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Efficacy and safety of cannabinoid-based interventions for behavioral and cognitive symptoms in dementia: systematic review and meta-analysis.

BACKGROUND: Behavioral and cognitive symptoms are frequent in Alzheimer's disease and dementia, and available pharmacological options offer limited benefit. Cannabinoid-based therapies have been proposed as alternatives, but evidence remains inconclusive. METHODS: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library through November 2025 for randomized controlled trials evaluating cannabinoids in Alzheimer's disease or dementia. Primary outcomes were agitation measured by the Cohen-Mansfield Agitation Inventory (CMAI) and neuropsychiatric symptoms assessed by the Neuropsychiatric Inventory-Nursing Home version (NPI-NH). Secondary outcomes included cognition using the Mini-Mental State Examination (MMSE) and adverse events. Standardized Mean Differences (SMDs) and Risk Ratios (RRs) were synthesized using random-effects (REML) and Bayesian random-effects models. Risk of bias was evaluated with RoB 2, and certainty of evidence with GRADE. RESULTS: Nine trials (334 participants) met inclusion criteria. Cannabinoids did not improve CMAI (SMD -0.58, 95% CI -1.71 to 0.55; I2 = 84%), NPI-NH total (SMD -0.02, 95% CI -1.00 to 0.96; I2 = 67%), NPI-NH agitation (SMD -0.44, 95% CI -1.45 to 0.57; I2 = 48%), or MMSE (SMD 0.86, 95% CI -16.33 to 18.06; I2 = 96%). Bayesian posterior estimates were close to zero, supporting the absence of effect. Leave-one-out analyses reduced heterogeneity only after excluding influential trials but did not alter results. Certainty of evidence was moderate for behavioral outcomes and low for cognition. Overall adverse events were similar to placebo, while somnolence was more frequent with cannabinoids (RR 2.03, 95% CI 1.29-3.20). CONCLUSIONS: Cannabinoid-based therapies do not improve agitation, neuropsychiatric symptoms, or cognition in Alzheimer's disease and increase somnolence.

Humans

Artificial intelligence in genitourinary oncology: publication trends and systematic review.

OBJECTIVE: To conduct an analysis of publication trends and a systematic review of randomized controlled trials (RCTs) to characterize the current state of artificial intelligence (AI) use in genitourinary (GU) oncology, as AI has emerged as a transformative tool in healthcare with potential applications in diagnostics, treatment planning, and prognostication. METHODS: We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE; Ovid), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) Ultimate for studies related to AI and GU oncology, excluding non-English papers, non-human studies, review articles, and articles using AI solely for manuscript writing. Publication trends were analysed from 2013 to 2023 and categorized by study design and cancer type. RCTs were evaluated through systematic review using Covidence (Veritas Health Innovation Ltd, Melbourne, Victoria, Australia) for screening and data extraction. Two reviewers independently assessed all studies, with risk of bias (RoB) evaluated using the Cochrane RoB 2.0 tool. RESULTS: Of 2409 articles identified, 1220 met inclusion criteria. These included 962 retrospective articles, 175 prospective studies, 79 studies with combined retrospective/prospective methods, and four RCTs. Studies most commonly addressed prostate (n = 923), renal (n = 274), and urothelial (n = 194) cancers. Publications grew from 14 in 2013 to 362 in 2023, with substantial acceleration in 2019. Four RCTs were identified - one in urothelial cancer and three in prostate cancer. Two RCTs evaluated AI-based diagnostics, demonstrating improved performance over conventional methods; the remaining two RCTs evaluated AI in prognostication and treatment planning, showing improved gains in imaging interpretation and operational efficiency. RoB varied across studies, primarily related to randomisation and deviations from intended interventions. CONCLUSIONS: Artificial intelligence research in GU oncology has grown, although high-level evidence from RCTs remains limited. Existing trials underscore AI's promise in diagnostics, prognostication, and treatment planning, and the rapidly evolving nature of this field warrants continued prospective investigation.

Humans

Open Dialogue versus treatment as usual for adults presenting in crisis to mental health services in England (the ODDESSI Trial): a multisite cluster-randomised trial.

BACKGROUND: Open Dialogue is a person-centred, transdiagnostic model of mental health care that emphasises continuity, therapeutic relationships, and collaboration with the service user's social network. Open Dialogue is a service-wide approach to care involving network meetings with the service user, members of their social network, and usually two practitioners who support the network throughout the duration of care. In this cluster-randomised trial, we aimed to evaluate the clinical effectiveness of Open Dialogue versus treatment as usual for adults presenting in crisis to community mental health services in England. METHODS: This multicentre, parallel two-arm, cluster-randomised, controlled superiority trial was conducted in mental health services in five National Health Service trusts in London and the South of England. Clusters were defined at the level of primary care practices within service catchment areas. Participants were adults aged 18 years or older presenting in crisis to mental health services and registered with a practice within trial clusters. Randomisation was done at the cluster level (1:1), stratified by catchment area, and balanced on average general practice (GP) list size and Index of Multiple Deprivation (2015). The chief investigator, senior statistician, and assessors of the primary outcome were masked in the study. Participants either received Open Dialogue or treatment as usual, which refers to the functional team model currently implemented throughout English mental health services. The primary outcome was time (days) to first relapse following initial recovery from the index crisis censored at the end of the 2-year follow-up period. Participant-reported secondary outcomes were EuroQol Visual Analogue Scale, Social Provisions Scale, Lubben Social Network Scale, Questionnaire about the Process of Recovery, and the Client Satisfaction Questionnaire, measured at five timepoints over 2 years, and clinical measures were extracted from electronic health records. People with relevant lived experience were involved in the design and execution of the study. Fidelity to the model of care in Open Dialogue and treatment as usual, and adherence to the delivery of Open Dialogue, were measured prior to each site starting participant recruitment, then every 6 months thereafter until the final participant follow-up in that site. The trial was retrospectively registered (ISRCTN52653325) and is complete. FINDINGS: 185 general practices associated with six mental health Trusts across England were identified for screening. 105 practices were excluded, and 80 were included in cluster formation, forming 32 clusters that were randomly assigned (16 to treatment as usual and 16 to the Open Dialogue intervention). One mental health trust (two clusters) withdrew, resulting in five mental health trusts (30 clusters) participating in the trial. Between June 25, 2019, and Dec 9, 2021, 494 participants (266 [54%] female gender, 221 [45%] male gender, 341 [69%] White British) with a mean age of 38·1 years (SD 13·4) provided consent for study inclusion (223 in the treatment as usual group and 271 in the Open Dialogue group). Of these, 174 (78%) in the treatment as usual group and 225 (83%) in the Open Dialogue group recovered and had data enabling relapse determination; there was no significant difference between groups on the primary outcome of time to relapse following initial recovery (marginal hazard ratio 0·95 [95% CI 0·67-1·32]). For secondary outcomes, Open Dialogue was associated with significantly lower probabilities of psychiatric inpatient admission and re-referral to crisis care or secondary mental health services, and with improvements in self-rated recovery, health-related quality of life, and satisfaction with services. There were no significant differences in social network quality or size. There were 386 serious adverse events (281 in the treatment as usual group and 105 in the Open Dialogue group); 376 (97%) were deemed to be unrelated to the intervention. INTERPRETATION: Open Dialogue did not reduce time to first relapse compared with treatment as usual, the primary outcome, but it reduced acute inpatient bed use, improved service user reported outcomes and experience, and there were no significant safety concerns. Further investigation is required to determine whether Open Dialogue can enhance the effectiveness and acceptability of crisis care and continuing care in community mental health services. FUNDING: National Institute for Health Research.

Humans

Weight-bearing locomotion mitigates the progression of post-traumatic knee osteoarthritis in male rodents: A systematic review and meta-analysis.

OBJECTIVE: Although knee post-traumatic osteoarthritis (PTOA) has been associated with altered loading, how weight-bearing (WB) activities should be modulated post-injury to preserve knee health remains unknown. This systematic review and meta-analysis examined the effects of WB locomotion on knee PTOA in rodents to provide insight into potential clinical implications. DESIGN: Studies published in PubMed, Cochrane Library, Embase, and CINAHL through 02/2025 and meeting the following criteria were included: 1) rodents with knee PTOA, 2) compared locomotor exercises (treadmill/wheel) to no exercise, 3) outcome measures of PTOA (OARSI/Mankin OA scores, bone quality, osteophyte condition, cartilage quality/morphology). If significant heterogeneity across studies was observed, locomotion speed, time/number of intervention sessions, frequency and duration of intervention, exercise initiation, follow-up time, animal species, PTOA model were analyzed as potential moderator variables that may influence findings. RESULTS: Twenty-two studies met the criteria, resulting in a total of 156 effect sizes (ESs) for meta-analysis (all males). A positive ES denotes less PTOA. Based on the CAMARADES checklist, 5 studies were ranked low risk of bias and 14 studies were ranked moderate risk of bias. Locomotion significantly reduced PTOA severity evaluated using OARSI/Mankin scores (ES=0.927, 95% CI: [0.590, 1.264]) and improved bone quality (ES=0.379, 95% CI: [-0.023, 0.780]) with substantial heterogeneity across studies. Follow-up analyses indicated that greater ESs for reducing OA scores were associated with a shorter duration of each training session, more training sessions, longer intervention duration, and longer follow-up time (all P≤0.004). Greater ESs for improving bone quality were associated with a longer follow-up time and later exercise initiation post-injury (both P≤0.014). Locomotion resulted in small to moderate but non-significant positive effects for isolated measures assessing osteophyte condition (ES=0.379, 95% CI: [-0.023, 0.780]), cartilage quality (ES=0.430, 95% CI: [-0.034, 0.893]), and cartilage morphology (ES=0.464, 95% CI: [-0.006, 0.934]). CONCLUSION: WB locomotion reduced the overall degree of knee PTOA in male rodents. Composite knee OA scores may be more responsive to exercise interventions than isolated cartilage/osteophyte measures. The benefits of locomotion may be more prominent with a longer follow-up time or with exercise protocols consisting of shorter but more individual sessions and longer overall intervention durations.

Animals

Blood Metabolomic Signatures of 1-Hour Glucose Predict Cardiometabolic Risk.

BACKGROUND: Elevated 1-hour glucose levels during an oral glucose tolerance test strongly predict type 2 diabetes (T2D) and cardiovascular disease. We investigated whether the fasting blood metabolome predicting 1-hour glucose could be a target for improving β-cell function, long-term glycemic trajectories, and reducing the risks of T2D and coronary heart disease. We also investigated whether plasma microRNAs derived from key metabolic organs regulate changes in a metabolomic risk score (MRS) for predicting 1-hour glucose. METHODS: Untargeted blood metabolomics and a frequently sampled 75-g oral glucose tolerance test were performed in participants from the OmniCarb trial (n=162). In an independent weight-loss dietary intervention trial (POUNDS Lost [Preventing Overweight Using Novel Dietary Strategies]), temporal changes in MRS and plasma microRNAs measured by genome-wide sequencing were analyzed. In addition, associations of MRS at baseline and its 10-year changes with long-term risk of incident T2D and coronary heart disease were prospectively investigated in the NHS (Nurses' Health Study). RESULTS: We created a fasting blood MRS for predicting 1-hour glucose (Pearson r=0.8) and found significant associations with half-day (diurnal) postprandial glucose excursions and insulin secretion after 5-week controlled feeding interventions varying in carbohydrate amount and glycemic index. In the POUNDS Lost trial, diet-induced changes in MRSs were related to 2-year trajectories of glucose metabolism; circulating microRNAs regulating cardiometabolic abnormalities were pivotal factors influencing these changes. In the NHS, women in the top 20% of MRS had a multivariate-adjusted relative risk of 3.80 (95% CI, 2.22-6.51) for T2D and 1.48 (95% CI, 1.04-2.12) for coronary heart disease compared with those in the lowest 20%. In addition, 10-year increases in plasma metabolites related to 1-hour glucose were linearly associated with a higher risk of T2D. CONCLUSIONS: Our findings indicate that fasting blood metabolomic signatures predicting elevated 1-hour glucose reflect disease pathophysiology and could be targets for preventing T2D and coronary heart disease.

blood glucose

Efficacy of afoxolaner (NexGard®) for the treatment of myiasis caused by the zoonotic tumbu fly, Cordylobia anthropophaga, in domestic dogs under field conditions.

Cordylobia anthropophaga is a major cause of furuncular myiasis in dogs and humans in sub-Saharan Africa, yet evidence for pharmacological control remains limited. Current management relies mainly on mechanical larval removal, and no controlled studies have assessed the efficacy of modern antiparasitic agents. This study evaluated the curative and preventive efficacy of a single dose of afoxolaner (NexGard&#xae;) in dogs with naturally acquired C. anthropophaga infestation under field conditions and explored a possible indirect protective effect in puppies after maternal treatment. A randomized, blinded, negative-controlled field study was conducted in Samburu County, Kenya, and included 104 naturally infested dogs allocated to a treated group (n&#x202f;=&#x202f;53) or an untreated control group (n&#x202f;=&#x202f;51). Clinical evaluations were performed on Days 0, 15 (&#xb1;2), 30 (&#xb1;2), and 45 (&#xb1;2). Efficacy was assessed based on the presence of active nodules and clinical scores. Additional observational data were collected from puppies born to or nursing from treated bitches. Compared to the control group, parasiticide efficacy was 94% on Day 15 and 100% on Day 30. The number of parasite-free dogs reached 86.3% on Day 15, 100% on Day 30, and 95.3% on Day 45. Treated dogs showed a significant clinical improvement from the first post-treatment assessment, whereas infestation persisted in controls (nodules: OR = 0.003, 95% CI 0.001-0.017; skin lesions: OR = 0.010, 95% CI 0.002-0.051; lymph node: OR = 0.013, 95% CI 0.002-0.074; body condition: OR = 0.073, 95% CI 0.027-0.195; all p&#x202f;<&#x202f;.001), with large effect sizes at the final visit for all outcomes (r&#x202f;=&#x202f;0.521-0.793). No C. anthropophaga infestations were detected in examined puppies from treated dams, including puppies exposed during pregnancy or lactation, up to 4 weeks after birth and up to 11 weeks of age, respectively. Afoxolaner appears effective to control canine cordylobiosis and may offer indirect protection to puppies, warranting further investigation.

Animals

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology

An expanded breakfast buffet increases daily energy and protein intakes in hospitalised patients: A prospective crossover quality improvement study.

BACKGROUND & AIMS: Inadequate dietary intake remains common during hospitalisation. Ordinary hospital meals are central to nutritional intake, but their contribution depends on what is offered and what patients are able and willing to eat. We evaluated whether a preference-informed, limited expansion of the hospital breakfast buffet could increase total daily energy and protein intakes. METHODS: This prospectively structured, ward-based crossover quality-improvement study was conducted in seven inpatient wards at a tertiary university hospital. Each ward was observed for four consecutive days and randomly allocated to begin with standard or expanded breakfast, after which conditions alternated daily. The expanded buffet consisted of standard breakfast supplemented with familiar energy- and protein-rich foods selected from previous patient-choice data. Twenty-four-hour intake was registered using component-level weighed food records during the day and nursing registration overnight. Primary outcomes were total daily energy and protein intakes. Linear mixed-effects models adjusted for observation day and ward-level starting sequence and accounted for repeated patient observations and ward-level clustering. Analyses used data from patients who consumed breakfast and contributed analysable observations under both breakfast conditions. RESULTS: The primary crossover population included 71 patients contributing 188 analysable patient-days. Compared with standard breakfast, the expanded breakfast increased total daily energy intake by +198 kcal/day (95% CI 44 to 352) and protein intake by +6.8 g/day (95% CI 1.0 to 12.5), without a statistically significant increase in total food weight. Daily energy and protein adequacy increased by +8.7 and + 6.8 percentage points, respectively. The increase was driven mainly by breakfast intake, with no measurable reduction in non-breakfast intake. CONCLUSIONS: A limited expansion of the ordinary hospital breakfast buffet increased total daily energy and protein intakes in the primary crossover population of hospitalised adults who consumed breakfast. This increase occurred without a statistically significant increase in total food weight or a measurable reduction in non-breakfast intake. Small, preference-informed additions of familiar energy- and protein-rich foods at breakfast may improve daily intake by increasing the nutrient yield of foods patients are able or willing to eat.

Humans

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas

Effects of exercise on aerobic capacity in people with prehypertension or hypertension: a systematic review and meta-analysis of randomized controlled trials.

This study aimed to quantify the effects of exercise on aerobic capacity in people with prehypertension or hypertension and to identify exercise prescription parameters that optimize improvements. A comprehensive search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and Scopus from inception to 24 October 2025. Data were pooled using standardized mean differences (SMDs) with 95% confidence intervals (CI). Fifteen studies met the inclusion criteria. Exercise significantly improved aerobic capacity in people with prehypertension or hypertension (SMD&#x200a;=&#x200a;0.88; 95% CI: 0.61-1.15; P &#x200a;<&#x200a;0.00001), with multicomponent training demonstrating superior efficacy. Exploratory subgroup analyses suggest that longer programs (&#x2265;12&#x200a;weeks), lower frequency (<3&#x200a;sessions/week), 60-min sessions or longer, total weekly exercise less than 180&#x200a;min, and professional supervision may be associated with better outcomes.

Humans

Determinants of Nonspecific Response to Treatment in Randomized Controlled Trials of Major Depressive Disorder: A Narrative Review.

The design, conduct, and interpretation of double-blind randomized placebo-controlled clinical trials in major depressive disorder (MDD) are complicated by determinants of nonspecific response to treatment (NSRT). This narrative review provides a comprehensive overview of the determinants of NSRT in randomized controlled trials (RCTs) for MDD, including the placebo effect, factors related to measurement of the primary endpoint, the inclusion of misdiagnosed patients, the relapsing-remitting course of MDD, and factors related to functional unblinding. Potential strategies to reduce the impact of the determinants of NSRT and to improve the interpretation of RCT outcomes in MDD are also summarized. These strategies include use of centralized rating and standardized rater training, independent diagnostic confirmation, optimized site selection, minimizing financial incentives, exclusion of subjects participating in multiple clinical trials, exclusion of patients with unstable major depressive episode trajectories, and use of active placebo and alternative trial designs. Uniformity among experts in the definitions of determinants of NSRT and related concepts, as well as in strategies to address them, may facilitate progress in the development of novel treatments for MDD.

Humans

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype

Assessing AI literacy and attitudes among medical students: implications for integration into&#xa0;healthcare practice.

PURPOSE: This study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice. DESIGN/METHODOLOGY/APPROACH: A quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare. FINDINGS: The study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI's role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology. ORIGINALITY/VALUE: This study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Students, Medical

Effectiveness of a Web-Based Educational eHealth Platform on Women's Health Literacy About Phthalate Exposure: Randomized Controlled Trial.

BACKGROUND: Phthalates are environmental endocrine-disrupting chemicals widely used in plastics, cosmetics, food packaging, and personal care products. Women may experience frequent exposure through everyday consumer and household products. Improving phthalate-related health literacy may support informed exposure-reduction decisions; however, conventional health education provides limited opportunities for repeated, interactive, and individually tailored learning. OBJECTIVE: This randomized controlled trial evaluated the effectiveness of an eHealth educational intervention (Phthalates Free) in improving women's overall and domain-specific phthalate-related health literacy and examined the association between platform engagement and health literacy outcomes. METHODS: A double-blind randomized controlled trial was conducted in the outpatient department of a regional teaching hospital in Taipei, Taiwan. A total of 114 women were randomly assigned to an intervention group (n=58) receiving a 6-month eHealth platform-based education program and a control group (n=56) receiving conventional paper-based education. Assessments were conducted at baseline (T0), 3 months (T1), and 6 months (T2). The Phthalate Health Literacy Scale (10 items; &#x3b1;=.90, content validity index=0.93) measured overall and domain-specific literacy (health care, disease prevention, and health promotion). Longitudinal outcomes were analyzed using generalized estimating equations based on all available observations according to participants' original randomized assignments, with adjustment for waist circumference and pregnancy history. Analysis of covariance (ANCOVA) was used to compare 6-month outcomes after adjustment for baseline scores. Platform engagement and perceived usability were assessed using back-end analytics and the System Usability Scale (SUS). RESULTS: At 6 months, the intervention group showed a significantly greater increase in total health literacy than the control group (+9.93 points, Wald &#x3c7;&#xb2;1=17.74; P<.001). Domain analyses revealed significant improvements in health care (+1.52; P=.001), disease prevention (+1.32; P=.001), and health promotion (+1.12; P=.001) domains. ANCOVA confirmed the between-group difference at T2 after adjusting for baseline scores (F1,109=11.43; P=.001; adjusted mean difference=7.15, 95% CI 2.96-11.34). Engagement analysis showed that high-engagement users (n=10) scored significantly higher in overall health literacy (t55=-3.00; P=.004) and all domains than general users. The SUS results (mean 84.7, SD 5.2; n=46, 79.3%) indicated high perceived usability. CONCLUSIONS: The Phthalates Free eHealth educational intervention significantly improved women's overall and domain-specific health literacy over 6 months. Higher platform engagement was associated with better health literacy outcomes. The intervention may serve as a practical adjunct to nurse-led education in outpatient and community settings by providing accessible, continuous, and evidence-based guidance on reducing phthalate exposure.

Humans

Urban stormwater infrastructure as a microplastic superhighway: a critical review of transport dynamics, modelling, and mitigation across pavements and drainage networks.

This review examines the transport, fate, modelling, and mitigation of Microplastics (MPs) in urban stormwater infrastructure, with emphasis on pavements, runoff pathways, micro-drainage, and macro-drainage systems. Following a systematic review approach, more than 1000 records were screened and approximately 50 core studies were retained when they addressed urban stormwater or drainage-related MP transport with adequate methodological reporting; marine-only studies and biological-effect studies without direct relevance to transport processes were excluded. The evidence shows that stormwater systems function not merely as passive conduits but as dynamic reactive transport systems with temporary storage, where particle mobilisation, sedimentation, resuspension, and temporary retention regulate MP export. Road surfaces, especially high-traffic areas, are major reservoirs of tyre wear, road-marking, atmospheric, and litter-derived particles that are rapidly mobilised during rainfall. Conventional grab sampling may underestimate MP loads, which in some cases exceed treated wastewater effluent loads by up to six-fold. Drainage structures such as manholes can immobilise up to 17.3% of near-neutrally buoyant particles, while biofouling and aggregation may shift buoyant polymers from wash-load to bedload. Mitigation systems, including permeable pavements, bioretention, wetlands, and technical inserts, can achieve high removal of coarse MPs, but performance declines for fine particles below 100&#xa0;&#xb5;m. The review highlights the need for standardised flow-proportional sampling, physically informed modelling, and treatment-train strategies targeting both surface sources and in-network storage.

Microplastics

Rhinoplasty Difficulty Scale: Development and Psychometric Analysis of a Surgeon's Assessment of Rhinoplasty Technique and Nasal Deformity Correction.

BACKGROUND: Rhinoplasty surgeons lack a universal scale of the relative difficulty of rhinoplasty techniques and rhinoplasty deformities. OBJECTIVE: To compare the expert opinion of the difficulty of rhinoplasty techniques and rhinoplasty deformities among international rhinoplasty surgeons, as measured by a scale of difficulty. METHODS: A cross-sectional survey of rhinoplasty surgeons collected training levels, experience, case volume, and perceived expertise. Rhinoplasty techniques/deformities (n = 64) were rated from 1-10, representing the least to most technically demanding. Rasch analysis was used to examine the fit of the observed data to Rasch model requirements, assess rating scale functioning, and provide estimates of internal consistency. RESULTS: Respondents (n = 63) were in practice (<5 years, 14%; 5-10, 20%; 10-20, 20%; 20-30, 26%; >30, 20%), and rhinoplasty volume ranged from <25 (14%) to >100 cases/year (32%). Self-reported expertise was comfortably novice (32%), intermediate (10%), advanced (28%), and expert (30%). Otolaryngology (42%), facial plastic surgery (30%), and plastic surgery (28%) were represented. Rasch estimates of internal consistency reliability were excellent (0.96 for surgeons and 0.99 for items); the item difficulties were more heterogeneous (mean: 0, SD: 1.23) than the distribution of surgeons (mean: -0.09, SD: 0.58). Survey items were ordered by difficulty, ranging from least difficult (inferior turbinate reduction = 1.01) to most difficult (contracted nose repair post-infection = 8.24). CONCLUSION: The newly developed Rhinoplasty Difficulty Scale provides ratings of common rhinoplasty techniques and deformities with a high correlation among experts using this rating scale.

Humans

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans

Facilitators and Barriers to Volunteers' Involvement in Palliative Care: A Qualitative Meta-Synthesis.

OBJECTIVE: This study aims to systematically synthesize qualitative evidence on facilitators and barriers to volunteer involvement in palliative care services, providing insights to inform strategies for strengthening volunteer support systems. METHODS: PubMed, Web of Science, Embase, Cochrane Library, Medline, EBSCO, ProQuest, China National Knowledge Infrastructure, Wanfang, VIP, and Sinomed were searched from inception to December 2025 to identify qualitative studies examining factors influencing volunteer participation in palliative care. Methodological quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Data were analyzed using Thomas and Harden's thematic synthesis approach and managed using NVivo 12.0 software, following the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidelines. RESULTS: Thirty-one studies involving 1042 participants were included, yielding 68 findings. Facilitators included intrinsic motivation and meaning-making at the individual level; supportive relationships and teamwork at the interpersonal level; structured support and professional recognition at the organizational level; social recognition and resource integration at the community level; and institutional safeguards and governmental incentives at the policy level. Barriers included emotional burden and limited competencies at the individual level; relationship conflicts and insufficient collaboration at the interpersonal level; management deficiencies at the organizational level; community resource imbalances at the community level; and inadequate regulations and incentives at the policy level. CONCLUSION: Volunteer participation in palliative care is influenced by multiple interacting factors. Strengthening training and support systems, enhancing team collaboration, and improving institutional frameworks may help sustain volunteer engagement and improve the quality of palliative care services.

Palliative Care