Frontiers in Aging

Objectives: We tested how multi-level socioeconomic disadvantage relates to biological aging and systemic inflammation in women and men from the population-based Canadian Longitudinal Study on Aging (CLSA). Methods: We examined cross-sectional data from 8,516 CLSA participants with baseline measures on systemic inflammatory biomarkers (C-reactive protein, interleukin-6, and tumoral necrosis factor-) and biological aging (metabolomic and six DNA methylation [DNAm] age estimates). Plasma samples underwent metabolomic profiling by Metabolon, Inc. Metabolomic age was estimated separately in males and females using sex-stratified models based on age-correlated metabolite levels. DNAm data generated using the Illumina Infinium MethylationEPIC v1.0 array were used to estimate DNAm age across six established models, including Horvath, Hannum, PhenoAge, GrimAge, GrimAge2, and DunedinPACE. We used log-transformed metabolite levels to calculate metabolomic age by sex. We linked education, income, material and social deprivation to biomarkers of systemic inflammation and biological aging stratified by sex using generalized linear models. Multivariable models were adjusted by age, major behavioral risk factors, and chronic conditions. Results: Participants were aged on average of 62.6 years of age, and approximately 50% were females. In multivariable linear adjusted models, we found that in comparison to those earning [&ge;]$100K a year, women earning less <$20K were on average 1.14 (95%CI: 0.46, 1.82) year older with respect to metabolomic age; those earning [&ge;]$20K & <$50K were on average 0.90 (95%CI: 0.26, 1.53) years older; and those earning [&ge;]$50K & <$100K were on average 0.70 (95%CI: 0.05, 1.34) years older. We did not observe this dose response among men. A similar dose-response association was observed for interleukin-6 in both men and women. Discussion: These findings suggest that socioeconomic adversity influences not only inflammatory pathways but also distinct biological aging processes, including metabolomic aging.

Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated death rates. This research introduces a dual-mode artificial intelligence system that uses two complementary models to enhance malaria pre-screening and diagnosis. The patient-centered model uses multivariate logistic regression to analyze biosignals, including heart rate, body temperature, and oxygen saturation, collected through a wearable sensor prototype and a mobile interface for symptom analysis. The system enables patients to begin self-assessment to determine their level of need before scheduling a doctor's appointment. The clinician-centered model represents a customized convolutional neural network that uses annotated microscopy images of red blood cells to achieve 94.84% accuracy, 95.71% precision, 93.87% recall, 94.78% F1 score, and 0.84 Area Under Curve (AUC). The patient model achieved 94.6% accuracy and an AUC of 0.985 using a 70/30 train-test split. These systems work together to create a layered diagnostic system that can operate independently or together to detect malaria at an early stage, especially in areas with limited resources. The findings demonstrate that wearable biosignal data integration with image-based deep learning can produce dependable, scalable, and user-friendly systems for malaria pre-screening. Keywords - malaria diagnosis, artificial intelligence (AI), convolutional neural networks (CNN), wearable biosensors, multivariate logistic regression

Introduction: Recommendations from physicians and peers play a crucial role in promoting vaccination. This study evaluated differences in recommendations to others regarding four vaccines with varying efficacy (seasonal influenza, measles, human papillomavirus [HPV], and coronavirus disease 2019 [COVID-19]) between physicians and the general public and examined the impact of birth-year-based vaccination policy changes on these recommendations. Methods: This cross-sectional study was conducted in February 2026 among 492 physicians and 5,252 members of the general public in Japan. Consistency in recommendations across the four vaccines was assessed using the intraclass correlation coefficient (ICC[3,1]), and group differences were examined using a two-way mixed-design analysis of covariance. Multilevel regression discontinuity analyses were performed to evaluate the effects of birth-year-based vaccination policy. Results: Physicians showed significantly stronger recommendations to others than the general public, and their recommendation patterns generally reflected vaccine efficacy. However, physicians showed lower consistency across vaccine types than the general public (ICC[3,1]), driven primarily by heterogeneity in COVID-19 vaccine recommendations. Regression discontinuity analyses showed that birth-year-based vaccination policy, including routine vaccination opportunities, was significantly associated with recommendations to others for measles and HPV vaccines, independently of perceived benefits and risks. Conclusion: To improve vaccination coverage from a public health perspective, it is important for physicians to provide effective vaccination recommendations to the general public on a broader scale; however, it is also necessary to address the heterogeneity in vaccine-specific recommendation patterns among physicians, as observed for COVID-19. Routine vaccination opportunities may increase vaccination coverage not only through the routine vaccination program itself but also through peer effects among the general public. Vaccination policy may therefore influence vaccination coverage not only in the current generation but also in future generations. Designing vaccination policy should consider its long-term impact on future vaccination coverage as well as herd immunity.

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.

We present FootNet, a 453-image multi-view smartphone foot dataset for binary foot segmentation, with expertannotated masks across six anatomical views (dorsal, medial, and plantar, both left and right). We benchmark four segmentation models under a controlled protocol: U-Net with a MobileNetV2 encoder achieves the best performance (IoU 0.9268, Dice 0.9608, 95 % CI [0.9209, 0.9320]); DeepLabV3 with MobileNetV3-Large scores IoU 0.8984 (Dice 0.9449); UNet++ with MobileNetV2 scores IoU 0.8913 (Dice 0.9391); and SAM ViT-B with oracle boundingbox prompt scores IoU 0.9219 on the matched 191-image subset. Bonferroni-corrected Wilcoxon signed-rank tests (k = 6 comparisons) show U-Net significantly outperforms DeepLab (p < 0.001, r = 0.638) and SAM ViT-B with oracle boundingbox (p = 0.005, r = 0.202); UNet++ does not significantly differ from DeepLab (p = 0.062). Connected-component postprocessing yields negligible benefit (mean {triangleup}IoU = +0.0003, 12 of 453 images improved). The extended dataset is available upon request

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.

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.

Background: International Classification of Diseases (ICD) codes are often used in epidemiological studies to track disease rates over time. Objective: This evaluation of ICD-code-based algorithms for electronic medical record (EMR) studies of Alzheimers disease (AD) and related dementias (ADRD) examines the impact of incorporating Centers for Medicare and Medicaid (CMS) data as an additional source of diagnostic and treatment information in Department of Veterans Affairs (VA) EMR studies. Methods: We performed a chart review of 100 VA Million Veteran Program (MVP) participants to evaluate algorithm performance. We also assessed genetic associations across algorithms in a large MVP cohort (n=396k). Results: Adding CMS data increased the number of detected cases, sensitivity, and positive predictive value, but decreased specificity and negative predictive value. Genetic analyses showed that broader (ADRD/dementia) algorithms with just VA data performed similarly to narrow (AD-focused) algorithms incorporating both VA and CMS ICD codes. Additionally, narrow AD algorithms based solely on VA data yielded the highest ORs, indicating the largest proportion of late-onset AD cases. Conclusions: We recommend using a broad (ADRD) algorithm without CMS or medication data, particularly for epidemiological studies or a strict AD algorithm including CMS and medication cases for genetic discovery of late-onset AD associations in VA EMR, and a strict AD algorithm without CMS data for applications focused solely on AD and sensitive to misspecification. Careful evaluation of algorithm performance is warranted in different EMR systems, as ICD coding practices vary by institution, as demonstrated by this comparison of VA EMR and CMS data.

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.

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.

The X-linked G-protein coupled receptor GPR174 is highly expressed in T and B lymphocytes and has immunoregulatory roles in mice, but its function in humans is unknown. We describe a cohort of six individuals who have function-disrupting variants in GPR174 and a clinical phenotype of lymphadenopathy and autoimmunity. Histological analysis of two patient lymph nodes revealed necrotizing lymphadenitis and lymphoproliferation resembling Kikuchi-Fujimoto disease. In-depth analysis of three patients and related carriers revealed overaccumulation of CD8 terminally differentiated effector memory cells re-expressing CD45RA (TEMRA). Patient cells and GPR174-deficient CD8 T cells generated from controls showed less repression of proliferation by the GPR174 ligand lysophosphatidylserine (lysoPS) and an effector-biased gene expression program. GPR174-deficient CD4 T cells were resistant to lysoPS-mediated suppression of IL2 production. In mice, chronic viral infection led to over-accumulation of GPR174-deficient effector CD8 T cells. We describe an inborn error of immunity associated with dysregulated lymphocyte responses that we propose predisposes to exaggerated lymphoproliferation and autoimmunity following viral infection.

Background. Early diagnosis and intervention are crucial in multiple sclerosis (MS), yet diagnostic delays are common. Large language models (LLMs) such as generative pre-trained transformers (GPTs) may help streamline diagnostic workflows by extracting MS diagnostic signals from clinical notes. Objective. To derive MS diagnosis status from the first neurology note using a computable algorithm based on the 2017 McDonald criteria and applying GPT-4 for node-level reasoning within a structured decision framework. Methods. We analyzed first neurology notes from 125 randomly selected patients (including those with MS, related disorders, and controls) enrolled in a clinic cohort between 2017 and 2023. We included the clinical history and diagnostic testing sections but redacted the assessment and plan. We converted the 2017 McDonald criteria into a decision tree and provided expert-curated clinical knowledge to guide GPT-4 reasoning at each decision node. GPT-4 generated binary decisions at each node to traverse the tree and classified MS diagnoses at terminal nodes. We evaluated performance against neurologist-assessed diagnoses and characterized hallucinations (non-factual, incongruent, irrelevant, over-reliant, and logical reasoning errors). Results. In this study cohort (mean age 40{+/-}13 years; 81% women) representative of the clinic population, GPT-4 performed well in predicting MS diagnosis (84% accuracy, 79% precision, 74% recall, 91% specificity) using first neurology notes. Hallucinations occurred in 32 cases (26%), most commonly incoherence (75%) and overreliance (47%). Conclusion. A structured, LLM-guided decision framework can flag MS diagnoses from early clinical documentation. Large-scale studies are needed to mitigate hallucinations, validate this approach, and test implementation in clinical settings.