSC, particularly PSC-IBD patients, carry a significantly increased risk of hepatobiliary and colorectal cancer. Importantly, this oncological risk persists after LT. Thus, long-term, structured cancer surveillance must remain an integral component of post-transplant care. Looking ahead, novel therapies targeting shared hepatic and intestinal fibro-inflammatory pathways are currently being investigated to modify disease activity in the pre-transplant setting. Future studies are needed to assess whether these agents might be applicable also in the post-transplant setting to improve long-term graft and patient survival. Full article

Diabetic kidney disease (DKD) remains a primary driver of end-stage kidney disease and cardiovascular morbidity despite the optimized use of renin–angiotensin system (RAS) inhibitors and sodium-glucose cotransporter-2 (SGLT2) inhibitors. Recent evidence identifies the overactivation of the mineralocorticoid receptor (MR) as a critical, independent pathway leading to persistent renal inflammation and fibrosis. In the diabetic milieu, MR overactivation—driven by both aldosterone and ligand-independent factors such as Rac1 GTPase and oxidative stress—triggers pro-inflammatory and pro-fibrotic gene networks. Unlike traditional steroidal mineralocorticoid receptor antagonists (MRAs), the novel non-steroidal MRA finerenone exhibits a distinct binding mode that more effectively blocks the recruitment of transcriptional co-activators, thereby silencing detrimental downstream signaling in podocytes, fibroblasts, and myeloid cells. Preclinical models have demonstrated that MR blockade significantly reduces albuminuria and preserves podocyte integrity independent of systemic blood pressure. These findings translated into landmark clinical trials; the FIDELIO-DKD and FIGARO-DKD trials established that finerenone significantly reduces the risk of kidney disease progression and cardiovascular events across a broad spectrum of chronic kidney disease stages in type 2 diabetes. Furthermore, recent data from the FINEARTS-HF and CONFIDENCE trials suggest a synergetic benefit when combined with SGLT2 inhibitors, offering more robust cardiorenal protection with a manageable risk of hyperkalemia. This review synthesizes the current understanding of MR pathophysiology and clinical evidence, providing a comprehensive framework for the integration of MRAs into the evolving standard of care for patients with diabetic kidney disease. Full article

Mulibrey nanism is a rare autosomal recessive multisystem disorder caused by biallelic loss of function variants in TRIM37 encoding a peroxisomal E3 ubiquitin ligase. Initially described in Finland, where it remains most prevalent due to a founder mutation, the condition is now recognized worldwide and is characterized by severe prenatal-onset growth failure, distinctive craniofacial features, radiological abnormalities, ocular findings, and hepatopathy. Although its clinical spectrum extends far beyond these core manifestations, the major determinant of morbidity and mortality is progressive cardiovascular disease, including constrictive pericarditis and restrictive cardiomyopathy. Additional features include metabolic dysfunction such as insulin resistance and type 2 diabetes, gonadal insufficiency, skeletal abnormalities including fibrous dysplasia, and an increased risk of benign and malignant tumours. The clinical course evolves across the lifespan from early growth and developmental abnormalities to progressive multisystem disease in adolescence and adulthood. Recent advances have expanded understanding of TRIM37 function, linking it to mTORC1 TFEB signalling autophagy, centrosome integrity, extracellular matrix regulation, and immune cell function, providing mechanistic insights into tumour predisposition, skeletal pathology, and immune dysregulation. Management remains supportive and requires multidisciplinary care with emphasis on early recognition and treatment of cardiac disease, metabolic complications, and malignancy risk. Prognosis is variable but improves with early diagnosis and appropriate surveillance. This review summarises the clinical spectrum molecular mechanisms and current management of Mulibrey nanism and highlights priorities for future research. Full article

Background/Objectives: A psychiatric consultation is a professional evaluation aimed at establishing a diagnosis, a prognosis, and developing a treatment plan. The objective was to assess psychiatric consultations (PCs) at the Community Mental Health Center (CMHC), Emergency Room (ER) and General Hospital (GH) to highlight differences across settings. Methods: With a retrospective design, we examined all PCs performed between 1 January 2024 and 31 December 2024 at the CMHC, ER and GH of Baggiovara in Modena. Descriptive statistical analysis and a multivariate logistic regression were performed. Results: We collected a total of 3174 PCs for 1801 patients, performed in the three settings: 52% in ER, 30% in CMHC and 18% in GH. In ER, PCs were most frequently requested for suicide risk (26%), psychomotor agitation (14%) and substance intoxication (14%). In CMHC, the most common diagnoses were depressive disorders (22%), acute anxiety (20%) and acute psychotic episodes (13%). In GH, consultations mainly addressed psychiatric symptoms associated with medical and eating disorders. The overall rate of psychiatric hospitalization after PCs was 16.2%, reaching 23.4% for consultations in ER. Discontinuation of pharmacological therapy was significantly associated with an increased risk of hospitalization (p < 0.001), which rose to 17% when therapy had been interrupted for more than one year. Conclusions: PCs at ER were the access point for most hospitalizations. Therapeutic discontinuation, acute psychosis and substance use represented the main predictors of hospitalization. Strengthening shared care pathways among CMHC, ER and GH represents an effective model of integration between hospital and community services, ensuring continuity of care. Full article

Background: Goals of care discussions are essential communication skills in medical training that bridge patient values with clinical decision-making. Integrating palliative care principles into these conversations enables holistic, patient-centered care, yet medical trainees often lack structured preparation for these critical interactions. Objective: This narrative review examines how medical training can effectively integrate palliative care approaches into goals of care discussions through structured communication frameworks, interdisciplinary collaboration, and emerging innovations to promote patient-centered outcomes. Methods: This narrative review is conducted using a structured literature search that includes relevant studies pertaining to goals of care (GOC) discussions, evidence-based communication frameworks, and communication training curricula. Databases used were PubMed and Google Scholar, using articles published between 2000 and 2025. The following keywords were used in our search: “SPIKES”, “REMAP”, “SUPER”, “serious illness conversation”, “goals of care,” “end of life,” “holistic care,” “palliative care,” and “medical education.” Exclusion criteria were used to select those relevant to inpatient care and training in inpatient settings. Studies in an outpatient setting were excluded. Findings were reviewed and synthesized to identify types of training approaches. An emphasis on clinical outcomes including patient satisfaction, hospice utilization, ICU transfers, and intervention intensity were examined. Educational barriers and facilitators—including communication training curricula, cultural competency, language considerations, and multidisciplinary team involvement—were evaluated. Emerging technologies supporting clinician education and practice were also assessed. Results: Training in structured communication frameworks improves patient–physician relationships, reduces patient anxiety, and increases family satisfaction. Early palliative care integration through effective discussions leads to increased hospice awareness and utilization while reducing burdensome interventions. Key educational facilitators include dedicated communication skills training, multidisciplinary team participation (including chaplains and palliative care specialists), and AI-assisted documentation tools that support learning while preserving humanistic clinician–patient interactions. Conclusions: Integrating palliative care principles into medical training for goals of care discussions is essential for developing patient-centered clinicians. Combining structured communication frameworks, interprofessional education, targeted skills training, and technological support creates a comprehensive educational approach that prepares trainees to elicit patient goals, create individualized care plans, and deliver holistic care that honors patient values. Full article