Search Results (9,580)

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

Cardiometabolic disease is increasingly shaped by the overlap among obesity, type 2 diabetes, chronic kidney disease, heart failure, and atherosclerotic cardiovascular disease, underscoring the need for prevention strategies that extend beyond glucose-centered care. This narrative review critically examines the mechanistic rationale, clinical evidence, guideline evolution, and practical implementation of sodium-glucose cotransporter-2 inhibitors (SGLT-2 inhibitors) and glucagon-like peptide-1 receptor agonists (GLP-1 receptor agonists) within the cardiorenal–metabolic continuum. A structured literature search was conducted in PubMed, Scopus, and Web of Science, focusing primarily on publications from January 2019 to March 2026, with selected landmark studies from earlier years included for context. Priority was given to randomized controlled trials, major cardiovascular and kidney outcome trials, meta-analyses, clinical practice guidelines, scientific statements, and expert consensus documents. The reviewed evidence indicates that SGLT-2 inhibitors show the most consistent benefits in reducing heart failure events, slowing chronic kidney disease progression, and lowering cardiorenal risk, whereas GLP-1 receptor agonists are more strongly associated with reductions in major adverse cardiovascular events, residual atherosclerotic risk, and body weight. Emerging data also support extension of this therapeutic paradigm beyond diabetes, particularly in obesity-associated cardiovascular risk. Contemporary care is increasingly moving toward phenotype-informed treatment selection, earlier organ-protective intervention, and multidisciplinary management, although cost, access, tolerability, and implementation barriers remain important limitations. SGLT-2 inhibitors and GLP-1 receptor agonists are therefore central to modern cardiovascular prevention across the cardiovascular–kidney–metabolic spectrum. In this context, the proposed Cardiometabolic 2.0 framework may serve as a clinically oriented model for integrating these therapies within contemporary organ-protective care. Full article

England’s population is living longer, a sign of progress and better health, but adult social care (ASC) services must adapt to support a growing number of older residents, who may need help to remain independent, safe, and well. Kent County Council (KCC), in South East England, projects a 28% and 53% increase in its residents aged 65+ and 85+, respectively, over the next decade. This study aimed to inform the development of KCC’s ASC Prevention Framework using a System Dynamics Modelling (SDM) approach to evaluate the impact of preventive interventions on ASC demand and expenditure. Using linked local health and social care data and the Johns Hopkins ACG® tool, the 1.3 million adult population was stratified into Patient Needs Groups. Analyses showed that higher ASC costs were associated with being older females, living alone, deprivation, and frailty-related indicators such as dementia, history of falls, etc. Around 28% of older adults aged 65+ accounted for 80% of ASC costs within that cohort, and related scenario testing projected a 48% rise in ASC costs over 10 years without interventions, moderated to 33% with targeted prevention. These findings demonstrate the value of integrated data and modelling to inform strategic, prevention-focused ASC planning. Full article

Background: Osteopathy’s integration into contemporary healthcare requires clear articulation of its theoretical and practical foundations and active engagement in interprofessional practice. Despite growing institutional recognition, conceptual ambiguity remains regarding foundational principles and their operationalization. Osteopathy is broadly described as a person-centered, evidence-informed discipline promoting health through manual and educational strategies within systemic and biopsychosocial contexts. Objectives: This Perspective critically examines osteopathic principles, proposes a shared conceptual model for interdisciplinary care, and outlines a structured research agenda for empirical validation, aiming to enhance person-centered, preventive, and sustainable practice. Methods: A narrative review synthesized historical, theoretical, and contemporary evidence. Records were thematically analyzed through expert collaborative brainstorming to achieve consensus, ensuring both conceptual and empirical rigor. Results: Twenty-two studies were included, forming two thematic areas: (1) historical evolution of osteopathic principles, encompassing foundational definitions, early interpretive divergences, codifications, and adaptations; and (2) contemporary reconceptualization for interdisciplinary care, integrating systems-oriented and biopsychosocial frameworks. Emphasis was placed on self-regulation, structure–function relationships, and holistic care. This synthesis bridges historical and modern insights, highlighting osteopathy’s relevance in integrative, pediatric, and preventive healthcare. Conclusions: Reconceptualizing osteopathic principles strengthens professional identity and supports sustainable, evidence-informed, person-centered practice. The proposed framework informs interprofessional collaboration and guides a research roadmap to validate and integrate osteopathy globally within contemporary healthcare systems. Full article

The efficacy of healthcare performance evaluation systems depends on their design and implementation, as well as on their perceived value and integration into daily practice. This study explores the acceptability of a healthcare performance evaluation system, used by health and administrative professionals in four rural healthcare settings in Ethiopia, Tanzania, and Uganda, three years after its implementation. In-depth semi-structured interviews were conducted, either in person or via video conference, with 17 professionals involved in system design and implementation. The analysis of qualitative data drew on Sekhon’s Theoretical Framework of Acceptability, using content analysis to identify themes across seven dimensions of acceptability. Key findings show that participants’ perceptions of acceptability of the performance evaluation system are influenced by data disclosure and reputational effect, the system’s understandability, alignment with their mission to improve quality of care, perceived usefulness, experienced opportunity costs, and intervention burden. The key features of the performance evaluation system are the most critical factors contributing to its acceptability, but the administrative burden, which includes professionals’ need to invest more time and change work habits to use the new system, poses some challenges and may hinder the medium- to long-term effectiveness of the intervention. Full article

Thoracic malignancies are uniquely positioned for the integration of emerging technologies such as artificial intelligence (AI), which have the potential to advance precision oncology across the cancer care continuum. In cancer screening, AI has emerged as a promising strategy to enhance diagnostic accuracy, efficiency, and scalability. Deep learning applied to pathology (pathomics) and imaging (radiomics) has enabled the development of novel, noninvasive tools capable of predicting histologic and molecular features that may correlate with treatment response or toxicity. In drug discovery, computational approaches can analyze large-scale genomic, chemical, and clinical datasets to accelerate target identification and match candidate compounds to available targets; this may be particularly useful in the context of resistance to targeted therapy. AI tools may also support treatment planning for radiation and surgery, guide systemic therapy selection, and facilitate continuous monitoring for early identification of treatment resistance or toxicity. As these technologies are integrated into clinical workflows, careful attention to ethical, regulatory, and clinical governance frameworks will be essential to ensure equitable implementation and bias mitigation. Maintaining human oversight and a human-centered approach remain critical, as complex treatment decisions and sensitive patient interactions are central to the care of patients with thoracic malignancies. Full article

Background/Objectives: Persistent gastrointestinal (GI) symptoms following oncologic treatment represent a major unmet need in survivorship care, often managed symptomatically without addressing underlying biological mechanisms. This study aimed to evaluate the clinical efficacy and biological correlates of an artificial intelligence (AI)-guided, personalized microbiome modulation strategy in oncology patients with chronic secondary GI dysfunction. Methods: We conducted a prospective, single-arm, open-label validation study including 29 adult female oncology patients with persistent GI symptoms lasting ≥3 months. Participants underwent baseline multidimensional assessment integrating shotgun metagenomic sequencing, inflammatory and nutritional biomarkers, and clinical symptom profiling. An AI-guided platform generated individualized dietary, supplement, and lifestyle recommendations. Outcomes were assessed at baseline and after a 3-month intervention, focusing on intra-individual changes in stool frequency (primary endpoint), self-reported energy, microbiome composition, and metabolic biomarkers. Paired statistical analyses, correlation testing, and multivariable regression were performed. Results: After three months, stool frequency significantly decreased (4.69 ± 2.41 to 2.07 ± 1.19 episodes/day; p < 0.0001), accompanied by a marked increase in energy levels (4.00 ± 1.04 to 7.24 ± 1.12; p < 0.0001). Microbiome analysis revealed consistent enrichment of butyrate-producing and barrier-supportive taxa, including Faecalibacterium prausnitzii, Eubacterium rectale, Roseburia intestinalis, Akkermansia muciniphila, and Bifidobacterium longum. Butyrate-related biomarkers and vitamin-associated parameters (B-complex, vitamin D) showed significant improvement, while lactate levels normalized. Changes in Bifidobacterium longum were independently associated with stool frequency reduction (β = −0.783, p = 0.0082). Conclusions: AI-guided personalized microbiome modulation was associated with significant clinical improvement and biologically coherent microbial and metabolic shifts in oncology patients with persistent GI symptoms. These findings support a precision supportive-care approach targeting microbiome restoration, warranting further validation in randomized controlled trials. Full article