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PubMed2026

Transcranial Alternating Current Stimulation at 40 Hz Improves Social Functioning in Children With Autism Spectrum Disorder: A Randomized Clinical Trial.

BACKGROUND: Autism spectrum disorder (ASD) lacks rapid and effective interventions for its core social difficulties. The right temporoparietal junction (rTPJ), a critical hub for social cognition, together with gamma band abnormalities implicated in ASD, provides a promising neuromodulation target. METHODS: In this randomized, double-blind, sham-controlled trial, 47 children with ASD (39 male; mean [SD] age = 8.79 [2.71] years) were assigned to receive either 21 sessions of 40-Hz high-definition transcranial alternating current stimulation (tACS) targeting the rTPJ (3 sessions/day for 7 days) or sham stimulation, with assessments conducted at baseline, postintervention (week 1), and a 3-week follow-up (week 4). The primary outcome was change in Ohio State University Autism Rating Scale-DSM-5 (OARS-5) total scores. Secondary outcomes included the Aberrant Behavior Checklist-Second Edition, Social Responsiveness Scale-Second Edition, and Short Sensory Profile. Eye-tracking metrics during Frith-Happé animations were exploratory measures of theory of mind (ToM)-related social cognitive processing. RESULTS: The active group demonstrated significant improvements in OARS-5 total scores at week 1 (mean difference = -1.13, 95% CI [-1.78 to -0.47], p < .001) and week 4 (mean difference = -1.47, 95% CI [-2.20 to -0.74], p < .001). Improvements in selected behavioral and sensory domains were observed. Average fixation duration during ToM animations showed a significant group × time interaction. No serious adverse events occurred. CONCLUSIONS: These findings suggest that 40-Hz tACS targeting the rTPJ may be associated with rapid improvements in ASD symptom severity, particularly social functioning, in children with ASD, while being well tolerated. Clinical significance requires further evaluation.

Humans↗

PubMed2026

Social Isolation and Loneliness Among Older Asian Immigrants Through the Lens of Sense of Coherence: Systematic Review of Qualitative Studies.

AIM: To explore the meaning older Asian immigrants attribute to social isolation and loneliness, their management strategies, utilisation of resources and impact on health. DESIGN: Systematic review of qualitative studies. DATA SOURCES: AgeLine, CINAHL, MEDLINE, ProQuest, PsycINFO, Scopus, and Web of Science databases were searched in September 2024. METHODS: Inclusion criteria: participants were Asian immigrants to Western countries aged 65 and over, community-living and experiencing social isolation and loneliness. Antonovsky's Sense of Coherence was used to frame the thematic analysis. RESULTS: Ten papers were included and analysed deductively using elements of the sense of coherence framework: • Comprehensibility: Social isolation and loneliness are viewed as multifaceted, influenced by cultural and environmental dislocation, language barriers, intergenerational conflicts, deteriorating health and mobility, and socioeconomic challenges. • Manageability: included engaging in culture-specific community programs, family and ethnic community support and living within ethnic enclaves mitigated isolation and loneliness. • Meaningfulness: Strong family ties, active community involvement, spirituality, volunteerism, and cultural practices fostered resilience. However, accepting the changing values of their new world, living independently, and carving their own niche provided meaning to their transformed reality. CONCLUSION: Older Asian immigrants experience social isolation and loneliness through a cultural lens, shaped by migration experiences, language barriers, and shifting family dynamics. Cultural roots, family ties, spirituality, community, acceptance, and independence enhance sense of coherence. Recognising the dynamic interplay between cultural identity, resilience, and adaptation is key to understanding their lived experience. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: This review informs culturally sensitive interventions, guiding healthcare, community services, and policy to support social participation, mitigate loneliness through ethno-specific activities, and improve the quality of life for aging immigrant populations in Western countries. REPORTING METHOD: The review was undertaken and reported using the PRISMA guidelines. PATIENT OR PUBLIC INVOLVEMENT: None. PROTOCOL REGISTRATION: PROSPERO (CRD42023425752).

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Mechanisms of impact of mental health peer support in high-, middle- and low-income settings: mediation analysis of the UPSIDES randomised controlled trial.

AIMS: While there is growing evidence for the effectiveness of peer support (PS) in improving psychosocial outcomes among individuals with severe mental health conditions, the mechanisms through which these effects occur remain insufficiently understood. This study examines whether social inclusion, hope and empowerment mediate the relationship between PS, personal recovery and health and social functioning. METHODS: Data were collected from 565 adults with severe mental health conditions who participated in the multicentre UPSIDES randomised controlled trial across six sites in Germany, Uganda, Tanzania, India and Israel. Participants in the intervention group received structured PS from trained peer workers over a 6- to 8-month period. Standardised, self-report measures of social inclusion, hope, empowerment and personal recovery, as well as clinician-rated health and social functioning, were administered at baseline, 4 months, end of intervention (8 months) and 12-month follow-up. Cross-lagged panel modelling was used to explore longitudinal associations and mediating pathways. RESULTS: The cross-lagged models showed strong autoregressive effects across all variables, indicating high temporal stability. There were no significant direct effects of PS on recovery or health and social functioning. However, mediation analysis identified significant indirect effects of PS on personal recovery via social inclusion (β = 0.114, 95% confidence interval [CI] [0.049, 0.194], P < 0.05) and hope (β = 0.037, 95% CI [0.001, 0.086], P < 0.05). Similar indirect effects were observed for health and social functioning (via social inclusion: β = -0.035, 95% CI [-0.064, -0.013]; via hope: β = -0.026, 95% CI [-0.052, -0.006]; both P < 0.05). CONCLUSIONS: Findings suggest that PS affects recovery-related outcomes primarily through intermediate mechanisms of enhanced hope and social inclusion. These results support theoretical models positing indirect pathways of change in PS interventions and highlight the value of targeting social and psychological domains when designing and implementing PS in mental health services. Individuals with lower baseline levels of hope and social inclusion may particularly benefit from PS.

Humans↗

PubMed2026

"Out of sync and overlooked" - Relationship between social jetlag and anxiety in adolescents: Systematic review and meta-analysis.

Anxiety is the most prevalent mental health difficulty in adolescence, a period characterised by a shift towards an eveningness chronotype that is not aligned with societal demands (i.e., school start times). Experiencing "social jetlag" (SJL), a discrepancy in weekday-weekend sleep timing, is proposed to be associated with increased anxiety. A PRISMA-compliant systematic review and meta-analysis was conducted to investigate the relationship between SJL and anxiety in adolescents (age range: 12-18 years). Systematic searches were conducted in PsycINFO, Web of Science, Embase, PubMed, MEDLINE, and ProQuest Dissertations & Theses Global on 14th November 2024 to retrieve empirical studies analysing the relationship between SJL and anxiety in 12-18-year-olds. A multi-level random-effect meta-analysis was conducted in R to estimate the magnitude of the association between SJL and anxiety. After screening 2,138 records, 18 studies were included in the systematic review, with 12 included in the meta-analysis (235,526 participants in total) and six in a narrative review. A small association was found between increased SJL and more severe anxiety (Fisher's z = 0.0614, 95% CI [0.0268, 0.0961], p = 0.0011). These findings highlight the importance of addressing behavioural strategies targeting healthy regular sleep as a tool to improve mental health in adolescence.

Adolescent↗

PubMed2026

Post-Breakup Instagram Surveillance: Attachment Style, Personality Traits, and Breakup Distress as Predictors.

The end of a romantic relationship is one of the most emotionally challenging life events. Social media platforms such as Instagram enable users to monitor an ex-partner, a behavior known as Interpersonal Electronic Surveillance (IES), which may complicate coping. This study examined associations with retrospectively reported IES on Instagram during the first 3 weeks post-breakup, focusing on attachment, personality, and breakup-related emotional distress. Previous studies suggest that higher anxious attachment and emotional distress are related to increased monitoring behaviors on Facebook. The present research extends this approach to Instagram, a popular platform among Generation Z, and additionally examines personality factors. Data from N = 232 participants (aged 18-27 years; 84 percent women), who had experienced a breakup within the past year and followed their ex-partner on Instagram, were collected using a cross-sectional online questionnaire. The survey included standardized measures and self-constructed items. Hierarchical regression analyses including breakup-related variables, mediation analyses, and independent-samples t-tests were conducted. Due to extremely low internal consistency, Agreeableness was excluded from inferential analyses. The analyses indicated that Extraversion was the only personality trait directly associated with increased IES. Attachment styles showed no direct associations after emotional distress was included in the model. Emotional distress emerged as the most consistent factor associated with IES, showing patterns consistent with indirect associations involving Neuroticism and anxious attachment, suggesting a central role of emotional distress in post-breakup surveillance behavior. These findings highlight digital monitoring as a potentially maladaptive coping strategy and underscore the importance of addressing social media use in post-breakup adjustment.

Humans↗

PubMed2026

Early-stage trajectories of social-occupational functioning and long-term functional outcome prediction in early psychosis: A 12-year follow-up of the randomized controlled trial on extended early intervention.

BACKGROUND: Functional impairment in psychosis often persists despite symptomatic remission. There is a paucity of research examining early-course psychosocial functioning trajectories, and none has been conducted to examine relationship between the trajectories and prospective long-term functional outcomes in early psychosis sample. METHODS: We conducted 12-year follow-up of a randomized controlled trial on extended early intervention for first-episode psychosis to identify early-course social-occupational functioning trajectories and their baseline predictors and associations with 12-year outcomes. Participants who completed Social and Occupational Functioning Scale (SOFAS) scores at three or more timepoints between baseline and 3-year follow-up were included in the study. Premorbid adjustment, illness characteristics, symptom severity, functioning, and treatment profiles were assessed. Latent growth mixture modeling was employed to derive early-course social-occupational functioning trajectories based on SOFAS scores over 3-year follow-up. RESULTS: A total of 148 participants were included in this study, with 106 patients having completed the 12-year follow-up. Our results identified four distinct trajectories, including persistently-good class, gradually-improved class, suboptimal-stable class, and persistently-poor class. Patients in persistently-poor class had more severe negative symptoms at baseline compared to patients in persistently-good class. Patients with persistently-poor trajectory had worse long-term outcomes than those with other classes in the majority of functional measures at 12-year follow-up. CONCLUSIONS: The majority of patients were classified in early-stage suboptimal or poor functional trajectories. Above one-fourth of the participants exhibited persistently-poor social-occupational functioning trajectory, which predicted worse functional outcomes at 12-year follow-up. These findings highlighted the importance of tracking functional changes during the initial years of illness.

Humans↗

PubMed2026

Artificial intelligence enabled social robotic interventions (PARO) in Australian dementia care: A systematic review and meta-analysis.

BACKGROUND: Although there is a growing body of research indicating that Personal Robot/Social Robot could be used in various aspects of care for individuals with dementia, little is known about how well these types of interventions work in an actual hospital setting in Australia. AIMS & OBJECTIVES: The objective of the present systematic review and meta-analysis is to assess the effectiveness of PARO-based socially assistive robotic intervention in terms of its effectiveness outcomes towards the reduction of dementia-related behavioural and psychological symptoms in Australian based healthcare settings. METHODS: A systematic search was conducted across five electronic databases, including MEDLINE (PubMed), EMBASE, CINAHL, PsycINFO, and the Cochrane Library, to identify randomised controlled trials (RCTs) investigating PARO-based socially assistive robotic interventions for dementia in Australian healthcare settings. This review was registered with PROSPERO (CRD420251251916) and followed the PRISMA 2020 guidelines. In addition, the Cochrane Risk of Bias tool (RoB 2) was used to evaluate the risk of bias across all studies. Pooled standardised mean differences (SMD) with 95 % confidence intervals (CI) were calculated for agitation, anxiety, and depression. Heterogeneity across studies was evaluated using the I2 statistic. RESULTS: Six RCTs involving 1444 participants were identified for inclusion in this review. AI-enabled socially assistive robotic interventions, specifically the PARO therapeutic robot, significantly reduced agitation and anxiety when compared to standard treatment or control conditions. The pooled analysis showed that agitation [SMD = -0.44 (95 % CI: -0.70, -0.18) p = 0.0008] and anxiety [SMD = -0.59 (95 % CI: -0.91, -0.27) p = 0.0003] were reduced significantly, while the decrease in depression [SMD = -0.44 (95 % CI: -0.95, -0.07) p = 0.09] scores was non-significant among dementia patients receiving PARO-based socially assistive robotic interventions as compared to the control. The overall risk of bias across all six studies was considered low to moderate. CONCLUSION: PARO-based socially assistive robotic interventions may provide preliminary evidence of effectiveness in reducing agitation and anxiety in individuals with dementia in Australian healthcare, but the evidence regarding the reduction of depression remains unclear. Therefore, additional high-quality trials with consistent methodology and extended follow-up will be necessary to determine both the short-term and long-term clinical efficacy and practicality of implementing these interventions into practice.

Humans↗

PubMed2026

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↗

PubMed2026

Affective reactivity to a remote computer-based Trier Social Stress Test during a planned quit attempt: associations with short-term cigarette smoking lapse risk.

BACKGROUND: The Trier Social Stress Test (TSST) elicits affective responses and has been linked to smoking behavior. However, its remote use during a planned quit attempt-when stress reactivity may influence early lapse-remains understudied. OBJECTIVE: To quantify affective reactivity to a remotely administered TSST on a planned quit date following overnight abstinence and evaluate associations with cigarette use and lapse within 48 h. METHODS: This secondary analysis used data from a randomized controlled trial of adult smokers completing a remotely administered TSST following overnight nicotine abstinence. Urge, anxiety, and stress were assessed using visual analog scales and summarized using area under the curve (AUC) metrics. Smoking outcomes included cigarette count and lapse within 48 h. Associations were estimated using generalized estimating equations. RESULTS: In adjusted models, anxiety reactivity-but not urge or stress-was associated with cigarette count and lapse. Greater anxiety exposure (AUCtot) and change above baseline (AUCab) were associated with higher cigarette count (IRR=1.0004, 95%CI:1.0002-1.001, p=.002; IRR=1.01, 95%CI: 1.002-1.01, p=.002) and increased odds of lapse (OR=1.001, 95%CI: 1.0001-1.002, p=.03; OR=1.02, 95%CI: 1.001-1.03, p=.03). Effect sizes were small. CONCLUSIONS: Anxiety reactivity under nicotine deprivation was associated with increased cigarette use and lapse 48 h post quit attempt, suggesting individual differences in stress-evoked anxiety may serve as a behavioral marker for early lapse. Remote TSST administration appears feasible for eliciting affective responses on a quit date.

Humans↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

PubMed2026

Development, feasibility, acceptability, and preliminary impact of NutriSOS®: A behavioral mobile app to promote sustainable diets.

The primary objective of this study was to describe the development of the NutriSOS® app and to evaluate its feasibility and acceptability for its use in the NutriSOS® Randomized Controlled Trial (RCT) to promote sustainable diets. A secondary objective was to explore preliminary changes in dietary and physical activity behaviors and environmental impact following app use. The NutriSOS® app integrates personalized dietary advice, educational content, self-monitoring, and social interaction features. A single-arm, pre-post pilot study was conducted in 37 young Mexican adults over four weeks. Feasibility, acceptability, quality, and usability were assessed using online surveys, alongside exploratory changes in dietary and physical activity behaviors, environmental indicators, and their association with perceived behavioral determinants. Feasibility and acceptability were high overall, with favorable responses reaching up to 100% in key components such as the nutritional guide and learning modules, and above 90% for messaging, registration, and design. Greater variability was observed in some sections, particularly the 24-h recall (41-86%). Reductions in red and processed meat and ultra-processed food consumption were observed (from 3 to 1 times/week, p < 0.01), with ∼60% decreases in their related environmental footprints (p < 0.01) and favorable self-reported behavioral determinants (p < 0.0001). Physical activity type and intensity changed (p < 0.05). These findings support NutriSOS® as a feasible and acceptable tool, while highlighting areas for refinement, particularly those related to the time and effort required for data entry, prior to its implementation in the NutriSOS® RCT, in which its effectiveness will be formally evaluated.

Humans↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Your story, your brand: A career core competency for nurses.

Intentional management of one's professional story, or narrative discipline , is now a core competency and responsibility for nurses at all career stages. Workforce mobility, interdisciplinary collaboration, broadening career opportunities, and the expansion of digital platforms have elevated the importance of how nurses are perceived by colleagues, organizations, and the public. Increasingly, a nurse's professional story and digital footprint influence professional opportunities, career advancement, and even employment decisions.Many companies invest heavily in brand management to build trust and emotional connection with the people they serve. Importantly, narrative discipline also contributes directly to healthy work environments by reinforcing trust, role clarity, respect, and psychological safety. Drawing from leadership practice, emerging research on nurses' social media use, healthy work environment principles, and guidance from national nurse leadership organizations, this article outlines how nurses can align personal, professional, and enterprise identities; use language deliberately; and engage with discipline and integrity. Practical strategies are provided to help nurses move from passive narrative formation to intentional storytelling that supports career development, workforce engagement, organizational trust, and the sustainability of the nursing profession.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Financial incentives and social messaging for repeat SARS-CoV-2 antibody testing among the underserved: A randomized trial.

Financial incentives may influence health behavior beyond their expected monetary value, and their effectiveness may depend on how the behavior is framed. Behavioral theories of decision making suggest that individuals may value protection against small-stakes losses more than expected utility predicts, while theories of family-centered health behavior suggest that messages emphasizing benefits to family members may strengthen participation in preventive health activities. We tested these ideas in a 2×2 factorial randomized trial involving 625 households recruited from a Federally Qualified Health Center serving low-income Latino/Hispanic communities. Participants completed repeat SARS-CoV-2 antibody testing. The trial crossed two messaging strategies (Family vs. Personal) with two incentive structures (Loss Protection vs. Lottery) that offered equivalent expected monetary value. Family Messaging emphasized protecting one's family from COVID-19, whereas Personal Messaging emphasized protecting oneself. Loss Protection allowed participants to secure an at-risk reward through repeat testing, whereas the Lottery condition offered a chance of a large reward. Repeat testing was approximately 8 percentage points higher under Family Messaging and 7 percentage points higher under Loss Protection. Baseline trust in medical providers, financial barriers to vaccination, and risk aversion were associated with initial testing, whereas household characteristics were not associated with repeat testing. Incentive design may matter beyond expected monetary value and that framing health behaviors in terms of family welfare may increase participation in repeated healthy activities. Broadly, the results support behavioral theories emphasizing loss aversion, anticipated regret, and family-centered motivations, and suggest practical approaches for improving engagement in repeat health behaviors. CLINICALTRIALS.GOV REGISTRATION NUMBER:: NCT01901624.

Adult↗

PubMed2026

Understanding Suicide through Coroners' Narratives: implications for primary care from a mixed‑methods study of 157 Coroners' reports.

Suicide is a major public health concern, and general practice is often a recent point of contact before death. While mental illness is well recognised, the broader social and contextual factors influencing suicide risk remain under-reported in primary care and epidemiological research Aim To describe the demographic, clinical, and psychosocial characteristics of individuals who died by suicide, integrating coronial quantitative data with qualitative narrative accounts to identify implications for primary/ secondary care and public health. Design and setting Explanatory sequential mixed‑methods study of 157 consecutive deaths by suicide recorded by coroners (2018-19) across five English local authorities. Method Demographic, clinical, and social data were extracted from coroners' records and summarised descriptively. Narrative case summaries were coded and analysed thematically to identify contextual, relational, and service factors preceding death. Results Of 157 individuals: 79% were male; 65% lived in the most deprived IMD quintile; 85% had a diagnosed mental health condition; 62% had a long‑term physical illness; 41% had a previous suicide attempt. About half consulted a GP in the preceding three months; mental health featured in about half of those consultations. Common stressors were relationship breakdown (37.2%), housing instability (22.1%), and work pressures (18.2%). Seven interlinked themes were identified: Mental health; Alcohol/Substance use, Physical health; Social connectedness; Life course trauma, Socioeconomic and Structural Vulnerability; Healthcare access. Service transitions were key vulnerability points Conclusion Coroners' records offer important insights into the complex circumstances preceding suicide and highlight opportunities for GPs to recognise intersectional complexity and support integrated, cross-sector suicide prevention approaches.

General Practice↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗