Toxicology and Applied Pharmacology

Background: Randomized controlled trials have shown that computed tomographic (CT) screening reduces lung cancer mortality. Improved identification of at-risk groups, by leveraging non-smoking risk factors, could help refine screening selection. Aim: To evaluate polygenic risk scores (PRSs) and ambient air pollution (AAP) exposure for risk stratification in the NELSON lung cancer screening cohort. Methods: Two PRSs (PRS-McKay/PRS-Byun) and several AAPs (including nitrogen dioxide, ozone, and particulate matter [PM]) were assessed in the NELSON lung cancer screening trial (N=7,364). PRSs were validated in the Rotterdam Study (N=11,493). Associations with lung cancer, mortality, screening results, and discriminative ability to distinguish lung cancer were evaluated. Results: PRS-McKay and PRS-Byun were associated with lung cancer (odds ratio [OR] per SD [95%CI]: 1.22 [1.08-1.37] and 1.28 [1.13-1.44], respectively) and lung cancer-specific mortality (OR [95%CI]: 1.24 [1.05-1.47], for both), but not with non-lung cancer mortality (OR [95%CI]: 1.01 [0.94-1.10] and 1.03 [0.95-1.12], respectively). Exposure to PM2.5 was associated with lung cancer (OR [95%CI]: 1.11 [1.01-1.22]). PM constituents were associated with adenocarcinoma, particularly PM10 (OR [95%CI]: 1.16 [1.01-1.32]) and ultra-fine particles (OR [95%CI]: 1.16 [1.04-1.30]). PRS and AAP added modestly to the discriminative ability for lung cancer on top of pack-years, age, and sex (area under the curve [95%CI]: 0.659 [0.624-0.695] vs. 0.643 [0.608-0.679]). Conclusions: PRSs and exposure to PM were associated with lung cancer in a high-risk screening population. The primary potential of PRSs may reside in refining lung cancer screening selection toward individuals at higher risk of dying from lung cancer specifically.

Copper (Cu) is an essential metal involved in neurobiological processes including energy metabolism and neurotransmission, yet dysregulated Cu levels may adversely affect brain health and olfactory performance. Although olfactory dysfunction has primarily been studied in older adults and neurodegenerative disease, adolescence is a critical period of brain maturation during which the olfactory system may be particularly vulnerable. This cross-sectional study examined associations between Cu exposure, olfactory bulb (OB) volume, and olfactory performance in 200 adolescents and young adults (64% female; ages 13 - 25) from the Public Health Impact of Metals Exposure cohort. Cu concentrations in blood, urine, hair, and saliva were measured using inductively coupled plasma mass spectrometry. T2-weighted magnetic resonance imaging scans estimated left, right, and total OB volumes using a three-stage deep learning pipeline. Olfactory performance was assessed using the Sniffin Sticks test. Weighted quantile sum regression evaluated associations between a Cu mixture index and OB outcomes, while standard linear regression models assessed individual Cu biomarkers. Models were adjusted for age and sex. A higher Cu index was associated with reduced left (Beta= -0.72, 95% CI [-1.42, -0.02]), right (Beta = -0.79, 95% CI [-1.43, -0.15]), and total OB volume (Beta= -1.55, 95% CI [-2.85, -0.25]), as well as lower odor threshold scores (Beta = -0.23, 95% CI [-0.42, -0.03]). Individual biomarkers were not independently associated with outcomes. These findings suggest that Cu exposure may adversely affect olfactory neurodevelopment during adolescence and highlight the importance of studying environmental exposures relevant to long-term neurological health.

Background: Lead exposure is estimated to cause approximately 3.5 million premature deaths a year, yet the key ongoing sources of lead exposure are unclear. Methods: We estimated the contribution of dietary lead intake to global blood lead levels (BLLs) for 7-year-old children and 22-year-old adults by applying the All-Ages Lead Model (AALM) to calculate blood lead levels (BLLs) based on 25 total diet studies (TDS) that quantify dietary lead intake across 46 countries. Results: For children, the population-weighted average dietary lead intake in low- and middle-income countries (LMICs) (32.0 g/day) was found to be more than three times higher than in high-income countries (HICs) (9.3 g/day), and more than 10 times higher than the FDA reference level for children (2.2 g/day). The average impact on BLLs for children is estimated to be near 29 g/L in LMICs and near 12 g/L in HICs. Averaged across the TDS data, vegetables (27%) and cereals (24%) were found to contribute the most to dietary lead. Conclusions: While there are limitations associated with biokinetic modelling and the TDS data from LMICs, these results suggest that the contribution of dietary lead intake to global lead exposure is in the region of 40 to 50%, suggesting, in turn, that dietary lead intake is likely a major global driver of lead poisoning. Lead absorbed from the environment into food crops is expected to be the key driver of dietary lead. Current regulatory levels for maximum lead concentrations in foods (0.05-0.3 mg/kg) are out-of-date and may imply a dietary lead intake of 200 g/day, far higher than the FDA reference level (2.2 g/day). Collecting representative TDS data in high lead burden countries should be a priority. Further research is also recommended on upstream lead sources and pathways of lead uptake in plants, driving global food contamination.

Exposure to higher levels of ambient air pollution during pregnancy has been linked to multiple adverse pregnancy outcomes. However, studies on acute exposures and reduced gestation length have reported inconsistent findings. This project aims to examine the acute association between ambient air pollution and preterm (28-36 gestational weeks) or early-term (37-38 gestational weeks) births. Daily concentrations of 12 air pollutants, based on bias-corrected numerical model outputs, were linked to vital records of singleton live preterm and early-term births from 2005-2017 in California, Florida, Georgia, Kansas, Nevada, New Jersey, North Carolina (2005-2015), and Oregon. Under a time-stratified case-crossover design, odds ratios (OR) were estimated via conditional logistic regression with adjustment for risks among ongoing pregnancies, meteorology, time trends and federal holidays. We estimated cumulative associations up to a 6-day lag using distributed lag models. Risk estimates per interquartile range (IQR) increase in exposure were pooled across states using inverse-variance weighting. Our study included 1,085,162 preterm and 3,901,185 early-term births. We observed positive associations between 0-2 day cumulate exposure to several air pollutants and early-term births, including NO2 (OR: 1.0023, 95% CI:1.0010, 1.0037 per 7.1 g/m3 increase), PM2.5 (OR=1.0022, 95% CI: 1.006, 1.0038 per 4.6 g/m3 increase), PM2.5 organic carbon (OR= 1.0026, 95% CI: 1.0013, 1.0039 per 1.7 g/m3 increase) and PM2.5 elemental carbon (OR=1.0025, 95% CI: 1.0014, 1.0035 per 0.26 g/m3 increase). Associations with preterm birth were mostly null. In conclusion, we found positive associations between short-term air pollution exposure, including PM and major PM2.5 components, and risks of early-term birth.

Background: Cardiovascular disease remains the leading cause of death in the United States, and marked geographic disparities in cardiovascular mortality persist. However, the community-level socioeconomic indicators most strongly associated with these disparities remain unclear. Community-level measures capture the social and economic conditions that influence cardiovascular health across populations and may help identify communities at greatest risk. We used the Area Health Resources File (AHRF) to identify socioeconomic measures most strongly associated with county-level cardiovascular mortality. Methods: We performed a national cross-sectional ecological analysis using the 2024-2025 Area Health Resources File (AHRF), including counties in the 50 U.S. states and the District of Columbia. The primary outcome was an AHRF-defined cardiovascular mortality composite derived from 2021-2023 National Center for Health Statistics (NCHS) mortality data. Community-level socioeconomic measures included 2023 overall, pediatric, and family childhood poverty and 2019-2023 overall, female, and White unemployment. County-level associations were evaluated using Spearman rank correlation and regional differences using the Kruskal-Wallis test. Sensitivity analyses used a partial mortality composite and Kendall {tau} correlation. Results: Among 1,982 counties, cardiovascular mortality varied significantly across U.S. Census divisions (P<0.001), with the highest population-weighted rate in the East South Central division (348.5 deaths/100,000) and the lowest in the Mountain division (233.9 deaths/100,000). Pediatric poverty demonstrated the strongest association with cardiovascular mortality ({rho}=0.612), followed by family childhood poverty ({rho}=0.603) and overall poverty ({rho}=0.524, all P<0.001). In contrast, unemployment measures were more weakly associated (overall {rho}=0.209, White {rho}=0.176, female {rho}=0.141, all P<0.001). Results were consistent in sensitivity analyses. Conclusions: County-level poverty, particularly pediatric poverty, was more strongly associated with cardiovascular mortality than unemployment across U.S. counties. These findings suggest pediatric poverty may serve as a useful community-level indicator for identifying populations at increased cardiovascular risk and prioritizing future public health interventions.

Background: The optimal timing of percutaneous coronary intervention (PCI) in patients undergoing transcatheter aortic valve replacement (TAVR) remains unclear, particularly regarding its impact on renal outcomes. Methods: We conducted systematic review and meta-analysis of studies comparing staged versus concomitant PCI in patients with aortic stenosis and coronary artery disease undergoing TAVR. We searched MEDLINE, Embase, and Cochrane databases comprehensively. Using a random-effects model, we calculated odds ratios (OR) with 95% confidence intervals (CI) to assess the incidence of contrast-induced acute coronary injury (CI-AKI) across different stages. Results: The analysis included 11 studies encompassing 7,119 patients. Overall, staged PCI did not significantly differ from concomitant PCI in reducing CI-AKI (OR 1.02; 95% CI 0.53 to 1.98; p = 0.959; Figure 2A). Subgroup analysis revealed no significant differences in stage 1 (OR 1.99; 95% CI 0.38 to 10.47; p = 0.417; Figure 2B) or stage 2 CI-AKI (OR 1.01; 95% CI 0.39 to 2.64; p = 0.978; Figure 2C). However, a statistically significant difference emerged for stage 3/4 CI-AKI, favoring the staged approach (OR 0.48; 95% CI 0.24 to 0.99; p = 0.046; Figure 2D). Conclusion: While staged PCI does not consistently reduce CI-AKI in patients undergoing TAVR, it may offer potential benefits for more severe kidney injury (stages 3/4). Given the observed heterogeneity, large-scale randomized controlled trials are essential to establish the relationship between procedural timing and renal outcomes.

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

Controlled human infection models (CHIMs) are increasingly used in infectious disease research to study pathogen dynamics and evaluate interventions under controlled conditions. However, these studies are resource-intensive and involve ethical and safety constraints, making efficient study design critical. Dose-finding is a key early component in CHIMs, where the aim is to identify a challenge dose that achieves a target infection probability. Traditional rule-based designs are commonly used but can be inefficient, motivating the use of model-based adaptive approaches such as the Bayesian Continual Reassessment Method (CRM). Although CRM has been extensively studied and widely adopted in Phase I oncology trials for identifying the maximum tolerated dose of therapeutics, its application in CHIM settings remains limited, particularly when the endpoint of interest is infection. This tutorial provides step-by-step guidance for implementing a Bayesian CRM in dose-finding CHIMs, using an oropharyngeal Neisseria gonorrhoeae challenge as a motivating case study. The framework outlines key design components, including dose-grid specification, dose-response model, prior elicitation, Bayesian updating, decision rules, and stopping criteria, with particular emphasis on a clinically interpretable parameterisation. Trial operating characteristics are evaluated through simulation studies under multiple dose-response scenarios and prior-predictive analyses, and compared with a commonly used '3+3' type rule-based design. This work highlights the advantages of Bayesian model-based designs for dose-finding in CHIMs over classic rule-based designs and provides a structured, reproducible framework for implementing CRM, supporting their application in future CHIM studies.

Background and Aims The optimal treatment for first episodes and first recurrences of Clostridioides difficile infections (CDI) is unknown and there is emerging evidence for pulse and taper (P-T) regimens. Therefore, we sought to estimate the relative efficacy of treatment options. Methods MEDLINE and CENTRAL were searched from database inception to May 21, 2025 and unpublished conference abstracts were searched from recent infectious disease conferences. RCTs on the treatment of first episodes or first recurrences of CDI comparing fixed-dose or P-T regimens of fidaxomicin or vancomycin were included. The primary and secondary outcomes were 40- and 56-day CDI recurrence, respectively. A random-effects network meta-analysis on the risk ratio (RR) scale was conducted using a standard regimen (10-14 days) of vancomycin as the comparator. Treatments were ranked using the surface under the cumulative ranking curve (SUCRA). Results 8 RCTs were included comprising a total of 2181 patients. For 40-day recurrence, fidaxomicin P-T had the highest probability of ranking best (RR=0.10, 95%Confidence Interval [95%CI]=0.10-0.49, SUCRA=1.00), followed by vancomycin P-T (RR=0.49, 95%CI=0.32-0.76, SUCRA=0.61), fixed-dose fidaxomicin (RR=0.61, 95%CI=0.49-0.76, SUCRA=0.39), and, finally, fixed-dose of vancomycin (SUCRA=0.00). The treatments ranked in the same order for 56-day recurrence, though only 3 RCTs reported on this timepoint. Conclusion Vancomycin P-T, fidaxomicin P-T, and fixed-dose fidaxomicin were all superior to a fixed-dose vancomycin. Head-to-head comparative effectiveness RCTs are needed to quantify their relative effect sizes of and impact on long-term prevention of recurrent CDI.

Background The 2022-2023 global mpox outbreak highlighted the critical need for robust genomic surveillance capabilities to track mpox virus (MPXV) evolution and transmission dynamics. Methods Building upon our established SARS-CoV-2 sequencing infrastructure, we implemented a Molecular Loop probe-based long-read sequencing approach using Pacific Biosciences Sequel II technology for comprehensive MPXV genomic surveillance across the United States (US). From August 2024 to June 2025, we generated 326 high-quality whole genome sequences from residual mpox-positive clinical specimens collected by Labcorp across all 10 US Department of Health and Human Services regions. Results Our analysis identified two samples containing clade Ib MPXV in January and June 2025 and captured shifting trends in clade IIb diversity, with 13 distinct lineages observed. We also identified multiple instances of large (~1.6-17.6kb) deletions proximal to the inverted terminal repeats in clade IIb genomes. APOBEC3 mutation analysis indicated substantial evidence of human-to-human transmission among both clades. Further, we observed significantly higher APOBEC3-associated SNPs per kilobase (P<0.001) in clade IIb genomic variable regions relative to their central conserved region. Our assay exhibited strong reproducibility across biological replicates from individual patients and accuracy was confirmed via parallel sequencing of select specimens by US Centers for Disease Control and Prevention (CDC) using metagenomic sequencing. We also demonstrated via custom simulation that our assay discriminates all known MPXV clades and lineages, including those we have not observed in the US. Conclusions Our integrated nationwide surveillance system facilitates real-time genomic tracking of outbreak evolution, with demonstrated capacity across SARS-CoV-2 and MPXV, positioning this platform for rapid deployment during future pathogen emergence.

Background: Monoclonal antibodies have emerged as a prophylactic strategy to prevent symptomatic SARS-CoV-2 infection in immunocompromised individuals. However, the evolutionary and clinical implications of breakthrough infections under this regime remain unclear. Methods: A male in their 80s with a haematological/oncological diagnosis received a 2000 mg intravenous infusion of sotrovimab in March 2023 and was diagnosed with COVID-19 by RT-qPCR from a nasopharyngeal swab in August 2023. Weekly samples (n=24) were collected through February 2024 (171 days). All samples underwent whole-genome sequencing, with select mutations subjected to functional assessment. Findings: Sequencing identified the GE.1 lineage at all timepoints. An intra-host recombination event in ORF1ab (positions 8942-12458) was detected prior to 23 weeks post-detection, followed by a 14-fold increase in viral load (7.42e+06 to 1.00e+08 RNA copies/mL) and a marked shift in the viral population. E340D, a sotrovimab resistance mutation, was detected at low abundance (46%) within the first week post-infection, fluctuated over time, and was nearly fixed by week 15 (107 days) post-detection. We assessed five spike mutations - V36M, S98F, and V213G in the N-terminal domain, Y505P in the receptor-binding domain, and P681Q near the S1/S2 cleavage site - and additionally evaluated the impact of E340D. V36M conferred the highest infectivity across all cell lines, with the most significant effect in low-TMPRSS2 cells. While all mutations showed enhanced infectivity with the addition of E340D, the effect was most pronounced in mutations with lower baseline infectivity. The addition of E340D significantly decreased relative neutralizing titres for V36M, S98F, and V213G, enabling escape from neutralizing antibodies in XBB-responsive individuals, illustrating an enhanced phenotypic advantage. Patient neutralizing activity was absent pre-sotrovimab, and sotrovimab-induced neutralization was further compromised by selection of E340D. Interpretation: Sotrovimab pre-exposure prophylaxis in an immunocompromised patient did not prevent SARS-CoV-2 infection, and selected for resistant mutation E340D, with unexpected fitness consequences across non-receptor binding domain spike regions.

Background: Opioid use disorder (OUD) is heritable, yet most genome-wide association studies (GWAS) have focused on European populations, leaving the genetic architecture of OUD in non-European populations underexplored. Methods: We conducted GWAS of OUD across three ancestries using electronic health records and genomic data from 52,357 All of Us Research Program participants (8,912 cases; 43,445 matched opioid-exposed controls; 48.5% female). Participants were stratified into European (EUR), African (AFR), and Admixed American (AMR) ancestry groups for logistic regression GWAS, with independent replication in the Million Veteran Program. We then applied the deep-learning model AlphaGenome to predict the tissue-specific transcriptomic and splicing consequences of top risk variants across 13 reward-pathway brain regions. Results: We identified and replicated a novel DDX6 risk locus, alongside established OPRM1 and FURIN signals. AlphaGenome predicted the DDX6 regulatory allele downregulates the stress-resistance gene FOXR1 in the nucleus accumbens, while the protective OPRM1 variant (rs1799971) upregulates OPRM1 expression across reward networks. Other signals of interest included IL6R and SHISA9 (EUR); GHR (AFR); and ASTN2 (AMR). Conclusions: This study identifies DDX6 as a novel OUD risk locus, replicates associations with OPRM1 and FURIN, and highlights biologically plausible ancestry-specific signals in AFR and AMR populations. We also replicated top variants in an independent population. Finally, integrating GWAS with deep-learning annotations provides specific, localized biological hypotheses to guide future experimental validation and targeted therapeutics.

The World Health Organization (WHO) validated Sri Lanka had eliminated lymphatic filariasis as a public health problem in 2016, the second country in Southeast Asia to attain this status. However, post-validation surveillance has identified sporadic cases of brugian filariasis. The reemergence of Brugia malayi infections in Sri Lanka warrants urgent investigations. Recent studies have shown that the parasite responsible for the reemergence is a novel zoonotic Brugia sp. maintained among dogs that is closely related but distinct to the human-infecting B. malayi species. The current study employed morphological and morphometric assessments, revealing that this novel zoonotic Brugia sp. is within the B. malayi morphological range. Molecular characterization of three genomic regions, the nuclear genomic region SLXI, the non-coding region HhaI, and the mitochondrial genomic region COXI confirmed it as a genetic variant more closely related to B. malayi than to B. pahangi. Phylogenetic analysis further indicated it as a distinct genomic variant, closely related to a B. malayi-like parasite reported from India. Notably, that same parasite was identified in infected humans, animals, and potential vector mosquitoes. This, together with the detection of both human and animal blood within the same brugian infective mosquitoes, and delineating the canine origin of the parasites in human infections, provides compelling evidence supporting zoonotic transmission of this parasite. To our knowledge, this is the first report demonstrating the presence of the same brugian parasite in humans, domestic animals, and potentially infective mosquitoes in Sri Lanka, supported by multi-genomic evidence. The recent identification of multiple potential mosquito vector species suggests that this parasite may have undergone adaptive changes, facilitating its ability to overcome the species barrier. These findings substantiate the long-held hypothesis of zoonotic transmission of the reemerged brugian parasite, highlighting significant implications for ongoing surveillance and control strategies.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.

Patients and caregivers seek informational and emotional support throughout medical care, especially when interpreting unfamiliar laboratory test results. Although resources such as patient portals and online health communities (OHCs) help address questions, gaps remain. The emergence of large language models (LLMs) offers the potential to be a complementary source of support to assist patients and caregivers in understanding and using their test results. The objective of our study is to empirically compare LLM responses to patients online questions containing their laboratory test results to responses written by peers in an OHC. We compared the 519 peer replies to 122 laboratory test-related posts from an OHC to 488 responses generated from four LLMs using mixed computational and qualitative methods. LLMs frequently provided clear explanations of medical terminology and structured interpretations of numeric results but were longer and less readable. Peers offered more personalized, context-specific emotional support. Overall, LLMs have the potential to complement peer responses in OHCs, but require greater emotional depth, reasoning transparency, and alignment with community norms.

Background: Children born prematurely (before 37 weeks) or admitted to the neonatal unit (NNU) are at increased risk of adverse long-term physical health outcomes. It is also recognised that there is an association with later academic performance and special educational needs, however it is not clear whether these broad risk factors could be used as stand-alone heuristics to identify children who may benefit from additional support in educational settings. We aimed to examine the associations between neonatal unit (NNU) admission and educational attainment in mid-childhood. Methods and Findings: Pregnancy data from a prospective birth cohort (Pregnancy Outcome Prediction Study, Cambridge, United Kingdom, 2008-2012) were linked to national educational outcomes (Department for Education, United Kingdom). Multivariable regression models adjusted for maternal, child, and socioeconomic factors were used to evaluate associations between (i) all NNU admissions, (ii) at term NNU admissions >48 hours, (iii) preterm birth without ongoing physical health needs, and educational outcomes at ages 5-11 years. Children who required any NNU care were more likely not to meet expected educational standards across multiple ages and domains in early and mid-childhood: age 5 early year foundation (aOR 1.64, 95% CI 1.19-2.27, p=0.003), phonics at age 6 (aOR 2.43, 95% CI 1.72-3.57, p<0.001), and at age 7 (here assessments were divided into multiple domains): reading (aOR 1.67, 95% CI 1.18-2.38, p=0.004), writing (aOR 1.72, 95% CI 1.25-2.38, p<0.001), mathematics (aOR 1.56, 95% CI 1.09-2.22, p=0.020), and science (aOR 1.85, 95% CI 1.22-2.78, p=0.003). Similar patterns were observed among both at term-born infants who stayed >48hrs in NNU (phonics assessment at age 6 aOR 2.26, 95% CI 1.51-3.36, p<0.001) and in children born preterm without long-term physical health sequelae (phonics assessment at age 6 aOR 3.07, 95% CI 1.96-4.81, p<0.001). These associations were robust to adjustment for demographic, perinatal, and socio-economic factors. By age 11, differences in academic attainment were attenuated and no longer clearly distinguishable across all exposure groups. However, there was an increased likelihood of special educational needs (SEN) at age 11 associated with any NNU admission (aOR 1.78, 95% CI 1.15-2.73, p=0.009), at term NNU admission for >48hrs (aOR 1.88, 95% CI 1.19-3.00, p=0.007), and children born preterm without long-term physical health sequelae (aOR 1.50, 95% CI 1.00-2.25, p=0.049). Predictive performance of any NNU admission for SEN at age 11 was moderate (AUC 0.70, 95% CI: 1.14-2.65, p=0.010), with balanced sensitivity and specificity and high negative predictive value. Conclusions: NNU admission, for both term and preterm infants, is associated with poorer educational outcomes and an increased likelihood of special educational needs in mid-childhood.

Background Post-infection conditions (PICs), such as Long Covid, are associated with heterogeneous, fluctuating symptoms that profoundly affect daily functioning. Despite moderate-certainty evidence from the NIHR-funded LISTEN trial (COV-LT2-0009) that personalised self management support improves outcomes and may reduce societal and economic impacts of Long Covid, many people living with PICs still receive condition-specific services, generic advice, or stand-alone digital tools that do not address their complex needs. Aim To map care approaches in general practice and synthesise UK evidence for PIC management. Design and setting Scoping review and online survey. Method A two-phase study was conducted: (1) a scoping review of UK evidence on PIC management in general practice; and (2) a supplementary online survey of practitioners working in UK general practice to provide contextual insights. Results The scoping review identified 32 studies focused on Long Covid. One study included a comparator group (ME/CFS). Study populations were predominantly white ethnicity and female. Evidence for non-Covid PICs in UK general practice was largely absent. The supplementary survey (n=46) provided preliminary practice-level insights. Healthcare practitioners reported varied PIC presentations, diagnostic uncertainty, limited referral pathways, inequitable access, and low confidence in managing PICs. Conclusion Evidence informing PIC management in UK general practice remains predominantly Long Covid-focused and may not reflect the range of PICs encountered in practice. While survey findings are preliminary and require confirmation in larger samples, they highlight uncertainty around PIC management. Further research is needed to evaluate whether existing Long Covid pathways should be expanded or complemented by broader PIC models. Keywords general practice; Long Covid; self-management; post-viral syndromes

Objective: There is a bidirectional relationship between emotional distress and pain. However, this relationship is understudied in people with HIV in low-resource settings. This study sought to describe the temporal relationship between emotional distress and pain in people with HIV. Design: Longitudinal observational study. Methods: Participants with virally suppressed HIV, reporting either no pain or persistent pain at baseline, provided weekly remote ratings of distress, worst pain, and average pain using 0-10 visual analogue scales. Within-individual fluctuations in distress and pain were visualised over time. Group-level correlations were determined using Spearman's correlation tests. Cumulative link mixed models assessed whether distress and pain each predicted the other in the following week. Results: 72 participants provided responses over 49 weeks. The participants had a median (IQR) age of 43 (37-51) years, 63% (n=45) were unemployed and most were females (n=51;71%). Distress and pain fluctuated concurrently within individuals: distress was positively correlated with worst pain ({rho}=0.66, 95% CI= 0.60-0.72, p<0.001) and average pain ({rho}=0.70, 95% CI=0.64-0.75, p<0.001) intensity within the same week. Worst pain (OR=1.42, 95% CI=1.17-1.71, p<0.001) and average pain (OR=1.43, 95% CI=1.20-1.71, p<0.001) intensity both predicted distress in the next week. Distress predicted worst pain intensity (OR=1.25, 95% CI=1.07-1.46, p=0.023) but not average pain intensity (OR=1.19, 95% CI=1.01-1.40, p=0.152) in the next week. Conclusions: The temporal relationship between distress and worst pain intensity was bidirectional, whereas distress did not temporally predict average pain intensity. Both pain and emotional distress should receive attention from HIV research and clinical care in low-resource settings.

Lithium has been the gold standard for the treatment and prevention of relapse in bipolar disorder for over 60 years. Guidance from the National Institute for Health and Clinical Excellence states explicitly to 'offer lithium as a first-line, long-term pharmacological treatment for bipolar disorder'. Yet, in the last two decades its use has been in decline with clinicians favouring anticonvulsants or antipsychotics when treating this condition. In this study, we have used three openly available datasets containing prescribing data from primary and secondary care to explore trends in the use of lithium in England, showing both regional and temporal variance between 2015-2024. We have shown that lithium use declined in primary care by 20.9% in the last ten years (2015-2024) and 10.9% overall in the last five years (2019 to 2025). We have also shown how there is some regional variation in the source of lithium for patients, although the vast majority is prescribed in primary care. Further research into clinical behaviour is needed to understand what is driving the decrease in lithium usage, and what barriers and enablers may influence its use across the country.