Magnetic Resonance Imaging

Purpose: To develop an accelerated motion-compensated diffusion-weighted pseudo-continuous arterial spin labeling (MCDW-pCASL) method using a spatial subspace low-rank reconstruction method for efficient quantification of blood-brain barrier (BBB) water exchange (kw) and permeability (PSw). Methods: An accelerated multidelay MCDW-pCASL sequence was developed to simultaneously encode intravascular and extravascular diffusion-weighted ASL signals across multiple post-labeling delays (PLDs). A spatial subspace low-rank reconstruction framework was optimized to enable joint estimation of cerebral blood flow (CBF) and BBB water exchange rate and permeability. Fourteen young healthy adults underwent test-retest scans (separated by ~1 week) at 3T with both the accelerated MCDW-pCASL and a conventional diffusion-prepared (DP) pCASL sequence. Whole-brain, gray-matter, and white-matter CBF and kw values were quantified to assess test-retest repeatability and cross-method agreement. An additional cohort of 30 older adults underwent single-session MCDW and DP scans to evaluate age-related perfusion and BBB kw/PSw differences. Intraclass correlation coefficients (ICCs) were used to assess reliability and agreement. Results: Accelerated MCDW-pCASL demonstrated excellent agreement with DP-pCASL for CBF (ICC = 0.89) and fair agreement for kw (ICC = 0.56). Test-retest repeatability of MCDW-pCASL was good for CBF, BBB kw and PSw (ICC {approx} 0.6). Across both sequences, younger subjects exhibited significantly higher CBF and kw compared with older adults. Conclusion: Incorporating a spatial low-rank subspace reconstruction enables accelerated MCDW-pCASL acquisition with reliable simultaneous quantification of CBF, BBB kw and PSw. Clinical applications of this method for assessing perfusion and BBB function are warranted.

White matter lesion (WML) identification, assessment, and characterization using magnetic resonance imaging (MRI) are fundamental for diagnosis and monitoring of multiple sclerosis (MS). Portable ultra-low field (pULF) MRI at 64 millitesla (mT) has been shown to visualize WML with at least one dimension greater than 4 mm. An automated WML segmentation tool catered to pULF-MRI can provide standardized and accurate quantitative measurements of WML volume. In this study, we sought to investigate and compare the accuracy of machine-learning (ML) and deep-learning (DL) pULF MRI segmentation tools. Same-day paired pULF (64mT) and high-field (HF, 3T) MRI scans from 84 adults with MS or suspected-MS (mean age {+/-} SD: 48 {+/-} 13, 62 females) included T2-FLAIR and T1w images. Reference WML segmentations were manually annotated on pULF T2-FLAIR for all scans, with WML confirmed with registered HF T2-FLAIR. HF reference WML segmentations were created. Four automated segmentation methods were applied to pULF scans: Method for Inter-Modal Segmentation Analysis (MIMoSA), an ML algorithm trained on HF WML masks; WMH-SynthSeg, a convolutional neural network model with flexible segmentation capabilities across field strengths and resolution; nnU-Net, a DL algorithm trained on pULF reference WML masks; and Pseudo-Label Assisted nnU-Net (PLAn), a DL algorithm pre-trained on HF reference WML masks and refined with 64mT reference WML masks. Two models were trained with nnU-Net, one using T2-FLAIR images only (nnU-Net-FL) and one using T1w and T2-FLAIR images (nnU-Net-FL/T1). The same was done with PLAn, creating PLAn-FL and PLAn-FL/T1. The six automated WML segmentation outputs were compared to the manual segmentations to determine Dice Similarity Coefficient (DSC) scores. Associations of WML volume estimates with clinical measures were investigated. DSC scores with pULF reference WML masks from PLAn-FL (DSC mean {+/-} SD: 0.50 {+/-} 0.24) outperformed MIMoSA (0.24 {+/-} 0.20, p < 0.0001), WMH-SynthSeg (0.30 {+/-} 0.18, p < 0.0001), nnU-Net-FL (0.41 {+/-} 0.24, p < 0.0001), and nnU-Net-FL/T1 (0.41 {+/-} 0.26, p = 0.0004). Worse Expanded Disability Status Scale (EDSS) and Scripps Neurologic Rating Scale (SNRS) scores were correlated with higher WML volumes in the pULF and HF reference masks. They were also correlated with WML volumes derived from WHM-SynthSeg, nnU-Net-FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1, but not MIMoSA. After adjusting for age, WHM-SynthSeg, nnU-Net FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1 had significant associations with EDSS and SNRS scores. nnU-Net and PLAn performed best in segmenting WML on pULF-MRI at 64 mT, providing accurate quantitative estimates of WML burden. Moreover, WML volumes estimated by these algorithms were associated with clinical measures of disability, underscoring their utility for reflecting clinical and radiological disease severity. Given pULF-MRI's mobility and lower cost, these findings highlight its relevance in clinical trials, particularly in involving more participants who face logistical constraints and barriers.

ABSTRACT Background: Metallic implants such as orthopedic screws, prostheses, and dental hardware produce beam-hardening, photon-starvation, and streak artifacts that degrade computed tomography (CT) image quality, and the metal artifact reduction (MAR) methods developed to mitigate them require objective, reproducible benchmarking. Purpose: Objective evaluation of MAR algorithms in CT is hindered by the absence of phantoms that simultaneously provide anatomically realistic backgrounds, embedded implants of known geometry, and controllable, ground-truth--referenced artifact intensity. We present a dual-filament, voxel-level three-dimensional (3D) printing method that fulfills these requirements and demonstrate its capabilities on a clinically representative cervical spine case with embedded orthopedic spinal screws. Methods: The proposed method extends the PixelPrint framework, a fused-deposition-modeling (FDM) workflow that converts clinical Digital Imaging and Communications in Medicine (DICOM) data directly into 3D-printer Geometric code (G-code) without intermediate segmentation or surface meshing, to interleaved, voxel-level deposition of two filaments: a calcium-doped polylactic acid (PLA) for soft tissue and bone, and a higher-attenuation metal-doped PLA for metallic implants. For demonstration, anonymized DICOM data of a healthy cervical spine were used to design and fabricate three matched phantoms, each with six embedded spinal screws at C4--C6: a 0% metal-infill ground-truth phantom, a 50% medium-metal-infill phantom, and an 85% high-metal-infill phantom. All phantoms were scanned on a clinical spectral CT system at 120 kVp and 1000 mAs, reconstructed at 0.67 mm slice thickness with virtual monoenergetic imaging (VMI) across 50--190 keV. Method performance was characterized by region of interest (ROI)-based Hounsfield Unit (HU) agreement with the source patient data and by the noise-independent Gumbel-distribution p-index metric. Results: The dual-filament method reproduced patient anatomy, soft-tissue contrast, and screw geometry with high fidelity. ROI HU values agreed with patient data within {+/-}25 HU for soft tissue and trabecular bone; cortical regions were underestimated owing to the current ceiling of the calcium-doped PLA used in this study. The tunable-artifact behavior was quantified as follows: the Gumbel location parameter scaled monotonically from 46.7 HU (no-metal background) to 57.1 HU (50% infill) to 90.5 HU (85% infill) for the VMI 70 keV with standard filter. High-keV VMI reconstructions substantially reduced streak and beam-hardening artifacts while preserving anatomic detail. Conclusions: The proposed dual-filament, voxel-level PixelPrint method enables the fabrication of patient-specific, multi-material CT phantoms with embedded metallic implants and controllable, ground-truth--referenced artifact intensity. Although demonstrated here in a single cervical-spine case, the workflow is anatomy- and implant-agnostic by construction and could in principle be adapted to other musculoskeletal sites (e.g., knee, hip, dental) and implant materials, providing a reproducible methodological foundation for benchmarking MAR algorithms, characterizing spectral CT performance, and validating emerging photon-counting detector systems. Keywords: 3D printing methodology; fused deposition modeling; voxel-level multi-material printing; spectral computed tomography; metal artifact reduction; phantom design; orthopedic implants; dual filament; PixelPrint.

Purpose: To compare the cross-site generalization of radiomic features and deep learning embeddings for MRI prediction of substantial lymphovascular space invasion (LVSI) in endometrial cancer. Materials and Methods: This retrospective two-center study included 206 women (mean age, 59.8 years) with endometrial cancer who underwent preoperative 3-T MRI from March 2016 to March 2023. Hospital A (n = 130) was used for development and Hospital B (n = 76) for strict external testing. T2-weighted, reduced field-of-view diffusion-weighted, and apparent diffusion coefficient images were manually segmented. Radiomic features and seed-pooled embeddings from 3D ResNet18, DenseNet121, and U-NEXtractor were modeled with elastic-net logistic regression or XGBoost. Out-of-fold Platt calibration and sensitivity-targeted thresholds were estimated using development data only. AUCs were summarized with 95% bootstrap confidence intervals. Results: External radiomics with elastic-net achieved an AUC of 0.609 (95% CI: 0.464, 0.740) and sensitivity of 0 of 12 (0%). DenseNet121 with elastic-net had the highest external AUC (0.685; 95% CI: 0.538, 0.822) but sensitivity of 3 of 12 (25%). U-NEXtractor with elastic-net detected 10 of 12 positive cases (83.3%) with specificity of 32 of 64 (50.0%) and balanced accuracy of 0.667. XGBoost showed higher apparent development performance but weaker external operating behavior. Conclusion: Under real-world cross-site MRI acquisition shift, DenseNet121 and U-NEXtractor embeddings showed better external generalization than handcrafted radiomic features for substantial LVSI prediction.

As machine learning and artificial intelligence systems are increasingly used in healthcare, rigorous evaluation of their classification performance has become critical. The F1 and F{beta} scores are widely adopted metrics for assessing performance in imbalanced biomedical data. Recently, we introduced psF1, a unified statistical framework for inference and study design for single and comparative F1 and F{beta} scores under the assumption of independent classifiers. In practice, however, benchmarking two classifiers on the same dataset creates a correlated paired setting. Ignoring this intrinsic dependency leads to overestimation of the standard error and a substantial loss of statistical power. To address this, we develop psF1pair, an advanced framework for statistical inference and power analysis that explicitly accounts for correlations between classifier pairs. Extensive simulation studies demonstrate the performance of psF1pair, and its utility is further illustrated through application to a real-world imaging classification system. As expected, higher correlation between classifiers yields narrower confidence intervals and enhanced statistical power. A freely available R package is provided to facilitate implementation, supporting accurate evaluation and study design for predictive and classification models in biomedical research.

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings

Background: Actigraphy-derived rest-activity rhythm (RAR) features are widely used to characterize clinical states in bipolar disorder (BD). Both mean levels and temporal variability of these features have been associated with mood episodes; however, variability measures are often statistically coupled with the mean, particularly in skewed distributions. This raises a question as to whether variability reflects a separate characteristic of the data or whether the observed association arises from statistical properties of the data. Objective: In this study, we aim to determine whether temporal variability of actigraphy-derived RAR features provides standalone information on mood episodes in BD beyond mean activity levels after accounting for mean-variance dependence. Methods: We analyzed actigraphy data from a subset of 72 participants with BD drawn from a larger longitudinal study, extracting 22 daily RAR features aggregated weekly as sample mean (MEAN) and within-week temporal variability computed as sample standard deviation (VAR). Variance-stabilizing transformations (Box-Cox or Yeo-Johnson) were applied to the entire study cohort to reduce mean-variance dependence. Associations with mood episodes and remission (mania: n=34; depression: n=58 annotated participants) were evaluated using generalized linear mixed-effects models with a logistic link function, including univariate (MEAN or VAR) and multivariate (MEAN+VAR) specifications, assessed by likelihood-based metrics and the area under the receiver operating characteristic curve (AUC). Results: Transformations reduced mean-absolute correlations from 0.43 to below 0.06. Temporal variability remained significantly associated with clinical state for 11/22 RAR features in mania and 16/22 features in depression, with all significant associations remaining after false discovery rate correction (p<0.05). Joint models showed modest incremental gains (AUC 3%-4% overall; up to 12% in mania, 7% in depression), with absolute performance remaining limited (AUC 0.50-0.66). In both mania and depression, nearly all significant variability-based regressors contributed incremental information beyond mean-based models. Only sleep duration and activity changes around wake time (+-1 hour), did not improve discrimination between mania and remission. Conclusions: Temporal variability in RAR features can be considered a standalone state marker of mood episodes not captured by mean activity. We found it to be more consistently associated with depression than mania. Its incremental discriminative contribution is modest, suggesting greater utility within multivariate or multimodal frameworks.

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.

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.

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.

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.

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.

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

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.

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: Large language models (LLMs) struggle with dynamic, longitudinal clinical reasoning. We developed a Multi-Stage Iterative Clinical Reasoning Agent framework to address this gap and systematically decouple the clinical efficacy of static retrieval-augmented generation (RAG) from dynamic self-refinement. Methods: Ten complex longitudinal nephrology cases, rigorously selected via a modified Delphi consensus technique, were blindly evaluated by four board-certified nephrologists and a multi-model AI panel. We compared three architectures across nine cognitive steps: (Model A) a baseline frontier LLM, (Model B) an LLM augmented with static guideline-based RAG, and (Model C) our proposed multi-agent framework featuring RAG integrated with iterative self-critique and refinement. Results: In human evaluations (20-point scale), Model C (mean 17.2, SD 1.2) significantly outperformed both Model A (16.1, 1.3) and Model B (16.2, 1.2) (P < 0.001). Implementing static RAG (Model B) yielded no significant improvement over the baseline. Automated AI evaluations (15-point scale) corroborated these findings: Model C (14.7, 0.6) outscored Model A (14.2, 0.9, P < 0.001) and Model B (14.3, 0.9, P = 0.01). While monolithic models exhibited severe score degradations in planning-heavy tasks such as dynamic differential diagnoses, the multi-agent framework effectively intercepted error cascades, achieving significantly higher diagnostic accuracy (mean 17.6, P = 0.019) and therapeutic management scores (17.3, P = 0.002). Conclusions: Static knowledge retrieval alone fails to enhance frontier LLM performance in longitudinal medical reasoning. Distributing clinical workflows into a multi-agent dynamic refinement pipeline significantly improves reasoning completeness, intercepts error cascades, and safely resolves planning bottlenecks in complex patient care.

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: 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.

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 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.