Inflammation is a biological phenomenon beneficial for homeostasis, but it is unfavorable if dysregulated. Although major progress has been made in characterizing inflammation in specific diseases, a global, holistic understanding is still elusive. This is particularly intriguing, considering its function for human health and the potential for modern medicine if fully deciphered. In this study, we leveraged advances in single-cell transcriptomics to delineate inflammatory processes of circulating immune cells during infection, immune-mediated inflammatory diseases and cancer. Our single-cell atlas of more than 6.5 million peripheral blood mononuclear cells from 1,047 patients (56% female, 43% male) and 19 diseases allowed us to learn a comprehensive model of inflammation in circulating immune cells. The atlas expands current knowledge of the biology of inflammation of immune-mediated diseases, acute and chronic inflammatory diseases, infections and solid tumors and lays the foundation to develop a disease classification framework using unsupervised as well as explainable machine learning. Beyond a disease-centered analysis, we charted altered activity of inflammatory molecules in peripheral blood cells, depicting discriminative inflammation-related genes to further understand mechanisms of inflammation. We present a rich resource for the community and lay the groundwork for learning a classifier for inflammatory diseases, presenting cells in circulation as living biomarkers.
There are no robust, reliable and easy to administer tests to screen for mild cognitive impairment (MCI) in people living with diabetes. Since the retina is ontogenically brain-derived, we hypothesised that retinal biomarkers could be used, alone or in combination with other simple tests, to screen for MCI in people with diabetes. Baseline data from participants screened for RECOGNISED, a Horizon 2020-funded European project, were analysed. Main eligibility criteria for RECOGNISED included age ≥65 years, type 2 diabetes of over 5 years standing, no previous history of stroke or neurodegenerative disease, and no overt diabetic retinopathy or only mild-to-moderate non-proliferative diabetic retinopathy. Baseline characteristics of participants, including scores from the Montreal Cognitive Assessment test (MoCA) and Self-Administered Gerocognitive Examination, the Diabetes Specific Dementia Risk Score (DSDRS) and ophthalmological endpoints gathered from standardised seven field colour fundus photography, spectral domain optical coherence tomography, microperimetry and a hand-held portable electroretinography device (RETeval), were obtained and used in the work presented here as potential screening predictors for presence of MCI. MCI and normocognition (NC) were determined based on a full neuropsychological test battery and the Clinical Dementia Rating score. A stepwise selection of variables, based on Akaike’s information criterion, and logistic regression models for predicting MCI were undertaken. Area under the receiver-operating characteristic curve analyses were used to predict the probability of the presence of MCI as well as sensitivity and specificity cut-off points. A total of 313 people living with diabetes (128 with NC and 185 with MCI) were included. People with diabetes with MCI were older (p=0.006) and had fewer years of education (p<0.001), lower retinal sensitivity (p=0.01) and less capacity of gaze fixation (p≤0.001) than those with NC. Statistically significant differences in pupillary area ratio (p=0.002) and photopic b-wave amplitude (p=0.03) were detected between people with diabetes with NC and with MCI. Multivariable logistic regression showed that the best model to identify people with diabetes with MCI was that combining retinal sensitivity, gaze fixation, photopic b-wave amplitude and pupillary size change following stimulation, years of education, DSDRS and MoCA score, with an AUC of 0.84 (sensitivity 79.9, specificity 79.0). The visuo-construction domain was the most affected in people with diabetes with MCI and its impairment was independently related to retinal sensitivity and gaze fixation. The assessment of retinal neurodysfunction in combination with simple clinical variables appears useful to identify people with diabetes with MCI. This strategy could optimise current screening of MCI in people living with diabetes.
OBJECTIVE:To investigate the use of retinal microvascular parameters, as determined by color fundus photographs (CFP), to detect individuals with type 2 diabetes with mild cognitive impairment (MCI). METHODS:The current study is based on cross sectional data obtained prospectively in RECOGNISED, a European Union-funded multicenter observational study involving individuals 65 years of age or older with type 2 diabetes. The diagnosis of MCI was established based on cognitive screening, followed by a detailed neuropsychological test battery. CFP were analyzed to quantify retinal vessel width, arteriovenous ratio, tortuosity, and fractal dimension. Crude and multivariable logistic regression models were used to investigate the association between MCI and retinal microvascular parameters and the ability of the latter to identify individuals with MCI using both eyes in our analysis if eligible. RESULTS:We included 578 eyes of 307 individuals with type 2 diabetes of whom 182 had MCI and 125 had normal cognition. Median age was 72 (IQR 69-77) years, 65 % were male, and median diabetes duration was 19 (IQR 12-24) years. We found arteriolar tortuosity was associated with MCI in the multivariable analysis (OR 1.44 [95 % CI 1.08-1.92]; the less the tortuosity the higher the risk). When estimating the discriminative ability of the retinal microvascular parameters, we found an AUC of 0.73. CONCLUSION:In people with type 2 diabetes, a lower retinal arteriolar tortuosity was associated with MCI. In addition, we found retinal microvascular parameters to have an acceptable discrimination ability when paired with patient's characteristics.
We present the TopBrain 2025 Challenge, the first benchmark for fine-grained multiclass segmentation of the whole brain vasculature in both computed tomography angiography (CTA) and magnetic resonance angiography (MRA). Building on the TopCoW challenge, TopBrain scales vessel annotation from the Circle of Willis to the entire brain, introducing a dataset of 90 annotated volumes across 48 landmark vessel classes spanning arterial and venous systems, of which 50 training volumes are publicly released. Vessel definitions were consolidated from established neuroanatomical references into a unified annotation scheme, and vessel caliber measurements along the centerline are reported for the first time across the whole brain vascular anatomy. To address the unique challenges of multiclass brain vessel segmentation, we propose an evaluation framework that accounts for detection in segmentation performance, assesses anatomical plausibility, and introduces novel contamination metrics that characterize inter-class prediction errors. Fifteen teams from over 220 registered participants submitted algorithms to the benchmark. The top-performing teams built on nnUNet with principled system design choices, achieving around 80% Dice scores, near-zero invalid neighbor counts, over 60% F1 scores for side-road vessels, and below 18% foreground contamination ratio. Larger vessels are easier to segment, while smaller and more complex vessels remain the true bottleneck. The annotated datasets and podium-finish algorithms are made publicly available on Zenodo. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of University Hospital of Zurich gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The TopBrain training data of 50 annotated volumes is released in our public Zenodo repository at https://zenodo.org/records/16878417. Helmut Horten Foundation European Unions Horizon Europe research and innovation programme GEMINI project, 101136438 Ministry of Trade, Industry and Energy, RS-2025-02221011 Institute for Information communication Technology Planning and Evaluation, RS-2022-II220959 Dutch Research Council, https://ror.org/04jsz6e67, 21299
