Neuro-Oncology

Background: Glioblastoma remains resistant to most immune-based therapies. Surgery may create a perioperative window in which systemic immune activation and tumor antigen release intersect. We evaluated whether COVID-19 vaccination shortly before first glioblastoma surgery was associated with survival. Methods: We performed a retrospective single-center cohort study of adults with newly diagnosed glioblastoma undergoing initial biopsy or resection from 2021 to 2025. The primary exposure was documented COVID-19 vaccination within 100 days before first tumor surgery. Overall survival was analyzed from surgery using Kaplan-Meier and Cox models, with 1:1 propensity matching and sensitivity analyses addressing treatment completion, calendar time, surgical selection, steroid exposure, immune-cell variables, COVID severity, and negative-control vaccination. Results: The cohort included 187 patients: 64 perioperatively vaccinated and 123 non-perioperative comparators. Among vaccinated patients, 59/64 (92.2%) received mRNA vaccines; median vaccination-to-surgery interval was 81 days (IQR 71-90). Median overall survival was 743 days in vaccinated patients versus 318 days in comparators (unmatched HR 0.48, 95% CI 0.30-0.76; p=0.002). After 1:1 matching, median survival was 743 versus 349 days (HR 0.52, 95% CI 0.34-0.80). Sensitivity analyses accounting for adjuvant therapy, surgery year, extent of resection, steroid exposure, immune-cell measures, and COVID hospitalization were directionally consistent. Influenza vaccination was not associated with survival. Conclusions: COVID-19 vaccination within 100 days before first glioblastoma surgery was associated with longer overall survival. These findings identify perioperative vaccination timing as a potentially relevant and modifiable variable in glioblastoma outcomes.

Objective: To compare first-line emergency department (ED) treatment classes for acute headache on short-term all-cause ED return and index admission across two independent health systems. Background: ED trials of acute headache treatment are judged on in-ED pain relief, a documented endpoint that is recorded incompletely and shifts with the scoring rule, and is a weak surrogate for what happens after discharge. All-cause ED return after an index headache visit (any subsequent ED encounter within the window) has not been used to compare first-line treatments at scale, and society guidance favors dopamine-receptor antagonists while recommending against routine opioids. Methods: Retrospective two-center cohort of adults treated for headache in the ED, using MIMIC-IV-ED (Beth Israel Deaconess Medical Center, 2011-2019) and MC-MED (Stanford, 2020-2022). The first-line class was the earliest qualifying acute agent. The primary contrast was opioids versus dopamine-receptor antagonists (the guideline-preferred class). Outcomes were 72-hour and 7-day all-cause ED return (among discharged patients; any subsequent ED encounter within the window) and index hospital admission. Confounding by indication was addressed with propensity overlap weighting; associations are reported as adjusted risk ratios (RRs) with bootstrap 95% CIs and E-values. Estimates were pooled with a site term and examined per site. Results: Among 13,285 treated adults (10,799 MIMIC-IV-ED; 2,486 MC-MED), opioid recipients were older and higher-acuity than dopamine-antagonist recipients (index admission 38.1% vs 16.4%). In the MIMIC-IV-ED discharged primary-contrast population, overlap weighting reduced the maximum standardized mean difference from 0.35 to 0.002; pooled and site-specific balance diagnostics are provided in the Supplement. First-line opioids remained associated with a higher 72-hour all-cause ED return (6.8% vs 3.8%; adjusted RR 1.79; 95% CI 1.31 to 2.33), 7-day return (10.7% vs 6.6%; RR 1.62; 95% CI 1.28 to 1.98), and index admission (RR 2.32; 95% CI 2.11 to 2.58, consistent with strong residual severity differences in patients selected for opioids). The direction of association was concordant across both health systems, although MC-MED return estimates were imprecise given the smaller opioid-treated discharged sample. In MIMIC-IV-ED, the cumulative all-cause return incidence by treatment class separated by day 3 and persisted through 30 days. The direction was consistent, though attenuated and no longer statistically significant, when the outcome was restricted to a headache-specific return (72-hour RR 1.31; 95% CI 0.91 to 1.88); the direction persisted for the composite of admission or 72-hour return, which does not condition on discharge but is influenced by the more confounded admission component (RR 2.16; 95% CI 1.98 to 2.39). Conclusion: Across two health systems, first-line opioid treatment for ED headache was associated with higher all-cause short-term ED return among discharged patients and higher index admission than dopamine antagonists. These observational associations reflect downstream all-cause ED utilization after an index headache visit rather than confirmed headache recurrence or treatment failure; they are consistent with guideline-concordant, opioid-sparing first-line treatment and warrant prospective confirmation. Plain Language Summary: Emergency departments treat headaches with several different medicines, but the usual way of judging which works, the pain score recorded during the visit, is often missing or inconsistent. Using two large hospital systems and a clearer outcome, whether patients came back to the emergency department for any reason, we found that patients first treated with opioids returned within 72 hours about 1.8 times as often as those given the guideline-preferred dopamine-blocking medicines and were admitted more than twice as often. These patterns pointed the same direction in both hospital systems after adjustment for the measured differences available in both databases. Because this was an observational comparison and returns were counted for any reason, the findings are consistent with using guideline-preferred non-opioid medicines first, rather than proof that opioids worsen headache.

Importance Sleep-disordered breathing (SDB) is common in childhood and is associated with attentional and behavioral impairments despite largely preserved sleep macrostructure and minimal abnormalities in conventional electroencephalographic measures. This discrepancy has contributed to the perception that sleep is relatively preserved in pediatric SDB and has limited understanding of the physiological mechanisms underlying morbidity. Objective To determine whether pediatric SDB is associated with disruption of the regional organization and homeostatic dynamics of slow-wave activity (SWA), a key physiological marker of sleep-dependent neural recovery and development. Design, Setting, and Participants Cross-sectional study of 62 children aged 4 to 12 years who underwent overnight polysomnography with high-density electroencephalography in a laboratory setting. Participants were recruited from clinical referrals and the community, spanning the full spectrum of SDB severity. Exposures SDB severity indexed by hypopnea index (HI), apnea-hypopnea index (AHI), and obstructive apnea index (OAI). Main Outcomes and Measures Regional electroencephalogram-derived SWA (0.5 to 4 Hz) topography and exponential decay parameters derived from frontal and posterior cortical regions. The frontal-to-posterior decay-rate ratio was evaluated as a summary measure of regional sleep homeostasis. Results In children with lower hypopnea index, SWA demonstrated the expected developmental pattern, with posterior predominance in younger children and a progressive shift toward a more balanced anterior-posterior distribution with age. Increasing HI was associated with attenuation or reversal of this spatial organization. Global SWA showed no meaningful association with SDB severity. In contrast, regional frontal and posterior decay parameters were strongly associated with HI (adjusted R2 = 0.53; p < 1e-6) but not OAI (adjusted R2 = 0.05; p = .95). The frontal-to-posterior decay-rate ratio showed the strongest association with HI {beta} = 4.15; 95% CI, 3.17-5.13; p < 1e-10; adjusted R2 = 0.55. Conclusions and Relevance Pediatric SDB was associated with regional disruption of slow-wave sleep homeostasis rather than global loss of deep sleep. These alterations affected both the spatial organization and temporal dynamics of SWA during a period of active cortical maturation and were not captured by conventional sleep metrics. Regional SWA dynamics may provide a developmentally sensitive marker of physiological disease burden in children with SDB.

Background: KRAS mutation status is an important biomarker in rectal cancer, with implications for prognosis and treatment response. MRI-based radiomics has emerged as a non-invasive approach for predicting tumor genotypes. However, the diagnostic performance of MRI radiomics for predicting KRAS mutation status remains unclear. This study aimed to evaluate the diagnostic accuracy of MRI radiomics for predicting KRAS mutations in rectal cancer. Methods: A systematic search of PubMed, Cochrane Library, Scopus, and Web of Science was performed through July 2025. Diagnostic test accuracy studies evaluating MRI-based radiomics or artificial intelligence models for predicting KRAS mutation status in adult patients with rectal cancer were included, using molecular testing as the reference standard. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Results: Seven studies involving 1,224 patients were included. The pooled sensitivity was 0.736 (95% CI: 0.697-0.772) and the pooled specificity was 0.645 (95% CI: 0.586-0.701). The false positive rate was 0.355 (95% CI: 0.299-0.414). The area under the hierarchical summary receiver operating characteristic curve was 0.754, with a normalized partial AUC of 0.666. Between-study heterogeneity ranged from low to moderate depending on the estimation method (I2 = 8.4%-53.3%). Conclusion: MRI radiomics demonstrates moderate diagnostic accuracy for predicting KRAS mutation status in rectal cancer and may serve as a promising non-invasive biomarker for preoperative molecular stratification. Further large-scale studies with external validation are required to confirm its clinical utility.

Motor speech disorders (MSDs) are early markers of neurological disease, but expert perceptual analysis is rarely available outside specialized centers. Automated speech analysis offers a scalable alternative, yet prior studies have not systematically compared modeling approaches or assessed clinically relevant metrics in independent datasets. This study compared static acoustic features, articulatory informed Phonet features, and self-supervised pretrained models for binary and multi label MSD classification. We trained and evaluated models on 583 speech samples using speaker level splits. Baseline models included logistic regression and Gated Recurrent Units (GRUs) trained on eGeMAPS and MFCCs. We extracted three types of Phonet derived features and evaluated pretrained HuBERT and SSAST models in frozen, partially fine-tuned, and fully fine-tuned configurations. Binary classification distinguished MSDs from controls, while multi label classification identified six MSD subtypes. Models were assessed using validation AUC, and cut points were tested on two independent datasets. Pretrained and Phonet based models substantially outperformed static acoustic features. In binary classification, HuBERT achieved the highest AUC (0.95), while compact Phonet derived GRUs achieved comparable performance (up to 0.94). These models generalized well to independent datasets, maintaining high sensitivity (0.94) and specificity (0.97). In multi label classification, Phonet models achieved the highest macro average AUC (0.86), but threshold-based subtype performance declined on unseen data. Automated MSD detection is feasible and clinically promising. Binary classification generalized well, whereas multi label classification showed limited threshold stability across datasets.

We used the September 2013 age-based rollout of the live-attenuated shingles vaccine in England as a natural experiment to estimate the effect of vaccine eligibility on shingles and dementia diagnoses in linked hospital records. Individuals born just before and after the eligibility cutoff were compared using regression discontinuity methods, with follow-up for up to eight years after programme introduction. Eligibility was associated with a clear reduction in hospital-coded shingles diagnoses (RD estimate -0.12 percentage points, 95% CI -0.153 to -0.079; p = 5.9 x 10-10), but there was no evidence of a corresponding reduction in hospital-coded dementia diagnoses (RD estimate -0.06 percentage points, 95% CI -0.40 to 0.27; p = 0.72). Results were robust across denominator definitions, diagnostic-code specifications, estimator choice, placebo cutoffs, and negative-control analyses. The dementia estimate was also close to null in an independently conducted analysis using a separately held HES extract. Comparator analyses in Welsh data with linked primary care and death data did not suggest these results were driven by our reliance on hospital data. These findings do not support a detectable intention-to-treat effect of live-attenuated shingles vaccine eligibility on hospital-coded dementia in England.

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.

Background and Objectives: Stereoelectroencephalography (SEEG) involves the implantation of intracerebral electrodes to investigate drug-resistant epilepsy. SEEG requires millimetric accuracy to ensure safety and optimal mapping. Although studies have evaluated SEEG accuracy, there is substantial variability in reporting. Here we report on implantation accuracy in a large series using the most common accuracy metrics described in the literature and perform a detailed analysis of contributing factors. Methods: SEEG implantations between 2013 and 2025 were included. Application accuracy was computed for each implanted electrode. Specifically, Euclidean, radial, depth, and angle error were calculated at both target and entry points. Correlative and multivariable analyses were conducted between each variable and error metric. Trajectories were also grouped by atlas-derived lobar target. Results: No metrics met assumptions of normality and thus we report accuracy using median with interquartile range (IQR). In a series of 3176 trajectories, median Euclidean target and entry errors were lower for robot-assisted electrodes (n=2858) at 2.19 (IQR: 1.54-2.98) mm and 1.38 (IQR: 0.89-2.01) mm respectively, compared to frame-based (n=318, p<.001) at 2.76 (IQR:1.79-3.76) mm and 2.21 (IQR: 1.42-3.32) mm. Correlation and multivariable regression analysis showed target error was positively correlated with implantation angle, scalp thickness, skull thickness, and trajectory length. Target error was also higher in obese patients. On lobar analysis, parietal lobe trajectories were the most accurate and frontal lobe trajectories were the least accurate. On temporal lobe trajectory analysis, posterior hippocampus trajectories were the most accurate and temporal pole trajectories were the least accurate. Presence of mesial temporal sclerosis also impacted accuracy. Conclusions: We present a detailed description of SEEG implantation accuracy, demonstrating the superior accuracy and speed of robot-assisted to frame-based methods. Furthermore, we analyzed how accuracy varies with specific factors from a global to trajectory level, which can be accounted for when planning SEEG implantations.

Objective: Shunt-dependent hydrocephalus is a common and costly complication of aneurysmal subarachnoid hemorrhage (aSAH), affecting up to 28% of survivors. Existing prediction tools, including the Chronic Hydrocephalus Ensuing from SAH Score (CHESS), have limited discriminative accuracy. We developed the CHECKMATE score, a clinically practical tool to improve prediction of ventriculoperitoneal shunt dependency after aSAH. Methods: In this multicenter retrospective cohort of 486 patients with aSAH from Mayo Clinic (January 1, 2006-December 31, 2021), we used multivariable logistic regression and machine learning to identify independent predictors of ventriculoperitoneal shunt placement. The CHECKMATE score was derived from 5 weighted variables: symptomatic hydrocephalus (10 points), intraventricular hemorrhage (5 points), SAH volume greater than 10 mL (3 points), neutrophil-to-lymphocyte ratio greater than 12 (2 points), and 10-year incremental age thresholds starting at older than 60 years (1 point each). Results: Of 486 patients (mean age, 56.3 years; 64.6% female), 137 (28.2%) required ventriculoperitoneal shunt placement. The CHECKMATE score achieved an area under the curve of 0.808 (compared to 0.737 for CHESS), with a sensitivity of 0.85, specificity of 0.67, and negative predictive value of 0.92 at the optimal cutoff of 14 points. Conclusions: The CHECKMATE score outperforms CHESS for predicting ventriculoperitoneal shunt dependency after aSAH and is easily used at the bedside. Its high negative predictive value helps identify low-risk patients who may benefit from earlier external ventricular drain weaning and shorter hospital stays.

Impaired cellular differentiation is a defining characteristic of myeloid malignancies and remains a major therapeutic challenge. The BRG1/Brahma-associated factor (BAF) chromatin remodeling complex, through the ATPases SMARCA4 and SMARCA2, maintains the stemness of leukemic blasts and thus represents a promising target for novel differentiation-based therapies. In a phase 1 study in advanced myeloid malignancies, the first-in-class dual SMARCA4/2 inhibitor FHD-286 combined with decitabine (DAC) was tolerated and produced an objective response rate of 12.8% (6/47) compared with no responses with FHD-286 monotherapy. To understand the basis of this activity, we integrated high-dimensional flow cytometry and single-cell genomic analyses of longitudinal bone marrow samples from responders and nonresponders. While FHD-286 monotherapy was predominantly associated with myeloid differentiation, responders to FHD-286+DAC combination therapy exhibited a range of myeloid and erythroid differentiation trajectories. FHD-286 potentiated the transcriptional impact of DAC, driving tumor clones to fully differentiate out of the immunophenotypically and transcriptionally defined blast compartment. Responders had a baseline transcriptional profile similar to that of CEBPA-mutant acute myeloid leukemia and showed further downregulation of CEBPA upon treatment. These findings reinforce tumor cell differentiation as a mechanism of response to pharmacologic SMARCA4/2 inhibition and support further evaluation of FHD-286+DAC in molecularly defined patient subsets.

The therapeutic landscape for hepatocellular carcinoma (HCC) is evolving rapidly, necessitating scalable approaches to synthesize the expanding scientific literature. We characterized thematic shifts in HCC treatment and prognosis research by conducting a retrospective bibliometric analysis of influential publications from 2023 and 2024. Using the OpenAlex database, we identified the 50 most highly cited papers from each year based on eighteen-month post-publication citation counts. Large language models were deployed to extract, normalize, and classify concepts from unstructured text into canonical topics and parent themes, enabling quantitative year-over-year frequency comparisons. Analysis of these 100 papers revealed a distinct maturation in research focus. Although broad categories like general immunotherapy remained prevalent, their relative frequency declined in favor of specific dual immune checkpoint regimens, notably CTLA-4 inhibition and the durvalumab plus tremelimumab combination. Concurrently, parent themes related to radiomics, imaging, and health systems exhibited significant growth in the 2024 cohort. These findings demonstrate a thematic transition in high-impact HCC research from foundational immuno-oncology toward optimized combination therapies and precision diagnostics. Furthermore, this study highlights the utility of artificial intelligence-driven bibliometrics for objectively tracking dynamic conceptual shifts in oncology. A web interface for exploring the data is available at https://pri.pepkio.com/.

The aim of this work is to validate patient-specific EEG baseline establishment using the e-norms method as a screening and retrospective-review tool for seizure detection in pediatric epilepsy. The method was applied to 247 seizure-free EEG recordings (263.92 hours) from 10 patients in the CHB-MIT Scalp EEG Database (ages 3-18). A composite stability metric combining first-derivative dynamics, spectral entropy, variance, and line length was computed per 2-second epoch across 23 channels. Patient-specific detection thresholds were derived from each patient's seizure-free baseline using a weighted statistical procedure. Performance was validated against 72 expert-annotated seizures (2,705 epochs) across 62 seizure files, with durations spanning 6 to 264 seconds (44-fold range). The results show that detection achieved 94.4% event-level sensitivity (68 of 72 seizures; 95% CI 86.6-97.8%) and 81.5% epoch-level sensitivity (2,204 of 2,705 epochs; 95% CI 80.0-82.9%). Eight of ten patients achieved 100% event-level sensitivity with epoch-level sensitivity ranging from 58.7% to 100.0%. Two patients showed partial event-level failures (CHB-15: 17 of 20; CHB-18: 5 of 6), with the four missed events attributable to two characterizable failure modes. Patient-specific thresholds ranged from 4.06 to 4.81 (mean 4.51 +/- 0.25); threshold variation did not correlate reliably with age or sex, confirming that no universal threshold could achieve comparable performance. Detection margins ranged from 0.88 to 1.24 times. Patient-specific e-norms achieves 94.4% event-level sensitivity for pediatric EEG seizure detection without requiring labeled seizure training data, exceeding published human expert inter-rater agreement (50-76%) and recent automated approaches in adult cohorts using behind-the-ear EEG and wearable ECG. Two characterizable failure modes account for the four missed events and inform appropriate clinical use. As a high-sensitivity screening tool complementary to real-time alarm systems, the method is ready for adult validation, prospective deployment, and head-to-head benchmarking.

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.

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

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.