SEARCH · Search PubMed

Results for “construct validity”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

342 records · Page 6Linked to original sources

PubMed2026

Identifying stakeholder behaviors for competency-based pharmacy education: A stage 1 behavior change wheel analysis.

INTRODUCTION/OBJECTIVES: Competency-Based Pharmacy Education (CBPE) is a strategic priority for preparing graduates to meet evolving healthcare needs. However, efforts to implement CBPE can stall due to behavioral challenges among faculty, administrators, preceptors, and learners. This study aimed to apply Stage 1 of the Behavior Change Wheel (BCW) to identify stakeholder-specific behaviors and associated determinants needed to implement the five core components of CBPE. METHODS: A multi-method approach grounded in the BCW, the Capability, Opportunity, Motivation - Behavior (COM-B) model, and the Theoretical Domains Framework (TDF) was used. Data were gathered through (1) targeted literature review; (2) structured focus groups with competency-based education experts and pharmacy education stakeholders; and (3) an iterative consensus process. Behaviors were mapped to the five CBPE components: (1) defined competencies, (2) developmental progression, (3) tailored instruction, (4) authentic experiential learning, and (5) programmatic assessment, and then mapped to COM-B and TDF constructs. RESULTS: Over fifty stakeholder-specific behaviors were identified and specified across the CBPE framework. This revealed shared barriers such as limited instructional design knowledge (psychological capability), insufficient assessment of infrastructure (physical opportunity), and misaligned professional identity (reflective motivation). Key TDF domains included knowledge, environmental context, beliefs about capabilities, and professional roles. The behavioral problem statements, specifications, and determinants were identified to support future intervention planning. CONCLUSION: This Stage 1 analysis provides a behaviorally grounded foundation for CBPE implementation by identifying stakeholder behaviors and conditions that enable change. These findings will inform the development of readiness-to-change assessments and targeted interventions (BCW Stages 2 and 3), supporting scalable and sustainable CBPE transformation in pharmacy education.

Education, Pharmacy↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Acceptability of alcohol-based hand rub during neonatal care in rural Ugandan households: a qualitative study nested within the BabyGel trial.

BACKGROUND: Poor access to water, sanitation and hygiene exacerbates the spread of infections among newborns. The BabyGel cluster randomised trial assessed the effectiveness of a community-level alcohol-based hand rub with a training component in reducing infection or death rates among newborns in Uganda. OBJECTIVE: Nested in the BabyGel trial, this study investigated the acceptability of the BabyGel intervention among mothers and household members in Eastern Uganda, using the Theoretical Framework of Acceptability. METHODS: In 2022, we conducted individual semistructured interviews with mothers, and group interviews with mothers and other household members, all recruited from intervention-arm clusters. Thematic analysis combined inductive and deductive coding, structured by the framework's seven constructs: affective attitude, burden, ethicality, intervention coherence, opportunity costs, perceived effectiveness and self-efficacy. RESULTS: Twenty mothers and 14 household members participated in 10 individual and 10 small group interviews. Participants found the intervention generally acceptable, appreciating its convenience and perceived protection against infection. Many described hand hygiene changes that persisted beyond the intervention period. However, concerns related to its harmful effects, spiritual beliefs and social tensions with visitors occasionally influenced its use. CONCLUSION: Alcohol-based hand rub combined with a training component was generally well accepted, driven by perceived health and practical benefits, although concerns related to safety, belief systems and social dynamics remained. Because data collection coincided with the COVID-19 pandemic, a period of heightened hygiene awareness, the acceptability observed could differ in nonpandemic periods. Culturally adapted education, community engagement and reinforcement of correct use may enhance acceptability and sustainability in similar settings.

Humans↗

PubMed2026

Stigma, discrimination-related events, and determinants among adult people living with systemic lupus erythematosus (SLE): Systematic review and indicator-level meta-analysis.

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease with 0.4 million new cases diagnosed annually. With its wide variety of visible and invisible manifestations, people living with SLE report being exposed to stigmatization, which impacts their personal and professional lives. However, the current literature is unclear on whether healthcare management teams assess this concern during follow-up. This study aims to synthesize existing evidence on the prevalence and determinants of stigma among people living with SLE.MethodsThis systematic review and meta-analysis gathered evidence from observational studies identified from three databases on 16 July 2025. Dual independent screening, data extraction, and risk-of-bias assessment (using the Newcastle-Ottawa Scale) were performed. Results were synthesized using descriptive statistics, narrative synthesis, and indicator-level meta-analyses.ResultsWithin the past two decades, 11 studies comprising 2254 people living with SLE reported and measured stigma- and discrimination-related events using various scales. Stigma was found to be prevalent across its three constructs: interpersonal, perceived, and intrapersonal stigma. This review demonstrated that people living with SLE reported a moderate overall burden of stigma (34.71 [95% CI 26.15, 43.27]), with average stigma scores indicating psychological impact. Additionally, nearly one in two persons (46% [95% CI 28-66%]) experienced at least one form of stigma or discrimination, most commonly social isolation and unfair treatment. Mental health associations were correlated with higher stigma burden.ConclusionThis review demonstrates that stigma and discrimination are not just social challenges but also critical determinants of health. With cautious interpretation, pooled evidence reveals a consistent high prevalence of stigma and discrimination, which act as "toxic" stressors, creating a vicious cycle with psychological stress and psychiatric manifestations and disease activity. There is an urgent clinical need to move beyond a mere biological approach to disease assessment and management and to begin screening for the "invisible" burden of invalidation and discrimination.

Humans↗

PubMed2026

Meta-analysis and pharmacoeconomic study of rasagiline versus selegiline in the treatment of Parkinson's disease.

OBJECTIVE: Given the persistent absence of direct head-to-head trials, this study aimed to evaluate the comparative efficacy, safety, and cost-effectiveness of rasagiline versus selegiline as early-stage monotherapy for Parkinson's disease (PD), informing clinical selection and healthcare policies in China. METHODS: A systematic search of PubMed, Embase, and the Cochrane Library identified randomized controlled trials (RCTs) up to April 2026. Focusing on short-term outcomes (10-16 weeks), an adjusted indirect treatment comparison (ITC) using placebo as a common anchor evaluated symptom improvement (UPDRS total scores) and adverse event (AE) incidence. For economic evaluation, a 2-year Markov model was constructed from a Chinese healthcare-system perspective. The incremental cost-effectiveness ratio (ICER) was calculated alongside robust sensitivity analyses. RESULTS: Ten RCTs (rasagiline: 6; selegiline: 4) were included. The ITC revealed no statistically significant differences between rasagiline and selegiline in short-term symptomatic relief (Mean Difference = -0.82, 95% CI [-2.08, 0.44], p = 0.203) or AE risk (Odds Ratio = 0.83, 95% CI [0.50, 1.38], p = 0.475). The overall evidence certainty was rated as moderate. Economically, the base-case simulation indicated rasagiline yielded a marginal benefit of 0.0088 QALYs over selegiline but incurred an additional 17,111.10 Yuan. This resulted in an ICER of 1,951,505.55 Yuan/QALY, substantially exceeding the conventional willingness-to-pay threshold. CONCLUSION: Supported by moderate-certainty evidence, rasagiline and selegiline provide comparable short-term efficacy and safety for early-stage PD monotherapy. However, at its current pricing, rasagiline is not cost-effective. Significant price reductions or definitive proof of long-term superiority are required to justify its economic value.

Humans↗

PubMed2026

Predictors of Efficacy Maintenance After Vunakizumab Discontinuation in Patients With Moderate-to-Severe Plaque Psoriasis: A Post Hoc Analysis of a Randomized Controlled Trial.

BACKGROUND: Efficacy cannot be maintained in some psoriasis patients after biological discontinuation. This study aimed to explore predictors of efficacy maintenance after vunakizumab discontinuation in patients with moderate-to-severe plaque psoriasis. METHODS: This post hoc analysis used data from a phase III trial (NCT04839016); 291 patients with moderate-to-severe plaque psoriasis who achieved 100% improvement in Psoriasis Area and Severity Index (PASI) score at Week 52 were enrolled. Efficacy maintenance was defined as patients who maintained PASI 90 or PASI 100 after 20 weeks of vunakizumab discontinuation. RESULTS: There were 44.7% and 72.5% of patients with PASI 100 and PASI 90 maintenance, respectively. In the multivariate logistic regression model, body mass index (BMI) (odds ratio [OR] = 0.922, p = 0.024) and treatment interruption (OR = 0.550, p = 0.020) were independently associated with a lower possibility of PASI 100 maintenance; however, the association of family history of psoriasis and the first time of PASI 100 achievement with PASI 100 maintenance did not achieve statistical significance. Duration of psoriasis (OR = 0.972, p = 0.049) and treatment interruption (OR = 0.257, p < 0.001) were independently associated with a lower possibility of PASI 90 maintenance. Two nomograms for predicting PASI 90 and PASI 100 maintenance were constructed based on the multivariate models, which disclosed good calibration performance. CONCLUSIONS: PASI 90 and PASI 100 maintenance rates are 72.5% and 44.7% after 20 weeks of vunakizumab discontinuation in patients with moderate-to-severe plaque psoriasis. BMI, treatment interruption, and duration of psoriasis predict a lower possibility of efficacy maintenance after vunakizumab discontinuation.

Humans↗

PubMed2026

Smartphone apps for obesity management: A systematic review using self-determination theory.

BACKGROUND: While bariatric surgery and pharmacotherapy are effective treatments for obesity, ongoing supportive care remains a challenge. Smartphone applications (apps) may assist with symptom management, but their effectiveness and practical use in obesity treatment is unclear. This review evaluated the effectiveness, acceptability, and feasibility of these apps in supporting individuals following obesity treatment. To better understand how these apps may promote sustained engagement and behaviour change, their design was analysed using Self-Determination Theory (SDT). METHODS: A systematic search was conducted across MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, SCOPUS, and CENTRAL databases. Eligible studies included randomised and non-randomised interventions involving adults (≥18 years) with obesity (BMI ≥ 30 kg/m2) who had undergone bariatric surgery or pharmacotherapy. Interventions had to include an app designed to support post-treatment symptom management. Findings were synthesised narratively, and app features were mapped to SDT constructs of autonomy, competence, and relatedness. RESULTS: Five studies (three RCTs, two cohort studies) involving 1,133 participants were included (female: 78 %; median age: 47.63 years). Most apps targeted post-bariatric surgery care; only one focused on pharmacotherapy. Common features included tracking, reminders, and education, supporting autonomy and competence. Relatedness features such as communication and peer support were least represented. Two studies reported improvements in weight-related outcomes and one in medication adherence. Effects on quality of life, self-efficacy, and healthcare utilisation were not significant. Patient satisfaction was reported in one study, with 95 % expressing positive feedback, though formal assessments of feasibility and acceptability were limited. CONCLUSION: Smartphone apps show potential to support obesity management, particularly after bariatric surgery. While some evidence suggests benefits for weight loss and adherence outcomes, the limited studies and variability of reporting prevent conclusive observations in other outcomes. Future app development should integrate behavioural theory to address psychological needs, nutritional risks and promote holistic self-management beyond weight control.

Female↗

PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Integrative quantum and systems biology of cancer: From molecular fluctuations to ecological outcomes.

This review treats cancer as a multiscale adaptive system, asks what the framework must predict to be worth adopting, and separates at each scale what the evidence establishes from what is proposed. It is an expert narrative synthesis, not a systematic review, and states the limits of that design. Proton transfer and tautomeric shifts contribute to spontaneous mispairing but do not license claims of directed or non-random mutation: replication timing, three-dimensional chromatin organization, sequence context and known mutagenic processes explain most mutational heterogeneity, leaving any quantum contribution as a residual against that baseline. The Waddington quasi-potential is bounded: outside detailed balance the dynamics are not gradient-derivable and require a probability-flux term. Hysteresis, rate-limited bimodality and return to state after perturbation distinguish an attractor from a transcriptomic cluster. Single-cell karyotype and live-imaging evidence supports whole-genome doubling as an unstable intermediate of heterogeneous origin and context-dependent consequence, not a uniform adaptive strategy. Systems and synthetic biology, virtual cells and digital twins are assessed against benchmarks, not promise. Tissue-scale ecology is reported with the spatial measurements now quantifying it, including evidence that stromal niche construction is not uniformly tumor-supporting. RNA modification is a layer in its own right, showing that the interpretation of a regulatory signal, not its magnitude, is biologically decisive. A dedicated section states the framework's commitments, the observable and evidence at each scale, and what would falsify them, asking what this adds to somatic mutation theory with clonal evolution and plasticity.

Neoplasms↗

PubMed2026

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (≥54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans↗

PubMed2026

Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

BACKGROUND: Despite advances in arthroscopic repair and biologic augmentation, surgical indication for meniscal tears remains heterogeneous. No standardized framework currently integrates biomechanical, clinical, and biological determinants to guide repair versus resection. PURPOSE: To develop a quantitative decision model-the Meniscal Preservation Score (MPS)-that unifies biomechanical and biological evidence to stratify reparability potential and standardize treatment selection in meniscal surgery. METHODS: A systematic evidence synthesis conducted in accordance with PRISMA 2020 reporting standards of studies published from 2000 to 2025 in PubMed, Embase, and Scopus identified key determinants of meniscal healing. Five consistent predictors-patient age, vascularity, tear morphology, associated pathology, and activity profile-were weighted through a two-round modified Delphi consensus among ten experienced knee surgeons. The resulting 0-9-point MPS was incorporated into a stepwise decision tree linking lesion morphology, biological context, and surgical strategy. Conceptual validation used 50 simulated cases and a retrospective cohort of 45 patients to test agreement between algorithm recommendations and expert surgical decisions. RESULTS: The MPS achieved 86% concordance with expert judgment in simulation and 84% agreement in clinical validation. In this retrospective exploratory cohort, cases in which surgical management was concordant with MPS recommendations demonstrated higher mean IKDC scores at 24 months and lower observed reoperation rates. These findings should be interpreted as associative rather than causal, as treatment allocation was not controlled and discordant cases may have represented inherently more complex pathology. CONCLUSION: The MPS represents an evidence-informed decision-support framework designed to systematize reparability assessment. While exploratory analyses suggest structural coherence with expert reasoning, prospective implementation and external validation are required before clinical adoption as a predictive tool. LEVEL OF EVIDENCE: conceptual model with exploratory validation.

Humans↗

PubMed2026

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n = 38, 74%). Hierarchical clustering (n = 20) and K-means clustering (n = 14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.

Humans↗

PubMed2026

A pragmatic randomized controlled trial of self-directed online writing interventions for posttraumatic stress symptoms in a real-world digital setting.

Background: Public health and other large-scale crises, such as the COVID-19 pandemic, have intensified the global mental health burden, creating unprecedented demand for accessible interventions for posttraumatic stress symptoms (PTSS).Objective: We evaluated the feasibility and effectiveness of two self-directed online writing interventions embedded within China's WeChat ecosystem during the COVID-19 pandemic through a pragmatic randomised controlled trial.Methods: Between December 2021 and August 2022, 1,526 adults were screened for PTSS via a Tencent Medinfo Mini-Program. Eligible participants (n = 211) were randomised to Guided Narrative Technique-Writing (GNT-W, n = 100) or Expressive Writing (EW, n = 111). Both interventions comprised three self-directed daily writing sessions delivered entirely online without human support. Primary outcome was PTSD symptom severity (PTSD Checklist-Short), assessed at baseline, post-intervention, 2-week, and 1-month follow-ups.Results: While initial engagement followed typical digital health patterns (64.5% overall attrition), participants who initiated treatment showed strong adherence (77% completion). Both interventions were associated with significant within-group reductions in PTSS severity (GNT-W: b = -0.43, p = .023, d = -0.43; EW: b = -0.60, p = .001, d = -0.58), with no significant between-group difference (group × time: b = 0.18, p = .48). GNT-W did not confer additional benefit over EW protocol on PTSS severity.Conclusions: Both self-directed writing interventions were associated with within-group reductions in PTSS; without an inactive control condition, however, these changes cannot be firmly attributed to the interventions. GNT-W showed no advantage over the simpler EW protocol. These findings offer preliminary support for embedding scalable, low-barrier writing interventions in widely used digital platforms.Chinese Clinical Trial Registry: ChiCTR2000034836.

Humans↗

PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗

PubMed2026

Experiences of stigma, bias, and communication challenges among pregnant healthcare workers: A systematic review of qualitative evidence.

BACKGROUND: Healthcare work environments are fraught with occupational hazards that can impact pregnant healthcare workers' health as well as patient care. Despite the feminization of healthcare globally, systematic discrimination against pregnant workers persists across diverse healthcare settings and cultural contexts. The intersection of stigma, bias, and communication challenges creates substantial barriers to career advancement and wellbeing. However, no systematic review has synthesized qualitative evidence on how these three constructs interact across healthcare professions and cultural contexts using an integrated theoretical framework. OBJECTIVE: To systematically review and synthesize qualitative evidence on experiences of stigma, bias, and communication challenges among pregnant healthcare workers across different healthcare settings and cultural contexts using an integrated theoretical framework. DESIGN: Systematic review of qualitative studies following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with thematic synthesis. DATA SOURCES: Seven databases were searched from inception to January 2026. REVIEW METHODS: Included qualitative studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist and synthesized through theory-guided thematic synthesis. Confidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach. RESULTS: Fourteen studies encompassing 1223 participants across 17 countries revealed four major themes: (1) professional identity stigma and workplace discrimination through systematic labeling and stereotyping; (2) gender-based institutional bias rooted in masculine organizational logic; (3) multilevel communication failures creating fear-based climates; and (4) individual and collective resistance strategies developed despite constraints. Occupational hazards specific to pregnancy included exposure to infectious diseases, imaging, physical tasks, cleaning products, patient violence, and medication administration. Support from coworkers and supervisors was identified as the most critical facilitator for avoiding hazards and making necessary modifications, while the desire to be 'supernurses' and fear of consequences emerged as significant barriers. These patterns were consistent across healthcare professions, settings, and cultural contexts, with specialty culture and healthcare system type moderating discrimination intensity. Confidence in core findings was rated high using GRADE-CERQual. CONCLUSIONS: Pregnant healthcare workers globally experience interconnected stigma, bias, and communication challenges that are systematically embedded within healthcare organizational structures. These challenges operate synergistically, requiring comprehensive multilevel interventions beyond policy compliance. Healthcare organizations must implement evidence-based strategies addressing stigma reduction, bias interruption, and communication transformation simultaneously to retain skilled workers and ensure quality patient care.

Female↗

PubMed2026

The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.

BACKGROUND: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. OBJECTIVE: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. METHODS: In October 2025, we conducted a 4 × 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. RESULTS: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention (P=.02) and greater trust (P=.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (β=0.087; P=.003), injunctive norms (β=0.078; P=.009), perceived susceptibility (β=0.051; P=.03), perceived benefits (β=0.253; P<.001), and trust (β=0.33; P<.001), and negatively associated with perceived severity (β=-0.047; P=.049) and privacy concerns (β=-0.11; P<.001). Perceived ease of use and self-efficacy were not significant predictors. CONCLUSIONS: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

Humans↗

PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗