Addiction Neuroscience

Adolescent substance use is a major public health concern due to increased risk of future physical and mental health conditions. Fronto-striatal functioning - particularly regarding inhibition and reward processing - may increase vulnerability to high-risk substance use. However, it remains unclear if these neurobiological differences precede substance use or are consequences of it. The ongoing Adolescent Brain Cognitive Development (ABCD) Study follows over 10000 youth, offering an unprecedented opportunity to longitudinally examine substance use patterns throughout development. We utilized family-clustered time-varying Cox proportional hazard models to prospectively examine main and interaction effects of right Inferior Frontal Gyrus (IFG) inhibitory control and bilateral nucleus accumbens (NAc) reward response, alongside early life adversity and peer substance use as predictors of alcohol and cannabis onset in the ABCD Study. We identified a significant crossover interaction such that left NAc activity had a slight positive association with first full alcoholic drink in the context of higher right IFG activity but a negative association in the context of lower right IFG activity. However, peer alcohol and cannabis use emerged as the strongest predictors of outcomes. Alcohol onset was also more common in females, and early life adversity was associated only with cannabis onset. Findings indicate that interactions between inhibition- and reward-related brain regions may impact risk for early substance use onset, but these effects may be modest relative to socioenvironmental factors. Additionally, divergent alcohol and cannabis findings suggest that risk profiles are substance specific. Peer-focused strategies should be considered in preventive efforts.

Introduction: Risk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch. Methods: We used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation-Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles. Results: Bup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries. Conclusion: This development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.

Opioid addiction treatment is often hampered by the severe dysphoria of opioid withdrawal, but withdrawal treatments are limited by incomplete understanding of brain mechanisms involved. One area frequently implicated in withdrawal symptoms is the central amygdala, whose capsular portion (CeC) is particularly strongly activated during withdrawal. Additionally, a ventral posterior striatal region that resides near CeC, the interstitial nucleus of the posterior limb of the anterior commissure (IPACc), is also activated as strikingly as CeC. However, it is still unknown how these regions are activated, nor whether their activation explains the high intensity of withdrawal dysphoria. Using RNAscope, we found that c-fos expression is induced in the parabrachial nucleus (PB), a key glutamatergic afferent of CeC, after precipitated morphine withdrawal. Chemogenetic inhibition of PB glutamatergic neurons (VG2PB) nearly eliminated withdrawal-induced CeC c-Fos, without affecting IPACc c-Fos, indicating these two nuclei are activated by distinct sources. Furthermore, VG2PB inhibition markedly reduced somatic (jumping) and modestly reduced affective (place avoidance) withdrawal behavior. On the other hand, inhibition of CeC-projecting PB neuronal subtypes expressing calcitonin gene-related peptide (CGRP) or mu opioid receptor (MOR) reduced place avoidance without affecting jumping, indicating their specific role in withdrawal aversion. Strikingly, simultaneous inhibition of VG2PB and posterior striatal region containing IPACc robustly reduced withdrawal-induced place avoidance much more than the modest effects of either inhibition alone, suggesting their cooperative action in driving aversion. Our data suggests that PB-CeC circuit and posterior striatal area constitute a cooperative system driving opioid withdrawal aversion.

Anxiety-related traits (ARTs) have been linked to altered fear learning, but previous studies have typically examined different experimental phases and response systems, limiting the comparability of findings and the accumulation of consistent evidence. Here, we comprehensively examined associations between ARTs and fear conditioning across acquisition, extinction and renewal and across subjective, physiological and neural response systems in a well-powered sample (N = 267) using a two-day differential conditioning paradigm. ARTs were operationalized as a composite of trait anxiety, neuroticism, and intolerance of uncertainty and conditioned responding was assessed using skin conductance responses, fear-potentiated startle, US expectancy ratings, fear ratings, and functional magnetic resonance imaging. Higher ARTs were consistently associated with elevated subjective fear and US expectancy to both threat and safety cues during extinction and renewal, without corresponding elevations in physiological responding. At the same time, ARTs were not associated with threat-safety discrimination in subjective or physiological measures across phases, while neural associations were limited to reduced dorsal anterior cingulate cortex discrimination during early renewal. These findings suggest that ARTs are characterized by a CS unspecific cognitive bias toward heightened threat expectancy and evaluation rather than altered associative fear learning, highlighting the importance of distinguishing conditioned discrimination from general levels of responding across response systems.

BACKGROUND: An individual's adaptation to threatening environments under uncertainty is reflected in stress responses. Predictability (the ability to anticipate events) and controllability (the ability to control outcomes) are central to how one adapts, yet their joint influence on aversive learning remains unclear. METHODS: Thirty healthy adults completed a probabilistic aversive learning task in which cue-outcome contingencies varied across levels of predictability and controllability, i.e. whether shock intensity depended on prediction accuracy. Prediction accuracy, reaction time, subjective stress ratings, and skin conductance responses were recorded throughout. Trial-wise learning dynamics were estimated using the Volatile Kalman Filter. RESULTS: Prediction accuracy reduced as environments became less predictable and negatively associated with higher learning rates across predictability levels, with the strongest relationship observed in highly predictable blocks. Skin conductance responses showed that moderately predictable environments elicited responses like those in highly predictable environments when accurate predictions reduced shock intensity, but resembled responses in unpredictable environments when shock intensity was uncontrollable. Model comparison revealed a double dissociation between subjective stress ratings and skin conductance responses. Subjective ratings were best explained by model-derived volatility when prediction accuracy determined shock intensity and by belief uncertainty when it was independent of prediction accuracy, whereas skin conductance responses showed the reverse pattern. Reaction times were best explained by belief uncertainty when predictions influenced shock intensity. Higher anxiety was associated with elevated learning rates in highly and moderately predictable blocks when predictions did not control shock intensity.

Pouring a drink feels deceptively trivial, yet it requires guiding a boundary-free fluid into a vessel without spilling, overflowing, or toppling it -- a task at which robots remain notoriously brittle. How humans achieve this so effortlessly is unknown, as motor control has predominantly been studied in brief, highly constrained laboratory tasks, leaving the control principles underlying ecological tasks largely unknown. Here we measured continuous sensorimotor control during liquid pouring across various containers, vessels, and speed demands. Despite substantial variation in movement trajectories and durations, individuals maintained a strikingly invariant preferred fill level. Counterintuitively, fill level variability decreased at higher fill levels, and precision was maintained even under time pressure. A stochastic optimal control model combining a data-driven nonlinear approximation of flow dynamics with a cost that balanced individualised fill level, energy expenditure and flow-rate reproduced the behaviour. Humans thus pour optimally, given their sensorimotor limits and idiosyncratic notion of "full".

Excitatory synaptic plasticity within the nucleus accumbens (NAc) drives motivated behaviors, and dysregulation is implicated in several psychiatric disorders marked by impaired reward processing. The NAc integrates glutamatergic input, which conveys information about reward, context, and behavioral goals, with local GABAergic signaling that regulates excitatory transmission and medium spiny neuron (MSNs) output. However, little is known regarding GABA-dependent modulation of activity-dependent excitatory synaptic plasticity. Here, we investigated GABAB receptor (GABABR) regulation of plasticity at hippocampus (Hipp)-NAc synapses, at which plasticity is a key mediator of reward-related behaviors. Using whole-cell electrophysiological recordings in mouse brain slices, we found that pharmacological inhibition of GABABRs converts long-term potentiation (LTP) into long-term depression (LTD) selectively in females, identifying a sex-specific role for GABABRs in modulating long-term plasticity of Hipp-MSN synapses. This LTD required mGluR5 activation and estrogen receptor alpha (ER) in both D1- and D2-expressing MSN subtypes, while only D1-MSNs suggested that LTD was expressed presynaptically through a CB1 receptor-dependent mechanism. Notably, GABABR inhibition did not alter basal synaptic transmission, indicating a specific role for these receptors in gating plasticity beyond regulation of basal excitatory drive. Together, these findings identify a novel, sex-specific mechanism by which GABABRs control the direction of synaptic plasticity.

Objective To measure the accuracy of clinical coding, clinician review, and a locally deployed large language model (LLM) in identifying alcohol, drug, and self-harm involvement in emergency department (ED) attendances, and quantify prevalence. Design Two-phase diagnostic accuracy study. In a validation week, the identification strategies were assessed against a conflict-adjudicated reference standard (n=2,256); the LLM was then applied to n=105,096 annual attendances at the same site. Setting UK Type 1 Emergency Department treating patients [&ge;]16yrs. Main outcome measures Prevalence quantification compared with the reference standard; sensitivity, specificity, and balanced accuracy of each strategy; monthly identification rates and adjusted annual prevalence. Results The reference standard identified 12.1% of attendances as involving alcohol, drugs, or self-harm (coding 6.0%; clinician 10.0%, LLM 15.6%). LLM balanced accuracy matched or outperformed clinician review in all three domains (alcohol 0.942 v 0.930, p=0.635; drug 0.959 v 0.791, p<0.001; self-harm 0.982 v 0.908, p=0.004). Coding recorded 1.07 domains per identified patient against 1.32 in the reference standard. Adjusted annual prevalence corresponded to 12,890 domain involvements per year not identifiable in coded data. Subdomain classification found at least 81.6% of self-harm attendances required medical assessment for injury or overdose before psychiatric review. Conclusions Clinical coding identified fewer than half of presentations involving alcohol, drugs, and self-harm and rarely captured co-occurring domains; under-recording was present across a full year. A locally deployed LLM generated more complete structured data from existing clinical text within NHS infrastructure, at a scale which is not feasible for manual review.

Background: As cannabis policy changes in the United States, secondhand cannabis smoke (SCS) is increasingly common, including within families. However, prevalence of exposure and clinical correlates over time in adolescents are not fully understood. Objectives: (1) To estimate the prevalence of SCS and personal cannabis use in US-based teens exposed to SCS, and (2) examine the cognitive trajectories of adolescents exposed to SCS compared to non-exposed peers. Methods: Data from the Adolescent Brain Cognitive Development (ABCD) Study was used. Participants (n=11,316 of full cohort with follow-up data; n=776 with self-reported family SCS exposure) attended yearly visits from ages 11-17, completing substance use interviews, toxicological testing, and the NIH Toolbox Cognitive battery. Youth with SCS but no personal cannabis use (n=419; 47% female) were matched on prenatal substance exposure, family substance use history, and sociodemographics to non-SCS exposed and non-cannabis-using youth with a 1:2 ratio (Controls n=838). Linear mixed-effects models assessed cognitive performance by SCS*age interactions, accounting for random effects of subject and family. Covariates included sex and alcohol, nicotine, and other substance use. Secondary models analyzed performance by cumulative waves of reported SCS exposure interacting with age. Results: Of the full cohort, 6.9% (n=776) reported exposure to SCS. Of these individuals, 46% endorsed lifetime personal cannabis use by age 17, relative to 20% of non-SCS exposed youth (OR=3.83[95%CI:3.29,4.44]). Within matched participants, SCS*age demonstrated a significant interaction on attention and inhibitory control ({beta}=-0.32, p=.028), with SCS demonstrating reduced improvement over time. More waves of exposure were also associated with worse performance over time ({beta}=-0.39, p=.057). Discussion: Almost half of those who had been exposed to SCS endorsed personal cannabis use. Cognitive findings were domain specific, similar to findings in secondhand tobacco: SCS exposed youth showed restricted improvement in attention and inhibitory control by age 17. Public health and policymakers should make efforts to curb youth SCS exposure, given the potential for risk which has not been fully explored to date.

Background Alcohol use disorder (AUD) is highly prevalent in schizophrenia, yet the neurobiological basis of this vulnerability remains poorly understood. Neurodevelopmental models suggest that pre-existing brain dysconnectivity may increase vulnerability to AUD. We therefore tested whether schizophrenia-like neurodevelopmental pathology alters how alcohol-related experience is incorporated into large-scale brain networks. Methods Resting-state functional connectivity was assessed in male Sprague-Dawley rats (n = 18-21/group) with neonatal ventral hippocampal lesions (NVHL), a neurodevelopmental model of schizophrenia, and sham-operated controls, with or without voluntary adolescent alcohol exposure. Functional connectivity was assessed using seed-to-voxel and seed-to-seed analyses within a cortico-striato-limbic network. We additionally examined whether individual alcohol intake during adolescence predicted adult functional connectivity according to neurodevelopmental status. Results NVHL and adolescent alcohol exposure independently produced predominantly hypoconnected cortico-striato-limbic networks. However, alcohol exposure did not exacerbate NVHL-associated dysconnectivity but instead induced a distinct network reorganization characterized by functional hyperconnectivity. Although alcohol intake was comparable between groups, dose-dependent relationships between adolescent alcohol consumption and adult functional connectivity were observed in sham animals but were absent or markedly attenuated in NVHL rats. These effects were primarily centered on prelimbic cortex connectivity with the amygdala, hippocampus, and dorsal striatum, highlighting this circuitry as a major locus of altered experience-dependent remodeling. Conclusions These findings suggest that vulnerability to AUD associated with schizophrenia-like neurodevelopment may arise less from additive network dysfunction than from an altered capacity of large-scale brain networks for experience-dependent functional remodeling. Schizophrenia-like neurodevelopmental pathology may therefore change how alcohol-related experience is translated into persistent brain network organization.

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.

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.

Introduction Acute lower respiratory tract disease (aLRTD) is a leading cause of hospitalisation and death, particularly in older adults and adults with comorbidities, with acute lower respiratory tract infection (aLRTI; pneumonia and non-pneumonic LRTI) being a major component. Non-pulmonary complications and functional decline after aLRTI are recognised, but their pathogen-specific burden is poorly described. We aimed to quantify renal, hepatic, thromboembolic and functional complications, and mortality, after aLRTI hospitalisation, by clinical phenotype and pathogen. Methods We conducted a cohort study of adults (>18 years) admitted with aLRTD to two hospitals in Bristol, UK (01 August 2022-31 July 2024). aLRTD was classified as pneumonia, non-pneumonic LRTI (NP-LRTI) or no diagnosis of aLRTI. Pathogens were identified from standard-of-care and research microbiology. Outcomes were acute kidney injury (AKI), acute liver dysfunction, venous thromboembolism (VTE), in-hospital falls, reduced mobility at discharge, increased care requirements, and 30-day and 1-year mortality. Analyses were descriptive. Results Among 246,797 adult admissions, 21,456 aLRTD hospitalisations were included: 10,239 (47.7%) pneumonia, 7,742 (36.1%) NP-LRTI and 3,475 (16.2%) with no evidence of aLRTI. Of 19,152 tested aLRTD admissions, 8,503 (44.4%) had a positive microbiological/virological test, yielding 9,204 pathogen detections; 1,194 (6.2%) had co-infections, and SARS-CoV-2 was most frequent, with influenza the second most common in pneumonia and NP-LRTI. Pneumonia had greater severity than NP-LRTI and no diagnosis of aLRTI (median length of stay 6 vs 4 vs 4 days; ICU admission 3.4% vs 0.7% vs 0.5%, respectively). Overall, 22.2% developed AKI, 6.1% acute liver dysfunction, 0.6% DVT and 2.4% PE; 1.8% had a fall, 11.5% reduced mobility, and 16.6% required increased care at discharge. 30-day and 1-year mortality were highest for pneumonia (14.0% and 32.0%, respectively). Pathogen-specific analyses showed longer stays and higher complications and mortality rates for SARS-CoV-2 and Streptococcus pneumoniae, and shorter stays with lower complication and mortality rates for influenza and Haemophilus influenzae. Conclusions Non-cardiovascular complications and functional decline after aLRTI were common, particularly in pneumonic and SARS-CoV-2 or pneumococcal disease. These findings support routine surveillance for renal, hepatic, thromboembolic events, early mobilisation and rehabilitation, and consideration of multi-system outcomes when evaluating public health and economic value of vaccines and therapies.

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

Background Elexacaftor-tezacaftor-ivacaftor (ETI) has been demonstrated to improve lung function in clinical trials; however, evidence describing effects on trajectories and whether long-term improvements are sustained (>1-year) is lacking. We estimated within-person lung clearance index (LCI) trajectories before and after ETI initiation, assessing changes in level and rate of change, alongside acute LCI change, up to three years after ETI initiation. Methods Prospective observational study of children at a tertiary hospital. Children aged 3-17 years with [&ge;]2 LCI testing occasions (i) before and (ii) after starting ETI were used to describe lung function trajectories. Children with [&ge;]1 pre-ETI and [&ge;]1 post-ETI LCI occasion(s) were used to describe acute LCI change after ETI initiation. Age-adjusted LCI trajectories for time periods (i) before and (ii) after ETI initiation were estimated using linear mixed-effects models, and pre- and post-ETI LCIs were compared using paired Wilcoxon tests. Results Mean pre-ETI and post-ETI longitudinal changes in LCI were -0.007 (95% CI: -0.28, 0.27; n=35) and 0.12 (95% CI: -0.17, 0.41; n=20) turnovers per year, respectively. Before ETI initiation, 57% (30/53) of patients had an LCI[&ge;]7.1 turnovers (indicating impaired lung function), compared to 26% (14/53) post-ETI, with a median LCI difference of -0.70 (95% CI -0.84, -0.46; p<0.001) turnovers. Within-individual variability in LCI decreased post-ETI. Conclusions Our real-world data within a unique longitudinal study provide a comprehensive picture of ETI benefit by outlining not only acute improvement in LCI but maintained stability in LCI trajectories and improved LCI stability sustained up to three years post-initiation.

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: Current understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk. Methods: We analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors. Results: Meta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg/dL to 140 mg/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol/L and chloride a 1.28-fold increase across 96-100 mEq/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk. Conclusion: This cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.

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.