Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, β = −0.14 standard deviations, SDs, 95% confidence intervals, CI: −0.21, −0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, β = −0.22 SDs, 95% CI: −0.35, −0.09). Effects were comparable to hospital presentation during illness (N = 281, β = −0.31 SDs, 95% CI: −0.44, −0.18), and 10 years age difference (60–70 years vs. 50–60 years, β = −0.21 SDs, 95% CI: −0.30, −0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. 10.13039/100011721 Chronic Disease Research Foundation , 10.13039/100010269 Wellcome Trust , 10.13039/501100000272 National Institute for Health and Care Research , 10.13039/501100000265 Medical Research Council , 10.13039/501100000274 British Heart Foundation , 10.13039/501100000320 Alzheimer's Society , European Union, COVID-19 Driver Relief Fund, French National Research Agency.

Adverse childhood experiences (ACEs) are associated with depression and systemic inflammation in adults. However, limited longitudinal research has tested these relationships in children and young people, and it is unclear whether inflammation is an underlying mechanism through which ACEs influence depression. We examined the longitudinal associations of several ACEs across different early-life periods with longitudinal patterns of early-life inflammation and depression in young adulthood and assessed the mediating role of inflammation. The data came from the Avon Longitudinal Study of Parents and Children (N = 3931). ACEs from the prenatal period through to adolescence were operationalised using cumulative scores, single adversities, and dimensions derived through factor analysis. Inflammation (C-reactive protein) was measured on three occasions (9–18 years) and depressive symptoms were ascertained on four occasions (18–23 years). Latent class growth analysis was employed to delineate group-based trajectories of inflammation and depression. The associations between ACEs and the inflammation/depression trajectories were tested using multinomial logistic regression analysis. Most types of ACEs across all early-life periods were associated with elevated depression trajectories, with larger associations for threat-related adversities compared with other ACEs. Bullying victimisation and sexual abuse in late childhood/adolescence were associated with elevated CRP trajectories, while other ACEs were unrelated to inflammation. Inflammation was also unrelated to depression and did not mediate the associations with ACEs. These results suggest that ACEs are consistently associated with depression, whereas the associations of inflammation with ACEs and depression are weak in young people. Interventions targeting inflammation in this population might not offer protection against depression.

Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary’s Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George’s Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients’ performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary’s Hospital TBI clinic in London (UK). The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.99; p < 0.001). Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. This work was funded by a 10.13039/501100009127 National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).

Large public datasets of the human microbiome now exist but combining them for large-scale analysis is difficult due to a lack of standardization. We developed curatedMetagenomicData (cMD) 3, a uniformly processed collection of over 22,000 human microbiome samples with manually curated metadata from 94 studies and 42 countries. This large and diverse resource allows for meta-analysis of the links between microbes and human health. Through meta-analysis, we identified hundreds of microbial species and thousands of microbial functions significantly associated with a person’s sex, age, body mass index, and disease status, and catalog these as references. We developed an “oral enrichment score” (OES) based on the relative abundance of bacteria typically found in the oral cavity and not in the gut. Higher OES in the gut is a consistent feature in individuals with disease, suggesting that the relative abundance of oral bacteria in the gut is a simple and quantifiable signal of altered microbiome health. These analyses identify modest but widely shared patterns in human microbiomes, serving as a reproducible and readily updatable reference.

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson’s Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.

AI agents promise to empower biomedical discovery, but realizing this promise requires the ability to complete transparent, long-horizon analyses using tools. Agents must make intermediate decisions explicit, and validate each decision and output against data and tool constraints as the analysis unfolds. We present M edea , an AI agent that takes an omics objective and executes a transparent multi-step analysis using tools. M edea comprises four modules: research planning with context and integrity verification, code execution with pre- and post-run checks, literature reasoning with evidence-strength assessment, and a consensus stage that reconciles evidence across datasets, tools, and literature. M edea uses 20 tools spanning single-cell and bulk transcriptomic datasets, cancer vulnerability maps, pathway knowledge bases, and machine learning models. We evaluate M edea across 5,679 analyses in three open-ended domains: target identification across five diseases and cell type contexts (2,400 analyses), synthetic lethality reasoning in seven cell lines (2,385 analyses), and immunotherapy response prediction in bladder cancer (894 patient analyses). In evaluations that vary large language models, tool sets, omics objectives, and agentic modules, M edea improves the performance of existing approaches by up to 46% for target identification, 22% for synthetic lethality, and 24% for immunotherapy response prediction, while maintaining low failure rates and calibrated abstention. M edea shows that verification-aware AI agents improve performance by producing transparent analyses, not simply more efficient workflows.