Toxicological Sciences

Objective White smoke inhalation injury (WSI) causes severe acute lung damage with no specific therapy currently available. Sphingolipid metabolism is implicated in pulmonary inflammation, but its transcriptional regulatory landscape in WSI remains unexplored. This study aimed to identify key sphingolipid metabolism related genes and evaluate their regulatory roles and therapeutic potential in WSI. Methods We established a rat model of WSI and performed integrated bulk RNA sequencing, weighted gene coexpression network analysis (WGCNA), and single-cell RNA sequencing (scRNAseq) to screen for differentially expressed sphingolipid metabolism-related genes (DESRGs). Protein-protein interaction (PPI) network with four centrality algorithms was used to prioritize hub genes. In silico gene knockout and molecular docking were conducted to assess regulatory functions and identify potential drug candidates. Results We identified 22 DESRGs that were predominantly enriched in DNA replication and cell cycle pathways rather than canonical sphingolipid metabolic processes. PPI consensus prioritized three hub genes--Top2a, Ttk, and Ccna2--with Top2a exhibiting the highest expression in epithelial cells and significant downregulation after smoke exposure. ScRNAseq revealed immune cell infiltration and epithelial differentiation trajectories. Virtual knockout showed that Top2a depletion affected the largest transcriptomic fraction (~0.4%) and was enriched in lysosome biogenesis, innate immunity, phagocytosis, and lipid catabolism. Molecular docking identified thalidomide as a high affinity ligand for Top2a (Vina score: -8.5 kcal/mol). Conclusion Our multiomics integrative framework identifies Top2a as a central regulatory hub linking sphingolipid associated inflammation to epithelial responses in WSI, and nominates thalidomide as a potential drug repurposing candidate. These findings provide prioritized targets for future translational investigation.

Importance: Immune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood. Objective: To define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway. Design and Setting: Cross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026. Participants: Among 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male. Exposures: ICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure. Main Outcomes and Measures: Reporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset. Results: Of 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways. Conclusions and Relevance: ICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.

Gabapentin (GBP) and pregabalin (PGB) are widely used gabapentinoids. Previously, we have demonstrated, for the first time, that GBP and PGB modulate the nociceptive response to noxious heat in C. elegans at an optimal concentration. In the current study, we use C. elegans and paired thermal nociception assays with direct internal drug concentration measurements to characterize the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of both compounds. Neither drug altered baseline mobility or quadrant preference, confirming that behavioral effects reflected genuine antinociceptive action. Both GBP and PGB produced dose- and time-dependent reductions in thermal avoidance, with 500 uM exposures generating a biphasic, V-shaped time course in which suppression of thermal sensitivity deepened before partially reversing. This partial reversal occurred later with PGB than with GBP. Internal concentrations confirmed dose-dependent absorption and retention for both drugs, yet at 500 uM, internal drug levels remained elevated through 360 min even as behavioral avoidance recovered, indicating that the recovery limb reflects active counter-regulation rather than passive clearance, consistent with previously reported transcriptional and proteomic signatures. Exposure-response profiles were notably flat, suggesting a saturable pharmacodynamic ceiling. Molecular modeling revealed conserved electronic pharmacophores supporting shared alpha-2-delta engagement, alongside shape-descriptor differences that may contribute to divergent absorption kinetics. These findings position C. elegans as a valuable model for dissecting gabapentinoid PK/PD relationships. Beyond mechanistic insight, these findings support the continued investigation of C. elegans as a screening platform whose validation could help address the 3R (Replacement, Reduction, Refinement) principles guiding animal research.

Background: Population-based biomonitoring of contemporary-use pesticides remains limited in the United States, particularly in rural agricultural regions, and few studies have repeated measurements within the same individuals over time. Methods: We analyzed 28 urinary pesticide-related biomarkers among 600 adults from the population-based Survey of the Health of Wisconsin with archived urine collected during 2008-2016; 296 participants provided repeat urine and updated exposure information in 2025. Detection frequencies, co-detection, and within-person detection patterns were characterized. Generalized estimating equations were used for stacked, repeated-measures analyses of factors associated with detection of aminomethylphosphonic acid (AMPA), glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D), and any of these three. Prospective-only analyses evaluated more detailed agricultural and recent exposure measures. Results: Glyphosate, AMPA, and 2,4-D were detected in 7.7%, 6.2%, and 4.3% of retrospective specimens and 5.4%, 3.1%, and 4.1% of prospective specimens, respectively. Co-detection and persistent detection across the 9 to 17-year interval was rare. In repeated-measures models, greater fruit and vegetable intake, older age, and male sex were associated with higher 2,4-D detection. Lower household income was associated with lower AMPA detection, while afternoon/evening collection was associated with higher AMPA detection. In prospective analyses, working on field-crop agricultural land showed the strongest agricultural associations, particularly for 2,4-D and detection of any of the three pesticides. Associations were not seen with self-reported conventional versus organic produce consumption. Conclusions: Urinary pesticide detections were generally infrequent in this Wisconsin population. Diet and direct agricultural activities may be more informative exposure pathways than residing near cropland or private well drinking-water characteristics.

Background: Children spend more than 90% of their time indoors, and early childhood education settings (ECEs) are an understudied, high-occupant-density indoor microenvironment where exposure to volatile organic compounds, particulate matter, and other toxicants has been documented. Limited knowledge exists on ECE-specific exposures affecting young children and how they compare to exposures in the home. Methods: This prospective, repeated-measures pilot study targeted enrollment of 44 preschool-aged children and 8 ECE staff across two geographically and sociodemographically distinct ECEs in metropolitan Atlanta, Georgia. Paired silicone wristbands, one home-designated and one ECE-designated, were exchanged between settings across three consecutive days and nights beginning at enrollment to characterize microenvironment-specific exposure. A single spot urine sample was also collected from each child. Continuous indoor air quality monitoring was conducted in two classrooms per site. Caregivers and ECE staff completed structured questionnaires assessing home and ECE environmental characteristics, child respiratory risk, and protocol feasibility and acceptability. Feasibility was evaluated using eight pre-specified indicators spanning recruitment and enrollment, wristband wear duration and loss by microenvironment, urine sample collection completeness, and survey completion by instrument and respondent group. Conclusion: This pilot will establish feasibility and acceptability parameters for a paired, multi-matrix silicone wristband protocol across home and ECE microenvironments. Findings will inform the design, sample size, and power calculations for a subsequent study testing indoor air interventions and pediatric respiratory outcomes in ECEs. Feasibility outcomes are reported in a companion manuscript.

Background: Serial glial fibrillary acidic protein (GFAP) trajectories have become an important framework for contextualizing evolving secondary-injury pathophysiology after moderate-to-severe traumatic brain injury (msTBI). However, total GFAP pools release and clearance signals that may be less useful for longitudinal bedside decisions than a proteoform-resolved assay. We compared total GFAP with neoGFAP, defined here as calpain-generated GFAP proteoforms intended to index active astroglial proteolysis during the subacute phase. Methods: We analyzed 651 serial serum samples from 95 msTBI patients from a previously described single-site cohort. Total GFAP and neoGFAP were measured on the same MSD platform from 6 to 240 hours after injury. Early (6 to 72 h) and late (96 to 240 h) windows, data-derived tertiles, and serial trajectory summaries were calculated directly from serial samples. Models were benchmarked against age plus admission post-resuscitation Glasgow Coma Scale (GCS) and the admission IMPACT extended risk score using five-fold stratified cross-validation. Outcomes were unfavorable outcome (GOSE 1 to 4), less-than-good recovery (GOSE 1 to 6), Disability Rating Scale (DRS) [≥]15, mortality, and neuroimaging worsening at 6 months. Results: The cohort contributed 95 serial biomarker profiles, with 90 participants evaluable for 6-month GOSE and 89 for DRS. Unfavorable outcome occurred in 57/90 (63.3%), and less-than-good recovery in 79/90 (87.8%). For unfavorable outcome, IMPACT plus early neoGFAP reached AUROC 0.85 versus 0.84 for IMPACT plus early total GFAP and 0.81 for IMPACT alone. For less-than-good recovery, IMPACT plus late neoGFAP achieved AUROC 0.90 versus 0.84 for late total GFAP and 0.82 for IMPACT alone. Secondary analyses for DRS, mortality, and neuroimaging worsening showed smaller differences. Conclusions: In this retrospective analysis, neoGFAP provided clearer incremental value than total GFAP for recovery-oriented monitoring, especially when late-window reassessment of patients who remained at risk for less-than-good recovery was required. Results support prospective testing of neoGFAP as a pathophysiology-informed adjunct to serial bedside decision making, repeat-assessment thresholds, and recovery stratification.

Abstract Objective: Glaucoma is a chronic disorder that impairs ocular health and may exacerbate ocular surface disease leading to tear film instability, dry eye symptoms and decreased quality of life. This study compared changes in tear quantity among glaucoma subjects living with and without diabetes mellitus, attending an eye clinic in Nigeria. Methods: A comparative cross sectional research design was used. 157 subjects which comprised 74 glaucoma subjects living with diabetes mellitus and 83 glaucoma subjects living without diabetes mellitus participated in the study. Tear quantity assessment included the Schirmer I test and tear meniscus height (TMH) measurement. Descriptive statistics, independent samples t-test and Chi-square test were used to examine the data at 0.05 level of significance. Results: Glaucoma subjects living with diabetes mellitus showed substantially decreased tear production (11.4 +/- 6.8 mm) compared with glaucoma subjects living without diabetes mellitus (19.6 +/- 9.6 mm; p < 0.001). Tear meniscus height in glaucoma subjects living with diabetes mellitus (0.8 +/- 0.3 mm) was significantly greater than in subjects living without diabetes mellitus (0.7 +/- 0.3 mm; p = 0.034). Conclusion: Diabetes mellitus dramatically deteriorates the ocular surface function in glaucoma subjects by decreasing tear production, altering the tear meniscus height and increasing the severity of ocular surface symptoms. Routine glaucoma care, especially in patients with diabetes mellitus, should include a full ocular surface evaluation including Schirmer I test, TBUT, TMH, and OSDI assessment to allow early detection and management of ocular surface disease, better treatment adherence, and improved visual outcomes. Keywords: Glaucoma, Diabetes Mellitus, Tear production, Tear Meniscus Height, Ocular Surface Disease.

Background: Consumer use of AI chatbots for health advice is rising, yet triage safety relative to established services remains unclear. Australia's Healthdirect, a government-backed symptom checker with 2.4 million uses in FY2024-25, remains unevaluated against frontier large language models (LLMs), and whether premium subscriptions improve triage safety remains unexplored. This study compared the triage accuracy and safety of Healthdirect against six LLM configurations across ChatGPT, Claude, and Gemini, assessed whether paid subscriptions improve triage safety, and characterised each system's error patterns. Methods: Forty-five clinical vignettes from the Semigran et al. benchmark spanning emergency, non-emergent, and self-care categories (15 each) were evaluated across seven systems. Healthdirect was tested following a seven-rule interaction protocol. LLMs were evaluated using first-person patient-language prompts under free-tier and paid-tier conditions. Outcomes were triage accuracy, emergency sensitivity, under-triage, and critical misses, analysed using Cochran's Q, Bonferroni-corrected McNemar tests, Cohen's kappa, and Wilson intervals. Findings: Triage accuracy differed significantly (Cochran's Q = 36.79, p < 0.001). Healthdirect achieved 48.9% accuracy (95% CI 35.0% to 63.0%; kappa = 0.233) versus 73.3% to 86.7% for LLMs (kappa = 0.600 to 0.800). Healthdirect operated under conservative interactive defaults while LLMs received complete information in a single prompt, which may have disadvantaged Healthdirect. Emergency sensitivity was 46.7% versus 80.0% to 86.7% for LLMs. Healthdirect produced two critical misses; no LLM produced any across 270 evaluations (95% CI 0% to 1.4%). When LLMs undertriaged, they recommended GP care rather than self-care. No tier differences were significant (all p > 0.05), and most systems over-triaged self-care cases. Interpretation: Frontier LLMs demonstrated higher triage accuracy and safer error profiles than Healthdirect. All LLMs avoided critical misses; Healthdirect did not. Premium subscriptions did not significantly improve triage safety. These findings support clinical governance decisions about whether LLMs warrant formal evaluation alongside government-backed symptom checkers.

Artificial intelligence applied to routine electrocardiograms (ECGs) has largely focused on detecting existing disease or predicting individual cardiovascular outcomes. Whether ECGs can support prediction of multiple future diseases across organ systems remains unclear. We developed ECG-RISK, a multitask survival model for 67 incident three-character ICD-10 endpoints using ECG waveforms, demographic characteristics and routinely collected laboratory data from 86,673 MIMIC-IV patients. Discrimination was highest for heart, brain, kidney and lung endpoints, with organ-level C-indices ranging from 0.796 to 0.825, whereas liver and pancreatic endpoints showed lower discrimination. The ECG-only model achieved strong discrimination across most endpoints, whereas the incremental improvement gained by incorporating ECG and laboratory inputs beyond demographic information varied substantially across endpoints. Across the nine exploratory aggregated outcomes, Kaplan Meier curves showed clear separation among model-score tertiles. Discrimination was highest for dementia (C-index, 0.891) and heart failure (C-index, 0.857). These findings support the feasibility of ECG-based longitudinal risk prediction across multiple diseases. External validation and competing-risk analyses are required to assess generalisability and clinical utility.

Objectives To evaluate whether access to a certified large language model (LLM)-based clinical decision support system improves physician diagnostic performance in rheumatology compared with conventional diagnostic resources alone. Methods In this multicentre, open-label, randomised controlled trial, 82 physicians from seven hospitals in two countries were randomised 1:1 to conventional diagnostic resources plus Prof. Valmed or conventional resources alone. Participants assessed three rheumatology vignettes before and after assistance. The primary outcome was top-1 diagnostic accuracy. Secondary outcomes included top-3 accuracy, diagnostic reasoning, confidence, case-processing time and perceived support quality. Results Top-1 accuracy increased from 22.2% to 33.3% in the intervention group and from 23.3% to 35.0% in the control group, with no between-group difference in improvement (adjusted OR 0.99, 95% CI 0.45 to 2.19; p=0.979). Differences in top-3 accuracy, diagnostic reasoning and confidence were also not significant. Assisted case-processing time was substantially shorter with LLM support (94 vs 206 s; adjusted mean difference -112 s, 95% CI -141 to -83; p<0.001). Information timeliness and perceived diagnostic support quality were rated significantly higher in the intervention group. Exploratory analyses showed persistent overconfidence and substantial AI over-reliance. Conclusions Certified LLM-based diagnostic support did not improve diagnostic accuracy compared with conventional resources, but substantially reduced case-processing time and improved perceived support quality. These findings suggest potential workflow benefits while highlighting overconfidence and over-reliance as important safety considerations.

Purpose: To evaluate the safety, procedural performance, sample recovery, and surgeon preference of an ophthalmic needle designed specifically for anterior chamber (AC) paracentesis. Methods: In this multicenter study, AC paracentesis was performed in clinic and operating-room settings using a 32-gauge x 4-mm needle with low dead space. The procedure was evaluated using a standardized physician survey. Prespecified outcomes included procedure-related adverse events (primary outcome), needle entry and handling, aspiration and sample recovery, comparative performance versus a 30-gauge needle, and physician preference for future use. Results: A total of 110 needle uses by eight surgeons were included. No ocular complications occurred, including lens or iris injury, hyphema, AC collapse, wound leak, hypotony, infection, or retinal complication, and no procedure required needle exchange or conversion to another device. Two technical events without ocular sequelae were noted, in which needle entry was partial thickness and did not reach the AC (1.8%; exact 95% CI, 0.2%-6.4%). Physicians rated needle entry, handling and sample recovery as good or excellent. Compared with a 30-gauge needle, the study needle was rated as at least comparable across all assessed domains. All surgeons rated it better or much better for intra-procedural safety and preferred it for future AC taps. Conclusions and Relevance: This short, 32-gauge low-dead-space ophthalmic needle demonstrated a favorable safety profile and was preferred over a 30-gauge needle by all surgeons. As aqueous humor liquid biopsy expands in clinical diagnostics and trials, an ophthalmic-specific needle design may help improve the consistency and safety of aqueous humor collection for molecular analysis and broader clinical use. Keywords: Anterior chamber paracentesis; Aqueous humor; Liquid biopsy; Low dead space; Ophthalmic needle

Introduction: Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is a common endocrine disorder affecting women of reproductive age. Besides reproductive and metabolic disturbances, PMOS negatively impacts psychological well-being and quality of life. Despite available treatment options, there remains a need for safe and effective therapies that improve both clinical symptoms and fertility outcomes. Aim: To compare the efficacy of VAMHA and MYRHA tablet combination therapy with standard non-hormonal therapy in restoring regular menstruation. Secondary objectives included assessment of ovulation, menstrual symptoms, polycystic ovarian morphology, hormonal and metabolic parameters, anthropometric measures, and skin manifestations. Study Design: Open-label, randomized, multicentre, prospective comparative clinical study. Methods: Seventy-one women with PMOS were randomized to Group A (n=37) or Group B (n=34). Group A received VAMHA and MYRHA tablets (2 tablets each), while Group B received Metformin 500 mg plus Myoinositol 600 mg (1 tablet), twice daily for 180 days. Data were recorded in Case Report Forms. Statistical Analysis: Continuous variables were summarized using mean and standard deviation, while categorical variables were expressed as frequencies and percentages. Appropriate statistical tests, including Chi-square, were used. A p-value [&le;]0.05 was considered significant. Results: Significantly more participants in Group A achieved regular menstrual cycles than Group B (31 vs. 22; p<0.05). Ovulation occurred in 16 participants in Group A compared with 6 in Group B (p<0.05). Both groups showed significant improvement in menstrual irregularity and related symptoms. Significant reductions in Anti-Mullerian Hormone (AMH), fasting insulin, and body mass index (BMI) were observed in both groups (p<0.05). Resolution of polycystic ovarian morphology occurred in 13 participants (38.23%) in Group A and 10 (33.33%) in Group B. Both treatments were well tolerated with no major safety concerns. Conclusions: VAMHA and MYRHA combination therapy was superior to standard non-hormonal therapy in improving menstrual regularity and ovulation. It also produced favourable metabolic, hormonal, and ultrasonographic outcomes, suggesting its potential as a safe and effective option for comprehensive PMOS management and fertility enhancement.

Background & Aims: Fatigue is a central symptom of chronic liver disease (CLD), substantially impacting health-related quality of life (HRQoL). This study aimed to further understand CLD symptomatology, including fatigue, and its impact on HRQoL from a patient perspective. Methods: Abbott Global Assessment of Patients unmet needs (aGAP) was a multinational, cross-sectional survey in adults with compensated CLD in China, India and Mexico, conducted between July and November 2024. Adult participants who self-reported that they had physician-diagnosed CLD and were experiencing fatigue completed a quantitative survey to assess symptom burden and included three HRQoL patient-reported outcome (PRO) questionnaires (Patient-Reported Outcomes Measurement Information System [PROMIS]-29+2, Work Productivity and Activity Impairment - Specific Health Problem version 2.0 [WPAI: SHP], Multidimensional Fatigue Inventory [MFI]). Results: Overall, 505 participants (China: 200; Mexico: 105; India: 200) completed the study. Participants reported that their CLD-related fatigue sometimes, often or always affected their self-esteem/confidence (45.1%) and ability to maintain or acquire new employment (38.6%). Most participants reported moderate (51.3%) or serious (26.9%) fatigue, with 33.5% experiencing fatigue every day or almost every day. Many participants felt their social life was negatively impacted by their fatigue (47.3%) and that there were related financial difficulties (53.9%). Use of validated PRO tools demonstrated severe fatigue (MFI: overall mean [SD] 13.9 [3.4] general fatigue and 13.4 [3.6] physical fatigue) as well as substantial levels of work and activity impairment (WPAI: SHP overall mean [SD] 53.0 [26.4]) and high levels of anxiety, pain interference, depression and sleep interference (PROMIS T-scores [&ge;]54). Conclusions: Fatigue has a substantial impact on HRQoL among adults with CLD across several countries, highlighting a global unmet need for targeted interventions to effectively identify and manage the condition.

Importance: Evidence on GLP-1/GIP therapies is largely derived from trials enrolling selected populations or medical records that miss utilization outside healthcare channels. No nationally representative cohort has characterized real-world uptake, indications, and access. Objective: To characterize GLP-1/GIP prevalence, indication, clinical profile, and access. Design: Prospective cohort study with three GLP-1/GIP surveillance waves (March 2024, December 2024, October 2025). Setting: The Understanding America Study, an address-based, nationally representative panel of approximately 15,000 US adults aged 18+ years initiated in 2014. Participants: UAS participants responding to at least one surveillance wave (n=9150). Exposures: GLP-1/GIP use status (never vs any use, comprising current and former use), self-reported primary indication (diabetes, weight loss, or other), and access pathway (traditional vs non-traditional). Main Outcomes and Measures: Survey-weighted prevalence of GLP-1/GIP use, overall and by indication and access pathway; sociodemographic, cardiometabolic, treatment, and access characteristics; and smartwatch-derived resting heart rate, heart rate variability, maximum activity heart rate, step count, and sleep duration and variability. Results: Among n=9150 adults (1274 with any use; 60.9% female; median age 53 years), weighted prevalence increased 46%, from 8.2% (March 2024) to 12.0% (October 2025) representing 32 million. Weight-loss indications grew, reaching nearly half of use (4.1% to 5.6%); diabetes-indicated use was stable (5.3% to 5.4%). Users carried high cardiometabolic burden (obesity, 68.2%; diabetes, 53.6%) but diverged by indication: diabetes-indicated users were older (median, 59 vs 49 years), whereas weight-loss-indicated users were more often female (69.9% vs 51.3%) and healthier. One in three users (~9 million) had non-traditional access, especially in weight-loss-indicated users, of whom 33% had no conventional prescription; 41% used compounding, online, or foreign pharmacies; and, 43% lacked coverage. Non-traditional users were five times as likely to report an unlisted, likely compounded formulation (19.8% vs 4.1%). All p<0.05. Conclusions and Relevance: Real-world GLP-1/GIP use has grown rapidly and diversified substantially in indication, access, and population profile. One in 3 users obtained treatment through nontraditional channels largely invisible to claims data, raising long-term safety, efficacy, and coverage questions. GLIMMER provides a public, nationally representative longitudinal evidence base for future payer and provider decisions.

Background. Videolaryngoscopy still requires adjuncts or hyperangulated rescue in a clinically important minority, and bedside screening discriminates modestly. Point-of-care ultrasound (POCUS) of the anterior airway is a promising alternative, but existing prediction models are opaque or assume a pre-specified functional form. We developed and internally validated a parsimonious, fully disclosed POCUS risk equation whose form is recovered from data and whose structural properties are machine-checked by formal proof - to our knowledge the first formally verified clinical risk predictor - following TRIPOD+AI 2024. Methods. In a prospective single-centre, single-operator cohort of 259 adults undergoing elective videolaryngoscopy (no-Easy airway 68/259, 26.3%), Sequentially Thresholded Least Squares with bootstrap stability selection (B=300) screened a 71-term library of nine POCUS features and retained a seven-term logistic equation; a two-term bootstrap-stable model was pre-specified as robustness analysis. Internal validation used 5x10 repeated cross-validation plus temporal and device hold-outs, with pre-specified overfitting and optimism assessments. Five behavioural properties of the deployed equation were machine-checked in Lean 4. Results. Two interactions met the |c|/sigma_c>2 stability criterion: skin-to-epiglottis x skin-to-hyoid-bone distance and tongue volume x sagittal tongue area. The seven-term equation reached a 5x10 cross-validated C-statistic of 0.966 (optimism-corrected 0.968) and held across temporal and device hold-outs (0.94-0.97). Calibration-in-the-large matched prevalence, with cross-validated slope 0.90 attenuating to 0.625 out-of-time; standard recalibration restored 0.92 without loss of discrimination. The pre-specified two-term robustness model reproduced this performance (C-statistic 0.964-0.968; events-per-parameter 34; shrinkage 0.99), confirming the result is not an artefact of the screening stage. Net benefit over a clinical baseline was positive across 10-50% thresholds. All five Lean 4 theorems compiled without sorry. Conclusions. A sparse, formally verified POCUS equation predicts difficult videolaryngoscopy with high internally validated discrimination and quantified, modest overfitting. Because the equation was developed in a single-operator cohort and its inputs are operator-dependent, external validation requires prior harmonisation of the measurement protocol and operator credentialing.

Background: Prostate cancer enzalutamide treatment is approved at a standard dose of 160 mg daily. Concerns for real-world patients -- older and more fragile than those enrolled in clinical trials -- have prompted consideration of initiating treatment with lower doses, but the long-term efficacy of this approach remains unknown. We evaluate the long-term survival and longevity in patients treated with standard versus upfront low-dose enzalutamide. Methods: Retrospective analysis of 151 patients treated with enzalutamide (102 receiving 160 mg; 49 receiving [&le;]80 mg) between 2014--2021 at the Centre Hospitalier Universitaire de Martinique, with complete follow-up through end of life (98.7% completeness of follow-up). Primary outcomes were overall survival (OS), progression-free survival (PFS), and longevity (attained age). Results: Doses [&le;]80 mg were associated with longer median OS (36.3 vs. 20.7 months), improved restricted mean OS (difference of 0.7 years, p=0.05), and enhanced longevity (median 82.5 vs. 78.3 years, p=0.004). PSA response rate at 12 weeks was higher with lower-dose (71.4% vs. 48.8%, p=0.016). In multivariable models adjusted for prognostic factors, [&le;]40 mg compared with 160 mg was non-inferior regarding OS (HR=0.61, 95% CI 0.36--1.06), superior regarding PFS (HR=0.59, 95% CI 0.35--0.99), and superior regarding longevity (HR=0.48, 95% CI 0.28--0.84). Bone metastasis, poor performance status, PSA response, time to PSA nadir, and disease duration were independent predictors of outcomes. A post-hoc analysis revealed a strong association between dose and physician-prescribing profiles, ranging from "endorse-lowest-dose" to "never-deviate-from-full-dose". Conclusions: Lower doses of enzalutamide were non-inferior to full-dose. Dose-adapted strategies warrant further investigation.

Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.

Background. In Europe, first-trimester combined screening with the Fetal Medicine Foundation (FMF) algorithm identifies women at increased risk of preeclampsia who may benefit from personalized aspirin prophylaxis. However, a substantial proportion of early-onset preeclampsia (EOPE) remains undetected at clinically acceptable specificity. Objective. To evaluate the first-trimester performance of MaiRa for early-onset preeclampsia (EOPE) risk stratification by benchmarking it against FMF screening in the same women, characterizing discordant patient-level classification profiles and exploring potential implementation strategies. Study Design. This secondary case-control analysis was nested within the prospective, multicentre PREMOM cohort [NCT04990141], which enrolled women with singleton pregnancies across 14 tertiary hospitals in Spain. First-trimester MaiRa and FMF risk estimates were evaluated in the same 126 pregnant women, comprising 99 uncomplicated controls and 27 EOPE cases, defined by disease onset before 34 weeks. Discrimination was compared using a stratified paired bootstrap analysis of the areas under the receiver-operating-characteristic curves. Performance was assessed at prespecified clinical thresholds, and detection rates were evaluated at fixed false-positive rates. Universal and contingent MaiRa implementation strategies were also evaluated. Results. MaiRa showed greater first-trimester discrimination for EOPE than FMF combined screening (AUC, 0.974 vs 0.900; P=.040) and consistently achieved higher detection rates across fixed false-positive rates. At false-positive rates of 5% and 10%, MaiRa detected 85.2% and 92.6% of EOPE cases, compared with 44.4% and 70.4% for FMF, respectively. Patient-level analysis demonstrated that MaiRa identified 12 of 27 EOPE cases (44.4%) classified as low risk by FMF; these pregnancies generally exhibited less abnormal conventional first-trimester profiles, including fewer maternal risk factors, lower mean arterial pressure and lower uterine artery pulsatility index, yet 8 of 12 (66.7%) subsequently developed severe EOPE. Exploratory implementation analyses showed that universal MaiRa screening achieved the highest EOPE detection, whereas a contingent strategy using FMF for triage and reflex MaiRa testing reduced molecular testing to 35.7% of pregnancies while maintaining 77.8% sensitivity and 97.0% specificity. Conclusion. MaiRa provided greater first-trimester discrimination for EOPE than conventional combined screening and detected additional pregnancies that later developed severe disease despite less abnormal conventional screening profiles. The findings suggest that maternal plasma cfRNA profiling captures biological alterations not fully reflected by combined first-trimester screening and support further prospective evaluation in an independent, unselected obstetric population. Key words: early-onset preeclampsia; first-trimester screening; cell-free RNA; liquid biopsy; Fetal Medicine Foundation algorithm; combined screening; risk stratification; aspirin prophylaxis.

Background: Stress research in nursing education has largely focused on distress, stressors, and negative outcomes, although challenging experiences may also support motivation, confidence, learning, and growth when appraised positively. Objective: To develop and evaluate the psychometric properties of the Nursing Student Positive Stress Scale (NSPSS). Design: A methodological instrument development and psychometric evaluation study. Methods: The NSPSS was developed using a deductive, theory-driven approach informed by the transactional theory of stress and coping and positive psychology perspectives. Content validity was assessed by an international nursing expert panel. Psychometric evaluation used national survey data from nursing students in New Zealand. Of 539 responses, 507 were analysed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted using separate subsamples. Internal consistency was assessed using Cronbach's alpha and McDonald's omega, and convergent validity through correlation with Perceived Stress Scale-10 scores. Results: Content validity was strong (I-CVI = .88-1.00; S-CVI/Ave = .975; S-CVI/UA = .800). EFA identified a dominant factor explaining 41.38% of variance (loadings = .528-.735). CFA supported a two-context Academic and Clinical Positive Stress model with correlated residuals between five parallel item pairs, chi-square(29) = 60.49, CFI = .970, TLI = .954, RMSEA = .063, SRMR = .065. Internal consistency was good (alpha = .839; omega = .843). NSPSS scores correlated negatively with PSS-10 scores (r = -.298, p < .001). Conclusion: The NSPSS demonstrated strong content validity, preliminary evidence of structural and convergent validity, and good internal consistency reliability for assessing positive stress appraisal among nursing students. Further validation in independent samples is warranted.

Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.