Uncategorized - Relatix Health

May 25, 2026

Prevalence, Themes, and Partisan Differences in US State Legislator X Posts Mentioning Suicide: Content Analysis

Background: Suicide is a leading cause of death in the United States, and state policies can be effective tools to prevent suicide. State legislators are increasingly active on social media, communicating about their legislative priorities and signaling information about their knowledge and attitudes about issues. Objective: This study aimed to characterize US state legislators’ social media posts mentioning suicide on X (formerly Twitter) and explore differences in how Democrat and Republican legislators communicate about suicide. Methods: We used Quorum, a public affairs database, to identify all state legislator X posts mentioning suicide (N=1049) between December 1, 2023, and November 30, 2024. We developed a codebook and used content analysis to characterize posts and document the frequency of communication about suicide and themes related to causes, solutions, and consequences of suicide. We assessed concordance between the social media post language used and guidelines for reporting about suicide. We conducted univariate analysis and chi-square tests to assess differences in the content of posts between Democrat and Republican legislators. Differences in the frequency of posts about suicide were analyzed using 2-tailed tests. Results: Of 1049 posts identified, 849 (80.9%) were included in the final sample. The annual suicide post rate per 10,000 posts was 13.2 (0.1% of all posts) among Democrats and 7.4 (0.1% of all posts) among Republicans (=.09). Suicide related to a specific population was identified in 52.2% (443/849) of posts, with youth, veterans, firearm owners, and the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and more) population being identified most frequently. Causes of suicide were identified in 37.1% (315/849) of posts, with no significant difference between Democrats and Republicans. However, the types of causes identified varied, with Democrats more likely to identify lethal means (eg, firearms) as a cause of suicide than Republicans (115/573, 20.1% vs 20/172, 7.5%; <.001). About two-thirds (558/849, 65.7%) of posts identified at least one solution to prevent suicide, with Democrats more likely to identify a solution than Republicans (443/573, 77.3% vs 114/268, 42.5%; <.001). General awareness was the most frequent solution, while policy-specific solutions were present in only 23.3% (198/849) of posts. Collateral consequences of suicide were infrequently mentioned. Conclusions: This study found differences between Democrats and Republicans in their X posts about suicide and areas of misalignment with research evidence. When considered within the context of research on the epidemiology of suicide and evidence supporting suicide prevention policies, the study highlights the need to improve communication about suicide with state legislators and to encourage further collaboration with suicide prevention organizations and experts. Furthermore, given the differences observed, study findings suggest potential value in tailoring messages about suicide for legislators based on their political party.

May 25, 2026

Smartphone-Based Grading and Rehabilitation in Patients With Facial Palsy Using Computer Vision: Prospective Validation Study

Background: Peripheral facial palsy causes significant functional and psychosocial impairments, requiring precise assessment and patient engagement for effective rehabilitation. However, conventional clinician-graded scales (eg, House-Brackmann Scale, Sunnybrook Facial Grading System, and Stennert Index) are subjective and prone to interobserver variability, limiting their reliability for tracking recovery. Smartphone-based computer vision solutions offer objective, standardized facial movement grading, and interactive home-based training to improve adherence and outcomes. Objective: This pilot study evaluated a novel iOS smartphone app (Apple Inc.) for facial palsy management. The app uses the iPhone TrueDepth 3D camera and on-device computer vision to compute a Digital Facial Index (DFI) for objective facial movement analysis, and provides guided neuromuscular facial exercises with real-time biofeedback. The study aimed to validate DFI against standard clinical grading scales and assess patient-reported outcomes and usability. Methods: A 4-week single-arm pilot included 21 patients with unilateral facial palsy. Participants used the app at home for daily facial exercises and periodic self-assessments with DFI. Clinicians, blinded to DFI, rated facial function from standardized video exams at baseline and 4 weeks using the House-Brackmann Scale, the Sunnybrook Facial Grading System, and the Stennert Index. DFI concurrent validity was evaluated via correlation with these clinician scores. Patient-reported outcomes included pre- and postintervention Facial Disability Index (FDI) physical and social scores, the System Usability Scale, and a poststudy user feedback questionnaire. Results: During the study period, strong correlations were observed between DFI and conventional clinical scores. FDI physical and social showed significant functional improvement. Mean System Usability Scale was 88.3 (SD 15.4), indicating excellent usability, and participants reported high satisfaction, preferring the app over traditional paper-based exercises. Conclusions: The app’s DFI provided objective facial function grading that correlated well with standard clinical scales. Patients’ FDI scores improved significantly over 4 weeks. High usability and patient preference support the app’s feasibility for home-based rehabilitation. This digital approach is promising for facial palsy management, and controlled studies are needed to confirm efficacy and improve long-term engagement.
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May 25, 2026

Testing a Smartphone-Based Intervention Targeting Anxiety Sensitivity Among Women Presenting for Emergency Care After Sexual Assault: Pilot Randomized Controlled Trial

Background: Anxiety sensitivity (AS), defined as the fear of anxious arousal, is a promising therapeutic target for reducing posttraumatic stress disorder (PTSD) symptom development after trauma exposure. Initial research suggests that smartphone-based AS interventions may be acceptable to sexual assault survivors at risk for PTSD symptoms and effective for symptom reduction, but only small one-arm proof-of-concept studies have been conducted. Objective: The goal of this study was to extend prior proof-of-concept work by conducting a pilot randomized controlled trial. The aims were to evaluate intervention efficacy, AS and PTSD symptom change, the acceptability and credibility of a control intervention, and the feasibility of a larger randomized controlled trial. Methods: A total of 60 women with high AS presenting for emergency care after sexual assault were recruited and randomized to either the AS intervention or a control condition, and they were followed up with for over 6 months via remote self-report questionnaires. Results: The findings indicated that the study population is at risk and in need of intervention: 88.8% (40/45) and 80.6% (29/36) of women sexual assault survivors in the sample met the criteria for probable PTSD at 7 weeks and 6 months post assault, respectively. Most (16/27, 59.3%) individuals receiving the AS intervention who completed it rated it as acceptable (eg, 18/21, 85.7% reported that the treatment was helpful). Early within-group reductions were not statistically significant, but by month 6, statistically significant reductions in AS and PTSD symptoms were observed in both conditions. Recruitment and retention data supported the feasibility of the study design, although some suggestions were noted for future research (eg, improving intervention and ecological momentary assessment compliance). Conclusions: This pilot study replicated the proof of concept and acceptability of a novel smartphone-based intervention targeting AS delivered to women sexual assault survivors presenting to emergency care. Intervention completion was in line with or better than traditional therapy but remained a challenge. Our selected control condition demonstrated a larger effect than expected, and we were unable to track its initiation or completion, causing difficulty in drawing conclusions. Overall, the results highlight the need for additional research. Trial Registration: ClinicalTrials.gov NCT05305235; https://clinicaltrials.gov/ct2/show/NCT05305235
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May 25, 2026

An In-Hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting Based on Machine Learning: Cohort Study

Background: Ischemic heart disease remains the leading cause of death worldwide. Coronary artery bypass grafting (CABG) remains the primary surgical treatment for ischemic heart disease. There is currently a lack of highly accurate and widely applicable models for assessing the risk of postoperative mortality following CABG. Objective: This study aimed to develop and validate an in-hospital mortality risk prediction system for patients undergoing coronary artery bypass grafting (CABG) by using machine learning algorithms and to compare its performance with the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and Sino System for Coronary Operative Risk Evaluation (SinoSCORE). Methods: Between January 2017 and December 2020, 21,443 patients undergoing CABG in the Chinese Cardiac Surgery Registry were included. Patients were randomly divided into training (n=17,753) and test (n=3690) cohorts. We addressed class imbalance using the synthetic minority oversampling technique (SMOTE) and optimized hyperparameters via grid search. Fifteen machine learning algorithms were developed to predict in-hospital mortality. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration metrics (Brier score), and decision curve analysis, and was compared against EuroSCORE II and SinoSCORE. Results: A total of 21,443 patients were included. Overall, in-hospital mortality was 2.1% (n=450). The Extreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.850 in the training cohort and 0.782 in the independent test cohort (this cohort was independent and not involved in model construction). While EuroSCORE II showed an AUC of 0.722 and SinoSCORE showed an AUC of 0.726 in the test cohort, the XGBoost model demonstrated superior discrimination and calibration (P<.05). Conclusions: Our study developed and validated a machine learning–based risk prediction model for in-hospital mortality after CABG by using a large-scale Chinese multicenter registry. Among the algorithms tested, the XGBoost model demonstrated superior discrimination and calibration compared with the traditional EuroSCORE II and SinoSCORE, suggesting that locally calibrated models may better capture the risk profile of Chinese patients. The derived 7-variable web calculator may serve as an exploratory auxiliary tool to provide a preliminary reference for bedside risk stratification, though its direct impact on surgical decision-making requires further prospective validation. Future research should focus on independent test cohorts across diverse hospital tiers to ensure broad generalizability.

May 25, 2026

Cell Therapy Shows Promise for Treating Advanced Liver Disease

A type of cell therapy made up of macrophage cells can lower risk for death or need for a liver transplant in people with cirrhosis due to advanced liver disease.

The research, published in Cell Stem Cell, was a long term follow-up study of patients recruited into an earlier Phase I/II trial and showed a 27.5% drop in risk for death or liver transplant in those who received the therapy versus usual care over up to four years of follow up.

“Cirrhosis represents a major global health burden, with substantial and rising morbidity and mortality,” write lead author Stuart Forbes, PhD, a professor at the University of Edinburgh and scientific founder of Resolution Therapeutics—the biotech developing the therapy, and colleagues.

“Apart from liver transplantation, therapeutic options for end-stage disease remain limited to supportive care and management of complications, underscoring the urgent need for innovative approaches to halt or reverse disease progression.”

The treatment used in the study involves extracting monocyte cells from the blood of patients and converting them to macrophages in the lab before injecting the macrophages into the liver to trigger repair.

The therapy is designed to overcome the tissue damage and scarring seen in people with advanced cirrhosis where the liver loses its normal ability to regenerate itself. Macrophages are able to break down scar tissue, reduce inflammation and encourage the growth of new and healthy cells in the liver.

In the initial MATCH Phase I/II study, 26 patients had macrophage therapy and 24 received standard care. After the follow up period, 30.8% of those in the treatment group had died or needed a transplant versus 58.3% of the standard care group. No serious adverse events were linked to the cell therapy.

“Liver disease is a major cause of death… Although we can use liver transplantation as a rescue treatment for a proportion of people who have advanced liver disease, this is restricted by a lack of suitable donor organs… There is therefore a desperate need for alternative treatments for patients with advanced liver disease,” commented Forbes, in a press statement.

“We hope this type of approach could one day add to our treatment choices for patients with advanced liver disease, reducing the need for liver transplants.”

Resolution has another similar Phase I/II trial to Match underway called Emerald that is building on the initial results but using macrophages modified to be more effective at tackling liver damage.

The company is also running an observational trial called Opal, a study in patients with cirrhosis and hepatic decompensation, to characterize disease trajectories and help design endpoints and inclusion criteria for future interventional trials.

The post Cell Therapy Shows Promise for Treating Advanced Liver Disease appeared first on Inside Precision Medicine.

May 25, 2026

STAT+: Eli Lilly says Verve’s gene editor lowers cholesterol levels in early study

Eli Lilly said Monday that a high dose of its gene-editing therapy reduced cholesterol levels by 62% in participants in a clinical trial, an early but encouraging test of whether a one-time treatment may one day help people seeking to lower their LDL, or “bad,” cholesterol.

Lilly acquired the therapy, VERVE-102, in its $1 billion buyout of Verve Therapeutics last year. Executives tout it as a potential treatment to broadly prevent heart disease, the world’s leading killer, as many patients struggle to stay on existing, more conventional medicines for reducing cholesterol levels.

There were no treatment-related serious adverse events in the Phase 1 study — a notable finding, given that Verve had to shelve its first candidate due to safety concerns.

Continue to STAT+ to read the full story…

May 25, 2026

Machine learning for Alzheimer’s disease progression under extreme class imbalance

BackgroundTimely identification of individuals at risk for Alzheimer’s disease (AD) progression remains a major clinical challenge. Traditional cognitive assessments provide limited prognostic insight, while many machine learning (ML) models rely on costly biomarkers or poorly interpretable algorithms that limit clinical scalability. This study evaluated whether widely available baseline demographic, clinical, and cognitive measures could support short-term progression prediction using interpretable ML methods under extreme class imbalance.MethodsWe analyzed 3,240 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of whom 2,423 had valid 24-month follow-up data. The primary outcome was strict unidirectional diagnostic worsening within 24 months (13 events; 0.5%). Baseline demographic, clinical, and cognitive variables were used to train XGBoost and logistic regression models under natural class imbalance using stratified k-fold cross-validation with out-of-fold predictions. Model performance was evaluated using AUROC, area under the precision-recall curve (AUPRC), calibration analyses, and bootstrap confidence intervals. Sensitivity analyses evaluated cost-sensitive learning, threshold optimization, and alternative imputation strategies (KNN and MICE). Longitudinal mixed-effects modeling was conducted separately to characterize cognitive decline and was not used as input to the predictive models. SHAP (Shapley Additive Explanations) quantified feature contributions.ResultsUnder natural class imbalance, XGBoost achieved AUROC = 0.912 and AUPRC = 0.051, while logistic regression achieved AUROC = 0.787 and AUPRC = 0.038. Although discrimination exceeded baseline prevalence, precision remained low and threshold optimization produced substantial false-positive burdens, limiting immediate clinical applicability. Cost-sensitive learning did not materially improve performance. MICE imputation produced results comparable to median imputation, whereas KNN imputation reduced performance. SHAP analyses identified baseline cognitive severity, functional measures, and diagnostic status as dominant predictors. Mixed-effects modeling confirmed significant cognitive decline over time (β = −0.027 points/month, p < 0.001).ConclusionAccessible baseline clinical and cognitive variables contain measurable but limited predictive signal for short-term AD progression under extreme event scarcity. These findings should be interpreted as an early-stage proof-of-concept rather than a clinically deployable decision-support tool. External validation remains necessary before clinical translation.

May 25, 2026

Workload analysis of pilot steep turn maneuvers using SR20 aircraft and EEG data

ObjectiveTo compare left vs. right steep turns in terms of workload-related neurophysiological signatures using electroencephalogram (EEG) and machine learning.MethodsThirty-seven flight cadets performed one left and one right steep turn in an SR20 desktop flight simulator while a 32-channel EEG (Emotiv EPOC Flex 32) was recorded. From 2-s sliding windows (50% overlap), 800 features per window were extracted (time-, frequency-, and non-linear domains). Six classifiers (XGBoost, LightGBM, GB, SVM, LR, and Linear SVC) were evaluated using cross-subject nested cross-validation with variance-ranked feature subsets (20%, 40%, 60%, 80%, and 100%), and an additional 10% subset was assessed to identify a more parsimonious feature set.Results: Objective EEG/MLLightGBM demonstrated superior performance across all feature proportions.SubjectiveNASA-TLX was significantly higher in right turns than in left turns (5.55 ± 1.13 vs. 4.98 ± 1.06, p < 0.001, and Cohen’s d = 0.52). Post-hoc interpretation combined RF-based importance and variance-ranked top-feature analysis, showing convergent frontal/frontocentral dominance with complementary utility definitions (predictive contribution vs. signal dispersion). Physiologically, left turns were associated with relatively higher high-frequency activity/complexity, whereas right turns showed relatively stronger theta/alpha-related patterns.InterpretationThese findings support MWL-associated directional neurophysiological differences in steep turns and identify candidate EEG markers for lightweight real-time workload monitoring, facilitating optimized flight training and enhanced aviation safety.

May 25, 2026

The impact of face-to-face social exclusion on university students’ interpersonal cooperation behavior: a hyperscanning study based on fNIRS

BackgroundMost studies on social exclusion adopt virtual paradigms focusing on unilateral responses, while neglecting real-world face-to-face interaction and its neural basis. Functional near-infrared spectroscopy (fNIRS) hyperscanning allows recording of interpersonal neural synchronization (INS) during dyadic interaction, providing a novel approach for investigating interpersonal cooperation.MethodsThis study recruited 24 dyads of college students randomly assigned to social exclusion and inclusion groups. Using fNIRS hyperscanning combined with a face-to-face rejection paradigm and the Prisoner’s Dilemma task, we examined subjective experience, behavior, and INS.Results(1) The exclusion group reported lower intimacy, trust, belonging need and state self-esteem than the inclusion group. (2) Only defection decision reaction time was faster in the exclusion group, with no group differences in overall cooperation and defection rates, indicating exclusion primarily accelerates defection. (3) INS showed channel-specific differences: the exclusion group had weaker right orbitofrontal INS during cooperation and stronger left dorsolateral prefrontal INS during defection. (4) Cooperation reaction time negatively correlated with trust, while defection efficiency positively correlated with left frontopolar INS. (5) State self-esteem partially mediated the link between social exclusion and defection reaction time.ConclusionFrom an integrated psychological–behavioral–neural perspective, this study confirms that face-to-face social exclusion accelerates defection decisions by impairing subjective interpersonal experience, altering prefrontal INS, and through the mediating effect of subjective feelings. These findings provide empirical evidence for understanding the mechanisms underlying campus social exclusion.

May 25, 2026

Long-term psychological and functional outcomes after hepatitis C eradication with direct-acting antivirals: an 80-month follow-up study

IntroductionDirect-acting antivirals (DAAs) have dramatically changed hepatitis C virus (HCV) treatment, by achieving high virological cure rates and a reduced percentage of adverse events compared with previous treatments. Despite this, long-term psychiatric and quality-of-life (QoL) outcomes after viral eradication remain insufficiently understood. This study provides an 80-month follow-up of a previously evaluated cohort.MethodsOf the original 62 patients, 24 (38.7%) were reassessed approximately 80 months after DAA initiation. Psychopathological symptoms (HAM-D, HAM-A, SCL-90-R), coping strategies (COPE), and QoL (SF-36) were evaluated and compared with baseline (T0). Patients were stratified by psychiatric history (Group P vs. Group NP). Non-parametric tests and exploratory correlations and regressions were performed.ResultsIn Group P, depressive and anxiety symptoms significantly improved from T0 to follow-up (HAM-D: 16 vs. 3, p < 0.01; HAM-A: 15 vs. 4, p < 0.01), with additional reductions in SCL-90-R Interpersonal Sensitivity, Paranoid Ideation, and Psychoticism (all p < 0.05). Group NP showed stable psychological profiles, reflecting the maintenance of improvements already observed at T1, with significant reductions in HAM-D (7 vs. 4, p < 0.01) and HAM-A (8 vs. 4, p < 0.001). Both groups demonstrated a significant decline in SF-36 Physical Component Summary (Group P: p < 0.05; Group NP: p < 0.01). No between-group differences were detected at follow-up. Avoidant coping and psychiatric history were significant negative predictors of long-term anxiety change.ConclusionsSeven years after HCV eradication, psychological well-being remains stable or improved, while physical QoL declines. DAAs demonstrate sustained long-term psychiatric safety, underscoring the need for integrated medical–psychological follow-up in HCV survivors.