Search Results (39,032)

Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease imposing a substantial health and economic burden. Artificial intelligence (AI) has emerged as a promising tool to address CRS complexity in both clinical and research settings. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, and Embase for peer-reviewed articles utilizing machine learning, deep learning, and natural language processing, with a focus on molecular-level or immunologic mechanisms in CRS. Thirty-one studies were included in the qualitative synthesis. Our results demonstrate that while AI-driven analysis helps stratify patient endotypes, the identified schemes exhibit substantial heterogeneity in cluster numbers and features, raising concerns about their reproducibility. AI applications also included histopathological image interpretation, drug discovery, biologic therapy selection, and prognostic modeling to evaluate revision surgery risks and olfactory dysfunction. However, using the PROBAST+AI framework, we found that 87.1% of the assessed studies carry a high overall risk of bias, mainly due to the reliance on apparent performance or internal validation and limited independent evaluation of model performance, indicating that many reported performance estimates may be optimistic. Future large-scale, multicenter collaborations integrating AI into clinical workflows will be essential to establish reliable and generalizable AI applications for personalized CRS management. Full article

Acute cardiovascular care is among the most resource-intensive domains of contemporary healthcare. Major investments are concentrated in the index hospital episode, including emergency diagnostics, reperfusion or revascularization, intensive monitoring, specialist pharmacotherapy and multidisciplinary stabilization. Yet a substantial share of this value may be lost after discharge, when patients enter fragmented outpatient pathways characterized by delayed follow-up, medication discrepancies, unclear accountability and inconsistent implementation of secondary prevention. This paper uses the discharge cliff as an organizing metaphor for that phenomenon. We argue that the discharge cliff is not only a clinical problem but also a health-economic and sustainability problem. When responsibility becomes discontinuous after discharge, high-cost acute gains are exposed to avoidable erosion. The result is recurrent emergency use, repeat diagnostic cascades, duplicated medicines work, potentially preventable rehospitalization and additional material consumption. These consequences are relevant after acute myocardial infarction, decompensated heart failure, cardiogenic shock survivorship and stroke with cardiovascular complications, where the post-discharge period is both vulnerable and management intensive. Drawing on cardiovascular guidelines, outcomes research, policy documents and sustainability literature, we organize the synthesis around three recurring governance mechanisms: discontinuity of accountability, misaligned reimbursement incentives and weak post-acute feedback loops. We then propose a framework of post-acute cardiovascular stewardship. Core levers include structured discharge-to-follow-up pathways, pharmacist-supported medicines optimization, early specialist review, digital monitoring for high-risk patients, accountable transition metrics, and bundled or episode-based payment models. Clinical continuity and economic stewardship form the operational core of the framework; environmental resource use is included as an emerging, cross-cutting dimension whose pathway-level quantification remains less mature. The vulnerability of the post-discharge period is well established. The contribution here is not a new clinical phenomenon but a reframing of that established failure as a problem of value preservation and governance, and its operationalization into accountable ownership, episode-level measurement and reimbursement design. The sustainable future of high-intensity cardiovascular care depends not only on saving lives in hospital, but also on preserving the value generated there once the patient returns to community care. Full article

Household environments in informal settlements may serve as potential environmental reservoirs or contamination points and transmission interfaces for enteric pathogens, particularly where water, sanitation, and hygiene (WASH) services are inadequate. Conventional WASH monitoring often focuses on infrastructure and source-water quality, providing limited information regarding potential household-level exposure pathways. A pilot cross-sectional study was conducted in Zandspruit, an informal settlement in Johannesburg, South Africa, using a multi-matrix environmental sampling approach. A total of 143 samples, including drinking water (n = 29), dishcloths (n = 28), Bio-Wipes of hand (n = 29), surfaces (n = 28), and toilet seats (n = 29), were collected from participating households (n = 30). Physicochemical water-quality parameters, according to the South African National Standard (SANS) 241: 2015 by the South African Bureau of Standards (SABS), and microbial indicators were assessed. Deoxyribonucleic acid (DNA) was extracted using a silica-based method, and multiplex polymerase chain reaction (m-PCR) assays were used to detect Escherichia coli (E. coli) and Vibrio species (spp.) from environmental samples. The measured physicochemical parameters were generally within the applicable national standards guideline ranges; however, enteric pathogen-associated genetic markers were detected in selected water samples. Molecular analysis demonstrated marked differences in the occurrence of E. coli pathotype-associated markers across household environmental matrices. ETEC was detected in drinking water (5/29, 17.2%) and was most frequent in dishcloths (12/28, 42.9%), followed by surfaces (10/28, 35.7%) and hands (9/29, 31.0%). STEC was detected in hands (10/29, 34.5%), surfaces (9/28, 32.1%), dishcloths (6/28, 21.4%), and toilets (6/29, 20.7%), but not in water. Matrix-specific associations were observed for STEC, typical EPEC, ETEC, and astA-positive E. coli after Benjamini–Hochberg correction. Vibrio-associated markers were detected occasionally: wbeO in one dishcloth (1/28, 3.6%) and sodB in one hand (1/29, 3.4%) and one surface sample (1/28, 3.6%); ctxA was not detected. These findings indicate that household contact matrices may represent important points of molecular contamination and potential cross-contamination. Molecular approaches such as Polyemerase Chain Reaction (PCR) does not establish viability, infectivity, toxin expression, or transmission, therefore the findings should not be interpreted as evidence of viable pathogens or disease. Longitudinal studies incorporating culture-based confirmation and quantitative molecular approaches are needed. Nevertheless, the results support the importance of household hygiene interventions targeting hands, dishcloths, and frequently touched surfaces. Full article

Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterised by persistent symptoms of inattention, impulsivity, and hyperactivity that can affect individuals throughout childhood and adulthood. Accurate and timely detection is important for appropriate support and management, but current assessment relies heavily on behavioural observations, clinical assessments, and information from multiple sources, which can be time-consuming and may be influenced by subjective judgement. These challenges have increased interest in machine learning (ML) and deep learning (DL) approaches that can analyse multiple physiological and behavioural data sources. Multimodal AI approaches may provide a more comprehensive and objective way to detect ADHD by combining complementary information from different data types. Objectives: This systematic review aimed to examine the use of ML and DL methods for ADHD detection using multimodal physiological and behavioural biomarkers, with particular attention to data sources, modelling approaches, multimodal fusion strategies, and reported diagnostic performance. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to identify studies published from 2015 to 2026 that applied machine learning (ML) or deep learning (DL) methods to multimodal data for ADHD detection. We selected studies using predefined inclusion and exclusion criteria and extracted and synthesised data from eligible studies. The review included 25 studies. The multimodal data sources examined included neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) and magnetic resonance imaging (MRI); physiological signals, including electroencephalography (EEG), heart rate variability (HRV), and electrodermal activity (EDA); behavioural assessments; and wearable or Internet of Things (IoT)-based measures. We also assessed the included studies for methodological quality and model performance to identify current evidence, limitations, and future research needs. Results: The included studies varied substantially in design and sample size, with participant numbers ranging from 22 to 15,883. Most studies used cross-sectional designs. Early multimodal fusion approaches frequently reported diagnostic accuracies above 80%, demonstrating the potential value of combining complementary physiological and behavioural information. Reported informative biomarkers included frontal brain activity, heart rate patterns, and oculomotor behaviour. However, substantial differences in data sources, acquisition procedures, feature extraction, modelling techniques, and evaluation protocols limited direct comparison across studies. Small sample sizes and a lack of standardised data collection and methodological protocols also challenged the generalisability and reliability of the reported findings. Conclusions: Multimodal machine learning and deep learning approaches show considerable potential for improving ADHD detection by integrating complementary physiological and behavioural information. However, substantial heterogeneity in data collection, devices, datasets, validation procedures, and reporting limits comparability and generalisability across studies. Future research should prioritise standardised data-collection protocols, larger and more diverse datasets (including children, adolescents, and participants from different cultural backgrounds), leakage-safe validation, and independent multi-site testing. Fusion strategies should be selected based on modality compatibility and study objectives. Improving model interpretability alongside predictive performance will also support clinical trust and real-world adoption. Full article

Background/Objectives: HF33 is a polyherbal formulation developed from Thai folk medicinal knowledge for the management of dyspepsia and gastric ulcer-like symptoms. Its gastroprotective activity and underlying mechanisms have not been systematically investigated. This study aimed to characterize the HF33 formulation and evaluate the gastroprotective activity, possible mechanisms of action, and acute oral toxicity of its ethanolic extract. Methods: HF33 formulation powder was characterized according to the Thai Herbal Pharmacopoeia using pharmacognostic, physicochemical, and HPLC-PDA analyses, and the extract was chemically fingerprinted by compact mass spectrometry. Antioxidant activity and total phenolic and flavonoid contents were determined. Gastroprotective activity of the extract (150, 300, and 600 mg/kg, p.o.) was evaluated in male Sprague–Dawley rats using restraint water immersion stress-, indomethacin-, and acidified ethanol (EtOH/HCl)-induced gastric ulcer models. Histopathology, MDA and SOD, gastric mucus content, and gastric secretion parameters were assessed. Acute oral toxicity was evaluated in female Sprague–Dawley rats according to OECD Test Guideline 420. Results: Quality characterization established the identity and quality attributes of the HF33 formulation and its extract prior to biological evaluation. The HF33 extract exhibited in vitro antioxidant activity and significantly reduced gastric lesion formation in all three ulcer models. Gastroprotection was associated with preservation of gastric mucosal architecture, increased gastric mucus content, reduced MDA levels, and restored SOD activity, without significant alteration of gastric secretion parameters. No treatment-related toxicity was observed following a single oral administration of 2000 mg/kg. Conclusions: The HF33 ethanolic extract exhibited gastroprotective activity across multiple experimental gastric ulcer models. The protective activity was associated with preservation of mucosal integrity, enhancement of the mucus barrier, and attenuation of oxidative damage without affecting gastric acid secretion. These findings support further preclinical investigation of HF33 as a quality-characterized polyherbal formulation with potential gastroprotective properties. Full article

Background: Pediatric bacterial meningitis remains an important cause of death and long-term neurological disability despite advances in vaccination, antimicrobial therapy, and supportive care. Because host inflammation contributes substantially to secondary neuronal injury, adjunctive corticosteroids have been investigated as a strategy to reduce inflammation-mediated complications. This narrative review aims to critically evaluate the current evidence on corticosteroid therapy in pediatric meningitis, with particular emphasis on acute bacterial meningitis, major clinical and neurological outcomes, treatment timing, pathogen-specific effects, and the impact of changing post-vaccination epidemiology. Methods: A structured search of PubMed/MEDLINE, Scopus, and Web of Science was conducted for relevant publications available up to June 2026. Randomized controlled trials, observational studies, systematic reviews, meta-analyses, and international guidelines involving patients aged 0–18 years were considered. Evidence was synthesized narratively, focusing on mortality, hearing loss, neurological sequelae, long-term outcomes, safety, causative pathogen, and timing of corticosteroid administration. Results: Adjunctive dexamethasone has not demonstrated a consistent mortality benefit, while evidence concerning hospital stay, intensive care utilization, and healthcare costs remains limited. Its most reproducible benefit is a reduction in sensorineural hearing loss, particularly in Haemophilus influenzae type b (Hib) meningitis. Benefits in pneumococcal meningitis are less consistent, and evidence supporting routine use in meningococcal disease remains limited. Efficacy appears greatest when dexamethasone is administered before or with the first antibiotic dose and may be less pronounced in the post-vaccination era. In tuberculous meningitis, corticosteroids improve survival, whereas routine use is not supported in uncomplicated viral meningitis or cryptococcal meningitis. Conclusions: Adjunctive dexamethasone should be used selectively in pediatric meningitis, with its clearest benefit being hearing preservation in early-treated Hib meningitis. Treatment decisions should account for pathogen, timing, and epidemiological context. Full article

Introduction: Acute otitis media (AOM) is a common pediatric condition requiring careful assessment to distinguish children needing medical referral from those suitable for watchful waiting. Community pharmacists are often the first healthcare professionals consulted; however, evidence regarding the quality and guideline concordance of their AOM management remains limited. This study evaluated community pharmacists’ management of pediatric AOM across scenarios of varying clinical severity. Methods: A cross-sectional simulated patient study was conducted using three standardized pediatric AOM scenarios: a 5-month-old infant requiring referral, an 18-month-old child suitable for watchful waiting, and a 5-year-old child with multiple red flags requiring urgent referral. Trained simulated caregivers completed 203 community pharmacy visits. The primary outcome was scenario-specific guideline-concordant management. Secondary outcomes included referral, watchful waiting, antimicrobial recommendations, information gathering, counseling, and communication quality. Generalized estimating equation (GEE) logistic regression models accounted for clustering by simulated patient. Results: Guideline-concordant management was provided in 58.8% of Case 1 visits, 8.8% of Case 2 visits, and 58.2% of Case 3 visits. Referral was recommended in 60.3%, 47.1%, and 58.2% of visits, respectively, whereas watchful waiting was recommended in only 13.2% of the low-severity Case 2 visits. Antimicrobials were recommended in 7.4%, 22.1%, and 22.4% of visits, predominantly as topical antibiotics. Although pharmacists commonly assessed patient age and generally communicated professionally, assessment of symptoms, fever, ear discharge, medication use, allergies, and recurrent infections was inconsistent. Compared with Case 2, Cases 1 and 3 had substantially greater odds of guideline-concordant management. Conclusions: Community pharmacists’ management of suspected pediatric AOM varied considerably by clinical presentation. Major gaps were observed in watchful waiting, systematic clinical assessment, and antimicrobial stewardship. Standardized triage protocols and targeted professional training are needed to improve safe, guideline-concordant management. Full article

Armed conflict is a well-established risk factor for children’s mental health, yet young children remain underrepresented in research on mental health among children affected by conflict in the Gaza Strip. This systematic review synthesized evidence on the relationship between war exposure and mental health outcomes among children aged 0–8 years in the Gaza Strip, drawing on studies published between 2000 and 2026 to compare findings across conflicts preceding and following the October 2023 escalation. Following PRISMA 2020 guidelines, five databases (Scopus, Web of Science, ERIC, PubMed, and PsycINFO) were searched, yielding 220 records. After screening and full-text assessment, seven reports representing six independent samples (N = 309–11,646) met the eligibility criteria; all included studies were conducted in the Gaza Strip, and no eligible studies from the West Bank or East Jerusalem were identified. Methodological quality, assessed using the JBI Critical Appraisal Checklist, ranged from low to high. Across the outcomes examined, including post-traumatic stress, anxiety, depression, and broader psychosocial and behavioral functioning, greater trauma exposure was generally associated with poorer outcomes. Reported PTSD estimates varied substantially across studies and assessment approaches, ranging from 6% in a preschool sample to 57.8% in a broader sample, with the latter estimate decreasing to 15.6% under a stricter DSM-5 diagnostic algorithm. These differences likely reflect, at least in part, variation in age range, assessment instruments, and diagnostic thresholds. Caregiver mental health was consistently associated with child outcomes and, in some studies, showed stronger associations than children’s reported trauma exposure. Household socioeconomic disadvantage was also associated with poorer outcomes, while findings regarding sex differences in internalizing symptoms were inconsistent; boys were more frequently reported to exhibit externalizing problems. All included studies were cross-sectional and conducted in Gaza, limiting causal inference and geographic generalizability. These findings indicate that supporting children’s mental health in the context of war cannot be separated from supporting the caregivers who raise them and underscore the urgent need for longitudinal and intervention-focused research addressing the ongoing consequences of the 2023–2025 war. Full article

Digital technologies are reshaping companion-animal healthcare by expanding access to veterinary services and enabling more connected, data-driven models of care. This narrative review synthesizes current evidence on digital veterinary care, with a focus on the integration of telemedicine, artificial intelligence (AI), and remote patient monitoring (RPM) in companion-animal practice. A targeted narrative literature search of PubMed, Scopus, CAB Abstracts, Google Scholar, and relevant professional guidelines published between 2010 and 2026 was conducted to identify evidence on digital veterinary healthcare; the synthesis was narrative, and no formal risk-of-bias or study-quality assessment was undertaken. Current evidence suggests that telemedicine has shown the greatest value for teletriage, follow-up consultations, chronic disease management, specialist referral, and postoperative care, while RPM extends longitudinal monitoring beyond clinic visits and AI has been evaluated for applications in diagnostic imaging, predictive analytics, clinical decision support, and workflow efficiency, largely in retrospective, pilot-scale, or single-center veterinary studies. However, widespread implementation remains constrained by limited veterinary-specific validation of AI algorithms and commercial biosensors, evolving veterinarian–client–patient relationship (VCPR) regulations, cybersecurity and data privacy concerns, interoperability challenges, and the need for robust clinical evidence. Emerging concepts—including AI-integrated RPM, digital phenotyping, digital twins, federated learning, and One Health interoperability—remain largely prospective or extrapolated from human healthcare but have the potential to advance precision companion-animal medicine, strengthen disease surveillance, and improve preventive healthcare. Overall, digital veterinary care should complement rather than replace conventional veterinary practice. Future progress will depend on rigorous clinical validation, interoperable digital infrastructure, transparent AI governance, harmonized regulatory frameworks, and evidence-based implementation to improve animal welfare, veterinary service delivery, and One Health outcomes. Full article

Background/Objectives: Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder imposing massive global mortality and socioeconomic burden, with disproportionately high prevalence and suboptimal diagnostic rates in rural China. This study aimed to identify factors associated with two-dimensional diagnostic adherence—operationalized as a cognitive/psychological dimension (knowledge of diagnostic guidelines, perceived value of early diagnosis, and psychological readiness) and a behavioral dimension (completion of standard diagnostic procedures)—among COPD high-risk populations based on the Health Belief Model (HBM), and to explore the statistical mediating roles of health beliefs and social support. Economic toxicity refers to the financial burden of healthcare, including direct costs (copayments, medications) and indirect costs (transport, lost wages). Methods: A cross-sectional survey was conducted among 248 COPD high-risk individuals recruited via stratified random sampling in Pingdu District, Shandong Province, eastern China. A two-stage screening procedure was employed: first-stage screening at township health centers using portable spirometry, followed by referral for standard diagnostic evaluation at secondary or higher-level hospitals. Validated scales were applied for data collection, including the European Health Literacy Survey Questionnaire-Short Form (HLS-EU-Q16). Analyses included descriptive statistics, reliability/validity testing, Pearson and point-biserial correlations, multiple linear regression (cognitive outcome), multivariable logistic regression (behavioral outcome), and Hayes’ PROCESS macro with 5000 bootstrap samples. Benjamini–Hochberg false discovery rate correction was applied to multiple comparisons. Results: The mean cognitive dimension score was 4.935 ± 2.512, and the behavioral diagnostic completion rate was 42.3%. Independent predictors of cognitive adherence included health literacy (β = 0.286), health beliefs (β = 0.252), COPD knowledge (β = 0.239), living alone (β = −0.157), and economic toxicity (β = −0.108). Predictors of behavioral adherence identified via logistic regression were health literacy (OR = 1.28, 95%CI: 1.15–1.42), social support (OR = 1.12, 95%CI: 1.04–1.21), economic toxicity (OR = 0.82, 95%CI: 0.72–0.94), and medical accessibility (OR = 1.85, 95%CI: 1.12–3.05). Health beliefs statistically mediated 31.4% of the knowledge → cognitive adherence association and 36.9% of the economic toxicity → cognitive adherence association (cognitive outcome; behavioral indirect associations are reported on the log-odds scale with bootstrap confidence intervals). GAD-7 anxiety score was weakly correlated with adherence but not an independent predictor. Conclusions: Health literacy, health beliefs, and COPD knowledge are associated with higher COPD diagnostic adherence in rural high-risk populations in eastern China, while economic toxicity and living alone are associated with lower adherence. Health beliefs mediate the statistical associations between knowledge and economic status and cognitive adherence. Given the cross-sectional design, these findings should be interpreted as associations rather than causal effects. Dual-target interventions covering both cognitive and behavioral barriers are proposed as future strategies to optimize early COPD diagnosis in rural primary care. Full article

The term artificial intelligence has existed for several decades, but its real boom has been recorded in the last five years due to the development of powerful computing capabilities and advances in the field of algorithms. Historically, artificial intelligence began to take shape in the 1950s, when scientists Alan Turing, John McCarthy and Marvin Minsky laid the scientific and technical foundations of artificial intelligence. The term artificial intelligence was first used by John McCarthy in 1956 at a seminar called the “Dartmouth Summer Research Project on Artificial Intelligence.” When examining this concept, we are confronted with different opinions, due to the absence of a universal legal definition and the existence of a large number of ideas, definitions, approaches and theories that are either too inclusive or too specific for a particular sector. Currently, there is no global legal definition of artificial intelligence, and we encounter definitions that are more applicable to regional or local clusters of needs. Several developed countries are currently not interested in international regulation of artificial intelligence, while their relations regarding the development of new technologies are marked by competition (USA versus China) for leadership in this area. At the European Union level, early soft-law instruments from 2018 onwards provided descriptive definitions of artificial intelligence. In the communications “Artificial Intelligence for Europe” (COM(2018) 237 final) and “Coordinated Plan on Artificial Intelligence” (COM(2018) 795 final), artificial intelligence was described as “systems that display intelligent behaviour by analysing their environment and taking action—with some degree of autonomy—to achieve specific goals”. These descriptions served as policy guidance rather than binding statutory definitions. By contrast, the binding Artificial Intelligence Act of 2024 does not contain a single exhaustive legal definition of “artificial intelligence” expressis verbis; instead, it defines the operational concepts “artificial intelligence system” (Article 3(1)), “general-purpose artificial intelligence model” (Article 3(63)) and “general-purpose artificial intelligence system” (Article 3(66)). The distinction between early advisory descriptions and the later hard-law classification of systems is therefore important for understanding the evolution of EU regulatory language. When it comes to AI legislation, the United States has chosen a fragmented, industry-specific approach. There is no single overarching federal AI law. Rather, limited federal statutes, agency guidelines, executive orders, voluntary frameworks, and an increasing number of state-level laws have all contributed to the evolution of policy. Full article

Patellar tendinopathy affects approximately 18% of athletes and up to 45% of volleyball players, representing a common cause of load-related anterior knee pain and functional limitation in jumping and landing sports. Clinical diagnosis relies primarily on history and physical examination. However, no consensus has yet been established on standardized diagnostic criteria, particularly for imaging-based assessment, and no definitive clinical practice guideline currently exists; imaging abnormalities frequently coexist with symptoms but are also common in asymptomatic athletes, and incidental findings may carry a risk of overdiagnosis and unnecessary imaging-guided management. This narrative review synthesizes the current evidence on the clinical management of patellar tendinopathy in athletes, spanning diagnosis, treatment, long-term prognosis, and return to sport; the literature was identified through structured searches of PubMed, Web of Science, and Google Scholar, with the final search conducted on 1 July 2026. Exercise therapy represents the most extensively studied conservative approach and may be considered a reasonable first-line option, although no single loading mode has demonstrated clear superiority and the underlying evidence is of low to very low certainty; injection strategies differ in mechanisms and evidence strength, but most data come from short-term studies. These studies commonly combine injections with exercise, making the injection’s isolated effect unclear. Additionally, shockwave therapy is not supported by current evidence as a first-line or stand-alone option. Because structural imaging changes and clinical symptoms frequently diverge, functional outcomes rather than imaging endpoints should guide load progression and return-to-sport decisions. This review provides clinicians with a practical synthesis of the currently available evidence for treatment selection and patient management, and identifies remaining uncertainties, particularly in adolescent populations and long-term outcome prediction. Full article

Dermocosmetic products for chronic inflammatory and barrier-impaired skin conditions sit in an underexamined space between cosmetics and pharmaceutical care: widely marketed, yet what actually drives their formulation is rarely made explicit or checked against evidence-based standards. This study analyzes 73 products across six major indications involving chronic inflammatory and barrier-impaired skin conditions, with ingredient composition data extracted directly from product packaging and manufacturer documentation and analyzed using ingredient-level profiling and benchmarking against current dermatology guidelines. The results identify a stable, shared formulation core across product categories, with distinct therapeutic adaptations layered on top: ceramide-rich barrier support in atopic dermatitis, mechanism-specific actives distinguishing psoriasis, rosacea and wound-healing formulas. Guideline comparison surfaces both strong alignment and specific gaps, including under-used, well-evidenced ingredients in xerosis products and guideline-flagged irritants present in sensitive-skin formulations. Together, these findings offer manufacturers a validated formulation baseline and a way to spot genuine innovation gaps, give clinicians a mechanism-based reference for product selection and help consumers gain a clearer, evidence-anchored basis for evaluating products in an increasingly crowded and claims-saturated market. The methodology is designed for reuse, offering a practical foundation for evidence-based advancement across formulation, clinical practice and product choice in dermocosmetics. Full article

The posterior shoulder endurance test (PSET) is increasingly used in clinical and sporting settings; however, substantial methodological variability limits its interpretation and application with no current standardised guidelines for the PSET. Therefore, the aim of this narrative review was to critically evaluate the methodological variability within the PSET literature and provide evidence-informed recommendations for its standardised implementation. Considerable variation was identified in shoulder abduction angle, contraction mode, external load prescription, cadence, familiarisation procedures, verbal instruction, and failure criteria. Although the PSET demonstrates moderate-to-good relative reliability in some protocols, measurement variability limits its sensitivity to detect meaningful change at the individual level. Current evidence also highlights that the PSET performance reflects an integrated task involving posterior shoulder musculature, scapulothoracic control, trunk stability, and broader neuromuscular contributions rather than an isolated measure of posterior shoulder endurance. Consequently, the PSET should be interpreted as a task-based performance assessment rather than a discrete measure of muscular endurance. A structured framework is proposed to guide standardised test selection, implementation, and reporting. At present, the PSET is best utilised as a benchmarking tool within a clearly defined protocol, with further research required to establish its role in monitoring and to clarify the construct it represents. Full article

Background: Neuromuscular fatigue reduces a muscle’s capacity to generate force and is a key determinant of performance and injury risk. In basketball, the quadriceps femoris (QF) is central to jumping, acceleration, and changes in direction, so objective detection of QF fatigue is clinically valuable. Objective: To develop and validate machine-learning models for detecting QF neuromuscular fatigue in professional basketball players from surface electromyography (sEMG), and to relate fatigue-induced changes to knee biomechanics. Methods: In this pilot study, eight male athletes (n = 8; basketball; 18–40 years) performed standardized isometric and isotonic 30-repetition knee-extension fatigue protocols (isometric contraction at 60% of maximal voluntary contraction [MVC]; isotonic 30-repetition set against a fixed submaximal load of 60% of MVC), performed on both legs (16 limbs). sEMG from vastus lateralis (VL) and vastus medialis (VM) was recorded per SENIAM guidelines at baseline, during, and post-fatigue. Time-domain (RMS), frequency-domain (MDF, MNF), and non-linear features (approximate entropy, sample entropy, recurrence quantification analysis), plus the VL/VM ratio, were extracted; logistic regression (baseline), Support Vector Machine, Random Forest, and CNN–LSTM classifiers were trained; the three classical models were evaluated with participant-level (leave-one-subject-out) nested cross-validation in the six athletes with separate VL and VM recordings, whereas the CNN–LSTM was evaluated under within-subject validation only; knee loading was examined with finite-element (FEM) analysis in one representative athlete. Results: Fatigue reduced MDF and MNF and increased RMS in both muscles (p < 0.001), increased signal determinism, and shifted the VL/VM ratio (0.97 → 1.14, p < 0.01). Across 57 sEMG trials (eight athletes, 16 limbs), RMS increased in 50 and MNF decreased in 37, with the complete fatigue signature (rising RMS with falling MNF) present in 32 (56%). Under within-subject validation, the CNN–LSTM model reached an accuracy of 0.93 (AUC 0.97), but it was not re-evaluated under participant-level (leave-one-subject-out) nested cross-validation, so its ability to generalize across athletes is unknown and no head-to-head comparison with the classical models is claimed. Under participant-level nested cross-validation, carried out in the six athletes with separate VL and VM recordings, the classical models reached only modest performance (accuracy 0.68–0.70, AUC 0.73–0.74, with per-fold accuracy ranging from 0.52 to 0.82); in a single representative subject-specific model, FEM indicated increased localized cartilage stress under fatigue. Conclusions: In this pilot study, sEMG captured the canonical fatigue signature in just over half of the trials, and under participant-level nested cross-validation in six athletes the classical classifiers generalized only modestly (accuracy 0.68–0.70, AUC 0.73–0.74), well below the within-subject CNN–LSTM estimate. No injury outcomes were recorded, so the sEMG and finite-element markers cannot yet be used to estimate knee-injury risk; these preliminary findings require confirmation in a larger, adequately powered cohort. Full article