School-based health promotion is critical for enhancing health literacy and academic performance. In South Africa, preventable visual impairment among school children may be exacerbated by the absence of structured eyecare promotion interventions. The objective of the study was to implement and evaluate the effectiveness of eyecare health promotion in the Thabo Mofutsanyane district, Free State province. A school-based cross-sectional interventional study was conducted. Following a simple randomised assignment, 10 schools received the intervention while the remaining 10 received no intervention. An adopted and piloted questionnaire was administered at baseline and readministered 6 months after the intervention. The McNemar's chi-square test was used for statistical analysis. The baseline study included 199 participants, and following attrition, stood at 136 learners, parents, and teachers. The learner experimental arm demonstrated a statistically significant change in eyecare knowledge (p < 0.05). While no statistically significant changes were noted among teachers, parents and learners had statistically significant variables. Despite limitations, this study demonstrated that targeted eye health promotion can improve eyecare knowledge.
Human norovirus is a major acute gastroenteritis pathogen with high genotypic diversity, mutation, and recombination rates. Existing sequencing methods for monitoring its genetic variations are limited by low sensitivity and contamination risk. This study optimized our previously established Micro Target Hybrid Capture System (MT-Capture) by increasing nucleic acid input, streamlining library preparation, and introducing a deployable USER enzyme-based contamination prevention system. The optimized protocol completes library preparation and capture within 7 hours. Compared with multiplex PCR sequencing, optimized MT-Capture avoids primer updates, captures multiple genotypes with improved compatibility, and detects low-abundance samples more efficiently. The USER enzyme degrades contaminating amplicons, ensuring data authenticity. The MT-Capture-USER system provides a rapid, sensitive, and contamination-resistant approach for norovirus whole-genome sequencing and epidemiological surveillance.
Growing evidence supports a close association between childhood obesity and precocious puberty (PP). However, there remains a lack of bibliometric research specifically examining the factors and trends within this intersecting field. A systematic bibliometric analysis would provide a comprehensive understanding of the current research landscape, identify key contributors and emerging trends. On June 2, 2026, relevant studies published between 2005 and 2025 were retrieved from the Web of Science Core Collection (WoSCC), Scopus and PubMed databases. Using the bibliometrix R 4.5.1, CiteSpace 6.4.R1, and VOSviewer 1.6.20, we conducted a visual analysis of dimensions such as publication volume, countries, institutions, authors, journals, and keywords. We employed the Latent Dirichlet Allocation (LDA) method to reveal the underlying thematic structure and track its temporal evolution. A total of 2506 publications were included, involving 9,843 authors and spanning 664 academic journals. The annual publication volume increased steadily, with the United States and China as the major contributing countries. Harvard University led institutional output, followed by the University of California System and Leipzig University. Chen Y was the most prolific author with 42 publications. Journal of Pediatric Endocrinology & Metabolism was the most productive journal in this field. "Obesity" (1,179) and "puberty" (1,031) were identified as the core research keywords. LDA analysis yielded 15 topics, which were grouped into four clusters. These clusters respectively cover adrenal endocrine function and pubertal physiology, obesity-related metabolic complications and multisystem damage in children, the link between childhood obesity and pubertal timing, and relevant upstream etiological factors. This study applies bibliometric analysis and LDA topic modeling to trace the development of research on the association between childhood obesity and PP over the past two decades. It systematically reviews research progress in this field, clarifies publication trends, the global research landscape, and patterns of thematic evolution, thereby providing an important academic reference for clinicians and scholars in the field of pediatrics to identify research hotspots and cutting-edge directions.
To investigate the prevalence of gastrointestinal symptoms (reflux, nausea, and vomiting) among nurses in COVID-19 isolation wards and identify associated factors, with a specific focus on discomfort caused by personal protective equipment (PPE). A cross-sectional survey. In March 2020, 354 of 368 eligible nurses (96.2% response rate) from the COVID-19 isolation wards of a designated hospital in Wenzhou, China, completed the survey. Data were collected using a demographic questionnaire, a self-rated PPE discomfort scale, the Gastroesophageal Reflux Disease Questionnaire (GerdQ), the Pittsburgh Sleep Quality Index (PSQI), and the Symptom Checklist-90 (SCL-90). Statistical assessments included univariate, correlation, and multivariable logistic regression analyses. Firth's penalized likelihood logistic regression and sensitivity analyses were applied to address sparse data in specific subgroups. The overall prevalence of gastrointestinal symptoms in the cohort was 23.2% (82/354). Multivariable regression identified several independent factors significantly associated with these symptoms (all P < 0.01): severe PPE-induced discomfort (OR = 3.64, 95% CI: 1.95-6.80), elevated psychological stress (SCL-90 total score: OR = 1.14, 95% CI: 1.09-1.20), and poor sleep quality (PSQI total score: OR = 2.10, 95% CI: 1.51-2.92). Although univariate analyses suggested protective associations for prior intensive care unit (ICU) experience and male gender, these effects lost statistical significance following Firth correction. Temporally, 76.8% of symptomatic cases emerged within the first five days of the shift cycle. Gastrointestinal symptoms are prevalent among nurses in COVID-19 isolation wards and are strongly associated with PPE-induced discomfort, psychological stress, and sleep disturbances. Although the cross-sectional design precludes causal inference, these findings underscore an urgent need for the ergonomic optimization of PPE to alleviate heat stress and physical burden. Furthermore, implementing rapid adaptation training programs and integrating psychological and sleep support into routine occupational health surveillance are vital to safeguarding frontline clinicians.
Latent autoimmune diabetes in adults (LADA) is characterized by progressive β-cell impairment and severe glycemic lability, predisposing patients to in-hospital hypoglycemia. Few tailored risk-stratification models exist for this population. This study aimed to develop and validate an interpretable machine learning model using routine clinical data to predict in-hospital hypoglycemia in LADA inpatients. This multicenter retrospective study recruited participants from five Chinese tertiary hospitals between January 2019 and September 2025. Data from four centers formed the derivation cohort, and the remaining center served as the independent external validation cohort. The primary endpoint was in-hospital hypoglycemia (blood glucose < 3.9 mmol/L). Three machine learning models, including logistic regression, random forest, and XGBoost, were developed using routine clinical data and assessed for discrimination, calibration, and clinical utility. SHAP analysis was applied to improve model interpretability. Exploratory subgroup analyses in the internal validation cohort examined model performance across clinical subgroups. A total of 752 LADA inpatients were enrolled. The incidence of in-hospital hypoglycemia was 44.8% in the derivation cohort and 54.4% in the external validation cohort. Six core predictive factors were identified: largest amplitude of glycemic excursion, fasting C-peptide, glycated hemoglobin, sex, insulin pump use, and previous hypoglycemia. The three models yielded numerically variable discriminative performance across cohorts. Pairwise DeLong tests indicated no statistically significant differences in the AUROC among the three algorithms during external validation. All models showed comparable calibration and threshold-dependent predictive performance in the external cohort. XGBoost was selected as the final model after comprehensive evaluation. Fasting C-peptide was identified as the most influential predictor. Exploratory subgroup analyses demonstrated generally stable model performance across clinical strata. These findings are limited by small subgroup sample sizes and wide confidence intervals, and thus cannot be generalized to external populations. Sensitivity analysis suggested that model performance was not predominantly dependent on the retained glucose-derived predictor. The interpretable XGBoost model showed acceptable discrimination, calibration, and potential clinical utility for in-hospital hypoglycemia risk stratification in patients with LADA. This pragmatic predictive tool has the potential to support individualized inpatient glycemic management and facilitate targeted clinical intervention for LADA populations.
This study aimed to assess current dietary patterns in adults with inflammatory bowel disease (IBD) in New Zealand and to evaluate associations with current self-reported disease activity and well-being. A prospective online survey was conducted using validated dietary data collection instruments. Dietary patterns were derived using principal component analysis. Self-reported current disease activity and health-related quality of life (HRQoL) scores were ascertained. Relationships between diet patterns and other variables were assessed. The responses of 205 participants were included in the analysis: mean age 43.0 (±14.0) years, and a mean disease duration of 31.5 (±14.0) years. There were 107 (52%) people with Crohn's disease (CD) and 98 (48%) with ulcerative colitis/IBD-unclassified (UC/IBDU). Six dietary patterns were identified among the cohort data: Western, vegetarian, pescatarian, semi-vegetarian, semi-pescatarian, and low-carbohydrate. Consuming a Western dietary pattern was associated with active CD (adjusted odds ratio [AOR] = 4.55, 95% CI [1.27, 7.26], p = 0.02) or active UC/IBDU (AOR = 3.50, 95% CI [1.07, 5.40], p = 0.04). In contrast, people with CD following a vegetarian diet pattern were less likely to report active disease (AOR = 0.32, 95% CI [0.11, 0.98], p = 0.04). Adherence to a Western or semi-pescatarian dietary pattern was predictive of worse HRQoL. In this group of adults with IBD, vegetarian dietary patterns were associated with lower current CD activity but not UC/IBDU, while the Western dietary pattern was associated with higher disease activity and impaired HRQoL.
Large language models (LLMs) are increasingly used in higher education, yet empirical evidence for their effectiveness in art education remains scarce. This study aimed to evaluate whether LLM assistance could improve undergraduate art history question-answering performance and explanatory support. This study developed the Art History Theory Question Set (AHTQS), comprising 104 single-choice items with Bloom-level annotations, and benchmarked three LLMs (ChatGPT-4o, DeepSeek-V3, and Qwen2.5-Plus). DeepSeek-V3 showed the highest accuracy (96.2%) and lowest observed run-to-run variability and was selected for a randomized crossover pilot study with six undergraduates. The primary outcome was the change in examination accuracy from independent to LLM-assisted answering. A Likert-scale evaluation involving nine students and three instructors was also conducted to assess the clarity and coherence of LLM-generated explanations. A one-sided Wilcoxon signed-rank test showed significant improvement with LLM support [W(6) = 21.0, p = 0.0156, r = 0.879], with median accuracy increasing from 43.3% to 93.3% (median gain = 42.3%). Five of six students showed higher accuracy under the LLM-assisted condition, and no clear evidence of a sequence or carryover effect was detected (Mann-Whitney U = 7.0, p = 0.3758). Domain-level analyses indicated significant gains in all four categories (p < 0.05). Error-frequency analysis further showed marked reductions in high-frequency mistakes. The Likert-scale evaluation indicated high perceived clarity and coherence of LLM explanations, with favorable but more cautious instructor ratings. These pilot findings suggest that supervised LLM assistance may support art history question-answering and explanatory feedback. Future studies should validate these findings in larger cohorts, assess delayed learning retention, and examine open-ended, image-based, and higher-order art history tasks before curriculum-level implementation.
Spinal cord stimulation (SCS) is an established therapy for chronic pain, but post-implant care depends on timely identification of evolving patient needs. Remote monitoring may help detect therapy-use changes that scheduled follow-up and patient-initiated contact can miss. We evaluated Proactive Intelligence, a workflow-integrated, human-in-the-loop AI recommender using passively collected SCS device-interaction data to support care without added patients' data-entry burden. We conducted a prospective, observational real-world pilot among research-consented patients implanted with Prospera SCS devices. Proactive Intelligence used a hybrid neural network incorporating longitudinal therapy-use patterns, operational variables, and patient history/demography to identify patient-days with therapy-adjustment-associated patterns. Model outputs were translated into a prioritized daily review list for the Embrace Care Team (ECT), which reviewed cases, performed outreach when appropriate, adjudicated behavioral-change reasons, and recorded downstream patient care actions. Outcomes included model enrichment, field adjudication yield, intervention distribution, and pain-score change after outreach when follow-up pain scores were available. Model development used data from 747 permanently implanted patients with sufficient telemetry. Confirmed reprogramming events were rare, occurring at 0.77% per patient-day. At the high-specificity threshold, the model achieved a positive predictive value of 28.8% and sensitivity of 8.6% on a held-out test set, corresponding to approximately 37-fold enrichment over baseline prevalence. During the pilot, predictions were generated across 101,042 patient-days. Across 91 operational days, the ECT reviewed 188 cases involving 175 unique patients. Among reviewed cases, 152 patients responded within three outreach attempts, yielding an 80.9% response rate. Of all reviewed cases, 66.0% were adjudicated as "therapy-related." Among reachable patients, the therapy-related yield was 81.6%, and "Address therapy" actions occurred in 67.8% of cases, including reprogramming and therapy adjustment/discussion. Among cases with follow-up pain scores, therapy-relevant cases showed significant mean NRS reduction after outreach, with improvement after both reprogramming and non-reprogramming therapy adjustment. To our knowledge, this pilot represents the first real-world evaluation of an ECT driven, human-in-the-loop care workflow supported by passively collected SCS device interaction data, without added patient data-entry burden. The findings suggest that machine learning can assist longitudinal SCS care by surfacing behaviorally meaningful changes for timely human review, contextual interpretation, and coordinated support.
Disturbances in brain fluid homeostasis are increasingly implicated in neurodegeneration. Imaging measures of structural alterations of the choroid plexus (CP) and impaired glymphatic transport have each been associated with cognitive decline, yet their potential interaction in humans remains poorly understood. We investigated the relationship between CP volume, glymphatic diffusion, and cognitive performance in 100 memory clinic patients. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) was used as an imaging proxy of glymphatic diffusion, and CP volume and WMH volume were derived from structural MRI using FastSurfer segmentation. Multivariable linear regression models examined associations between CP volume, ALPS index, and global cognitive performance measured by the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Models were adjusted for age, sex, education, APOE ε4 status, WMH burden and plasma phosphorylated tau (pTau217). Interaction terms tested whether CP structure and glymphatic diffusion jointly influenced cognition. Larger CP volume was associated with lower ALPS index after adjustment for demographic and molecular covariates (β = -282.19, p = 0.015). CP volume and ALPS index were not independently associated with MoCA scores; however, a significant interaction between CP volume and ALPS index was observed (β = -13,299.09, p = 0.036). The association between CP volume and cognitive performance depended on DTI-ALPS, such that larger CP volumes were associated with poorer MoCA scores at higher ALPS values, whereas CP volume showed little association with cognition at lower ALPS values. This interaction improved model fit compared with main-effects models (R2  = 0.31). The findings remained significant after adjusting for CSF volume and were replicated using MMSE as the outcome. Plasma pTau217 levels were strongly associated with worse cognition but did not significantly modify the CP-ALPS interaction. CP enlargement is associated with glymphatic diffusion, and its relationship with cognitive performance varies across DTI-ALPS index values. These findings suggest that interactions between CSF regulatory systems may correlate with cognitive performance in a state-dependent manner. Notably, higher ALPS values in individuals with enlarged CP may reflect compensatory or altered perivascular fluid dynamics rather than preserved glymphatic function, highlighting the complexity of interpreting diffusion-based markers of brain clearance.
Despite the widespread deployment of current COVID-19 vaccines, significant gaps remain in understanding the complete biological behavior of the SARS-CoV-2 spike (S) protein. Through systematic characterization of mammalian expression systems, this study shows that the full-length S protein exhibits a complex intracellular distribution, predominantly localizing not only to the cell membrane but also to the cytoplasm, nucleus, and extracellular compartments. Comparative analyses revealed distinct subunit-specific trafficking patterns. The S1 subunit showed increased intracellular accumulation and secretion compared to the full-length S protein, although with reduced surface expression. Conversely, the receptor-binding domain (RBD) and S2 domains were mainly associated with cytoskeletal (CS) structures. Notably, the signal sequence-enhanced RBD (SS-RBD) construct engineered in this study demonstrated dramatically enhanced extracellular accumulation, approximately 100-fold higher than the full-length S protein and 10-fold greater than S1, as measured by proximity extension assay (PEA). Signal peptide modification effectively redirected RBD from CS retention to efficient secretion, significantly improving detection sensitivity. PEA outperformed conventional methods such as flow cytometry (FACS), Western blotting (WB), and immunofluorescence, offering sensitivities several orders of magnitude higher. Consequently, these findings provide: (1) a structural framework for rational antigen design by distinguishing essential versus dispensable domains; (2) experimental support for SS-RBD as a promising vaccine candidate due to its high secretion efficiency and preservation of neutralizing epitopes; and (3) a robust platform using PEA for high-sensitivity antigen characterization. This study enhances the fundamental understanding of spike protein biology and offers actionable insights for developing next-generation vaccines targeting SARS-CoV-2 and related coronaviruses.
Psychoactive substance use (PSU) has become a major public health concern. Many countries are alarmed by the increasing use of psychoactive substances among adolescents, and Ghana is not an exception. The study assessed psychoactive substance use among adolescent commercial tricycle riders of Tamale, Northern Region, Ghana. A community-based cross-sectional study was conducted among 342 adolescent commercial tricycle operators selected from five (5) selected operational areas. Respondents were selected using a multistage sampling approach with simple random sampling within the operational areas, and a structured questionnaire was used as a data collection tool. The prevalence rate of psychoactive substance use was 60.8%. Reasons for use include acceptability (20.4%), increased self-confidence (60.8%), and improved work performance (24.6%). Factors that influenced psychoactive substance use include religious affiliation (Christian [aOR = 2.77 (CI: 1.44, 5.50), p = 0.003] or a Traditionalist [aOR = 4.76 (CI: 1.39, 20.75), p = 0.021]), fathers' educational level (aOR = 4.34 [CI: 1.38, 15.09], p = 0.015), large families (aOR = 2.55 [CI: 1.40, 4.79], p = 0.003), and peer and colleague introduction of psychoactive substances (aOR = 12.22 [CI: 3.16-63.48], p = 0.001). This study has shown a high level of psychoactive substance use among adolescent commercial tricycle operators within the Tamale metropolis, occurring within a context of poverty, hazardous child labor, and weak enforcement of child protection laws. Policy actions should include strict enforcement of the Children's Act, regulation of adolescent involvement in commercial transport, and coordinated interventions involving the Department of Children, Ghana Education Service, and National Road Safety Authority.
Type 2 Diabetes (T2D) is a chronic condition requiring lifelong personalized management to prevent disease progression and complications. Mobile health applications like dibi can substantially support patients in daily disease management. This study analyzes user demographics, self-reported treatment characteristics, and early feature use among users of the dibi digital diabetes companion app to better understand app uptake and feature use, advance personalized care and facilitate predictive healthcare strategies. Of 5,744 dibi users, 2,422 (42.2%) provided consent and completed registration (date: 15.12.24). Users were included if they had active consent and were aged ≥18 years, resulting in 2,262 users for the main analysis. Users with missing or invalid entries were excluded from respective parameter-specific analysis. Among all included users, 1,253 (55.3%) defined at least one medication plan with overall 1,039 unique medications, and 514 (22.7%) users utilized the adherence feature at least once to track medication intake. Of the included users, 89.4% were patients with T2D, mostly male (57.5%), aged 56-65 years (33.5%) and recently diagnosed (0-1 year since self-reported diagnosis). Female T2D users appeared to be distributed toward younger age groups than male users and more often chose lifestyle changes only during onboarding. Most T2D users (36.9%) reported either treatment with oral antidiabetics (OAD) only or lifestyle modifications alone (17.1%). Of the T2D patients who used medication plans, the majority (57.0%) reported using only OADs, while 7.2% reported only non-diabetes medications. More escalated treatment regimens were observed with longer disease duration. While 87.5% confirmed medication intake at least once, 29.6% used the adherence feature only once. This analysis demonstrates that the dibi app reached a predominantly T2D user population in Germany. It provides insights into treatment patterns and patient reported lifestyle changes. The dibi cohort reflects trends seen in other studies, representing real-world disease management. These findings indicate that inclusion of medical questionnaires, and clinical metrics, such as HbA1c, will deepen our understanding of the disease and enable treatment-lifestyle correlations.
As the global demand for soil pollution control becomes increasingly urgent, innovative material application strategies are essential for remediating soils co-contaminated with chromium (Cr) and cadmium (Cd) by efficiently immobilizing heavy metal ions and promoting their stabilization. This study evaluated humic acid-loaded nanoscale zero-valent iron (nZVI@HA) for the remediation of Cr- and Cd-contaminated soil. The remediation performance and underlying mechanisms of nZVI@HA were systematically investigated through soil incubation experiments, metal speciation analysis, high-throughput sequencing, and quantitative real-time PCR. nZVI@HA achieved immobilization efficiencies of 58.19% for available Cr and 35.84% for available Cd. On day 50 of remediation, exchangeable Cr and Cd were markedly transformed into residual, Fe-Mn oxide-bound, and carbonate-bound fractions, substantially reducing their mobility and bioavailability. Concurrently, nZVI@HA alleviated Cr(VI) and Cd(II) stress, increased microbial community diversity, and improved soil fertility. The enrichment of taxa associated with ChrA, CzcA, and NitR suggests that nZVI@HA may enhance microbial resistance to Cr(VI) and Cd(II), potentially contributing to Cr and Cd immobilization. The immobilization mechanisms primarily involved chemical reduction, adsorption, ion exchange, and microbially mediated processes. The synergistic effects between nZVI@HA and soil microorganisms make nZVI@HA an efficient and environmentally benign remediation material, providing an effective strategy for treating soils co-contaminated with heavy metals.
Epilepsy affects about 50 million people worldwide, with stigma and misconceptions that contribute to social exclusion and psychological distress, sometimes more disabling than the physical symptoms. In Syria, epilepsy awareness among university students remains under-studied, despite their role as future healthcare providers and community leaders. This study assessed epilepsy-related knowledge, attitudes, and first-aid practices among Syrian university students and identified associated factors. A cross-sectional study was conducted including 1005 students from 24 Syrian universities using a structured questionnaire that covered demographics, epilepsy knowledge, first aid, and attitudes. Reliability was assessed using Cronbach's alpha. Data were analyzed using descriptive statistics, t-tests, ANOVA, and logistic regression, with statistical significance set at p < 0.05. High epilepsy knowledge was found in 59% of students, but misconceptions persisted. Although 63% showed good first-aid knowledge, harmful practices were common. Attitudes were mixed, with 56% expressing positive views but significant reluctance toward close personal and professional relationships, potentially leading to social isolation, discrimination in employment and education, and reduced opportunities for marriage and community integration. Syrian students show moderate knowledge but persistent stigma and unsafe practices. Targeted education and first-aid training are needed to reduce misconceptions and improve support for people with epilepsy.
Type 2 diabetes is a major public health challenge that can often be prevented through lifestyle interventions. Artificial intelligence (AI) is increasingly used for risk prediction, behavioral coaching, and individualized prevention, offering scalability and low-intensity interventions. However, AI also raises ethical and regulatory concerns, especially for patient-facing tools. Limited evidence compares public comfort with physician use versus patient use for diabetes prevention. We analyzed data from a 2025 national survey conducted through the NORC AmeriSpeak Panel, a probability-based sample of 1,939 respondents. Participants evaluated two hypothetical AI use cases for diabetes prevention: Physician use of AI and patient use of AI-chatbot. Paired t-tests compared comfort across use cases. Weighted univariable and multivariable logistic regression models identified predictors of comfort. Participants reported significantly greater comfort with physician use of AI than with patient use of an AI-chatbot for diabetes prevention (p < 0.001). Comfort across both cases was strongly associated with belief that AI benefits population health (patient-AI: OR = 3.67, p < 0.001; physician-AI: OR = 3.86, p < 0.001), trust in health system AI use (patient-AI: OR = 1.47, p < 0.001, physician-AI: OR = 1.72, p < 0.001), and physician confidence in AI reliability (patient-AI: OR = 1.31, P = 0.003; physician-AI: OR = 1.30, p < 0.001). Comfort with physician use of AI was lower among Black/African American participants than White participants (OR = 0.51, p < 0.001), while women reported lower comfort with patient use of AI compared than men (OR = 0.71, p = 0.032). Public comfort with AI for diabetes prevention appears higher when integrated with professional oversight. Trust in clinicians, health systems, and AI reliability may be central to acceptance. Differences across demographic groups highlight the importance of equity-focused, physician-led implementation, transparent communication, and inclusive trust-building strategies for ethical AI adoption.
Family coping is integral to chronic heart failure management, yet systematic research on how families collectively cope remains limited. This study aimed to identify distinct family coping patterns among CHF patients and examine their associations with sociodemographic factors, fatigue, and family functioning. This cross-sectional study was conducted in China. Patients with chronic heart failure were recruited through convenience sampling from tertiary hospitals in Yunnan Province, southwestern China. Participants completed questionnaires assessing sociodemographic characteristics, family coping (FCS-CHF), fatigue (CChFS), and family functioning (CGFFS). Latent profile analysis was conducted to identify distinct coping subgroups, followed by chi-square tests, t-tests, and binary logistic regression to examine the factors associated with the profile membership of these subgroups. A total of 536 patients were included in this study. Latent profile analysis identified two distinct coping patterns among families: constructive family coping (56.7%) and depleting family coping (43.3%). The constructive family coping pattern consistently scored higher across all six coping dimensions. Binary logistic regression revealed that family structure, family size, annual household income, and family functioning were significantly associated with the type of coping pattern adopted by families. Specifically, better family functioning (OR = 0.311, p < 0.001), a medium family size (3-6 persons; OR = 0.427, p = 0.004), and higher income (≥200,000 CNY; OR = 0.415, p = 0.025) were associated with a lower likelihood of depleting family coping. In contrast, a nuclear family structure was associated with a higher likelihood of depleting family coping (OR = 1.739, p = 0.048). This study provides empirical evidence of two distinct family coping profiles among patients with chronic heart failure in a rural resource-limited population. The findings highlight the need for tailored, family centered nursing interventions that address specific support needs, including comprehensive multicomponent support for depleting families and reinforcement of existing strengths for constructive families. Future research should extend these findings through multi-informant designs that capture both patient and family member perspectives and through replication in more diverse populations to establish their generalizability to other populations.
Postural transitions (PTs) are crucial daily movements often impaired in neurological conditions, impacting autonomy and fall risk. Wearable inertial measurement units (IMUs) enable objective assessment of PTs, but robust algorithms for automatic identification remain limited. This pilot study used Dynamic Time Warping (DTW), a time-series alignment method that is robust to temporal variations, to automatically identify PTs in healthy subjects (HS) and subjects with Parkinson's Disease (SwPD). For this purpose, 10 participants (5 HS and 5 SwPD) performed 5 postural transition tasks (sit-to-stand, stand-to-sit, supine-to-sit, sit-to-supine, and roll) using a sternum-mounted IMU. Reference PT patterns were generated from previously collected acceleration data representing optimally executed transitions. The DTW algorithm classified each detected candidate transition by minimizing its distance from predefined reference patterns. Classification performance indexes were statistically compared between groups. Within this pilot dataset, the DTW algorithm correctly identified 118/118 postural-transition signals in HS and 136/147 signals in SwPD. Misclassifications in SwPD primarily affected sit-to-stand (31%) and stand-to-sit (19%) transitions. These preliminary findings support the feasibility of a DTW-based approach for postural-transition identification, although larger independent validation studies are required. Future work should prioritize real-time deployment and larger validation cohorts.
Electrochemical water splitting is limited by sluggish oxygen evolution kinetics and high energy consumption. Thermo-electricity coupling, utilizing heat and electric fields, has become an effective strategy to break away from traditional thermal methods and improve electrocatalytic efficiency. This work summarizes the core mechanisms of thermo-electric coupled water splitting, including thermal suppression of charge disproportionation, thermal driven spin regulation and thermal strain engineering, while elucidated the thermal effects beyond mass transfer. Utilizing industrial or power waste heat as a low-cost heat source, this strategy improved energy efficiency and matched with renewable energy systems. Besides, the challenges and prospects are proposed for the practical development of high-efficiency thermo-electricity coupled hydrogen production.
Admission to the neonatal intensive care unit (NICU) places families in complex medical environments where parental agency, the perceived capacity to understand treatment, communicate effectively, participate in decisions, and maintain emotional well-being, is crucial for family-centered care. This study aimed to develop and psychometrically validate the NICU Parental Agency Scale (NICU-PAS), a brief instrument measuring this multidimensional construct. The 15-item NICU-PAS was developed through literature review, expert panel evaluation, and parent focus groups. A multicenter cross-sectional study was conducted across eight NICUs in Suzhou, China, recruiting 350 primary caregivers. Psychometric validation included exploratory and confirmatory factor analysis, internal consistency reliability, and assessment of convergent validity (with parental stress) and discriminant validity (with general health and coping). Subgroup analyses compared first-time and experienced parents. Confirmatory factor analysis supported the four-domain structure of Treatment Understanding, Communication, Decision Making Participation, and Emotional Well-being (χ2/df = 1.69, CFI = 0.972, RMSEA = 0.045). Internal consistency was good (Cronbach's α = 0.783-0.860 for domains; 0.839 for total scale). Convergent validity was demonstrated by a significant negative correlation with parental stress (r = -0.189, p < 0.001), while discriminant validity was supported by negligible associations with general health and coping (r < 0.05). Experienced parents reported higher agency than first-time parents in Communication (p = 0.013), Emotional Well-being (p = 0.005), and total scores (p = 0.002). The NICU-PAS demonstrates robust psychometric properties and provides a practical, domain-specific measure of parental agency suitable for research and clinical quality improvement in NICU settings.
Hypertensive disorders in pregnancy create major clinical and economic burdens on maternal health services, making evidence on efficient resource allocation and cost-effectiveness essential for treatment decisions. However, no clear consensus exists on the most cost-effective antihypertensive therapy in Iran. This study evaluated the cost-effectiveness of methyldopa, labetalol, and nifedipine in pregnant women in Iran. A decision-analytic model was created using a decision tree framework to compare the cost-effectiveness of labetalol with nifedipine and methyldopa. The model used data from published randomized clinical trials and relevant literature. A hypothetical group of pregnant women aged 18 and older, at least 28 weeks pregnant, and experiencing severe hypertension (systolic blood pressure of 160 mmHg or higher, or diastolic blood pressure of 110 mmHg or higher), was studied over 4 months. The analysis considered the viewpoint of healthcare payers. Costs were measured in US dollars, and health outcomes were represented in quality-adjusted life years (QALYs). A willingness-to-pay threshold of $18,261 per QALY was set. Deterministic and probabilistic sensitivity analyses, including 10,000 Monte Carlo simulations, were conducted to evaluate uncertainty. In the base-case analysis, nifedipine provided the highest QALYs (0.21), followed by labetalol (0.19) and methyldopa (0.16). The total costs were $607 for nifedipine, $500 for labetalol, and $847 for methyldopa. Labetalol was less expensive and more effective than methyldopa, making it the dominant choice. Compared to nifedipine, labetalol was not cost-effective at the specified willingness-to-pay threshold. The probabilistic sensitivity analysis revealed that labetalol was cost-effective in 70% of simulations against methyldopa and 29% against nifedipine. From the perspective of Iranian healthcare payers, labetalol is a cost-saving and dominant option compared to methyldopa. However, it is not cost-effective when compared to nifedipine at a willingness-to-pay threshold of $18,261 per QALY in managing hypertensive disorders during pregnancy.