ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions (https://doi.org/10.5281/zenodo.7153326). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge (https://www.isles-challenge.org/) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke

Quality control data of physiological and immunological biomarkers measured in serum and plasma

In two work packages of the MARK-AGE project, 37 immunological and physiological biomarkers were measured in 3637 serum, plasma or blood samples in five batches during a period of 4 years. The quality of the serum and plasma samples was very good as judged by the low number of biomarker measurements (only 0.2%) that were rejected because of a high hemolysis, icteria or lipemia of the samples. Using quality control samples, day-to-day and batch variations were determined. The mean inter-assay variation of the five batches were all below 8%, with an average inter-assay coefficient of variation of all biomarkers of 4.0%. Also the precision of the measurements was very good, because all measurements were between 90% and 115% of the defined target values. A possible mix-up of samples was determined by comparison of the extreme testosterone levels of men and women. It was concluded that 3% of the sample identification could be mixed-up. Considering the complex procedure from collection to analysis, including preparation, handling, shipment and storage, of the samples in the MARK-AGE project, both the quality of the samples and the quality of the measurements are very good

Density functional theory based screening of ternary alkali-transition metal borohydrides: A computational material design project

The dissociation of molecules, even the most simple hydrogen molecule, cannot be described accurately within density functional theory because none of the currently available functionals accounts for strong on-site correlation. This problem led to a discussion of properties that the local Kohn-Sham potential has to satisfy in order to correctly describe strongly correlated systems. We derive an analytic expression for the nontrivial form of the Kohn-Sham potential in between the two fragments for the dissociation of a single bond. We show that the numerical calculations for a one-dimensional two-electron model system indeed approach and reach this limit. It is shown that the functional form of the potential is universal, i.e., independent of the details of the two fragments.We acknowledge funding by the Spanish MEC (Grant No. FIS2007-65702-C02-01), “Grupos Consolidados UPV/EHU del Gobierno Vasco” (Grant No. IT-319-07), and the European Community through e-I3 ETSF project (Grant Agreement No. 211956).Peer reviewe

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA‐Epilepsy study

Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross‐sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA‐Epilepsy working group. Methods: A state‐of‐the‐art deep learning‐based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE‐HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results: Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset ( η ρ max 2 $$\eta {\mathit{\mathsf{\rho}}}_{\mathsf{max}}^{\mathsf{2}}$$ = .05) and longer epilepsy duration ( η ρ max 2 $$\eta {\mathit{\mathsf{\rho}}}_{\mathsf{max}}^{\mathsf{2}}$$ = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE‐HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy

Predictive markers related to local and systemic inflammation in severe COVID-19-associated ARDS: a prospective single-center analysis

Abstract Background As the COVID-19 pandemic strains healthcare systems worldwide, finding predictive markers of severe courses remains urgent. Most research so far was limited to selective questions hindering general assumptions for short- and long-term outcome. Methods In this prospective single-center biomarker study, 47 blood- and 21 bronchoalveolar lavage (BAL) samples were collected from 47 COVID-19 intensive care unit (ICU) patients upon admission. Expression of inflammatory markers toll-like receptor 3 (TLR3), heme oxygenase-1 (HO-1), interleukin (IL)-6, IL-8, leukocyte counts, procalcitonin (PCT) and carboxyhemoglobin (CO-Hb) was compared to clinical course. Clinical assessment comprised acute local organ damage, acute systemic damage, mortality and outcome after 6 months. Results PCT correlated with acute systemic damage and was the best predictor for quality of life (QoL) after 6 months (r = − 0.4647, p = 0.0338). Systemic TLR3 negatively correlated with impaired lung function (ECMO/ECLS: r = − 0.3810, p = 0.0107) and neurological short- (RASS mean: r = 0.4474, p = 0.0023) and long-term outcome (mRS after 6 m: r = − 0.3184, p = 0.0352). Systemic IL-8 correlated with impaired lung function (ECMO/ECLS: r = 0.3784, p = 0.0161) and neurological involvement (RASS mean: r = − 0.5132, p = 0.0007). IL-6 in BAL correlated better to the clinical course than systemic IL-6. Using three multivariate regression models, we describe prediction models for local and systemic damage as well as QoL. CO-Hb mean and max were associated with higher mortality. Conclusions Our predictive models using the combination of Charlson Comorbidity Index, sex, procalcitonin, systemic TLR3 expression and IL-6 and IL-8 in BAL were able to describe a broad range of clinically relevant outcomes in patients with severe COVID-19-associated ARDS. Using these models might proof useful in risk stratification and predicting disease course in the future. Trial registration The trial was registered with the German Clinical Trials Register (Trial-ID DRKS00021522, registered 22/04/2020)