Functional revascularization is key to stroke recovery and requires remodeling and regeneration of blood vessels around which is located the brain’s only stromal compartment. Stromal progenitor cells (SPCs) are critical for tissue regeneration following injury in many organs, yet their identity in the brain remains elusive. Here we show that the perivascular niche of brain SPCs includes pericytes, venular smooth muscle cells and perivascular fibroblasts that together help cerebral microvasculature regenerate following experimental stroke. Ischemic injury triggers amplification of pericytes and perivascular fibroblasts in the infarct region where they associate with endothelial cells inside a reactive astrocyte border. Fate-tracking of Hic1+ SPCs uncovered a transient functional and transcriptional phenotype of stroke-activated pericytes and perivascular fibroblasts. Both populations of these cells remained segregated, displaying distinct angiogenic and fibrogenic profiles. Therefore, pericytes and perivascular fibroblasts are distinct subpopulations of SPCs in the adult brain that coordinate revascularization and scar formation after injury.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation .

Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures’ ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n = 8, 104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.

Seit Inkrafttreten des Präventionsgesetzes (PrävG) im Jahr 2015 wurden bundesweit Projekte der gesetzlichen Krankenversicherungen (GKV) zur Gesundheitsförderung und Prävention (GfP) initiiert. Die Messung der Wirkung von GfP wurde im PrävG nicht explizit formuliert, mit der Folge, dass in den letzten 10 Jahren externe Evaluationsstudien nicht flächendeckend und systematisch in GKV-geförderten Projekten zur GfP eingesetzt wurden. Dennoch gibt es einige Beispiele von Evaluationsstudien, die Effekte von GKV-geförderten Projekten der GfP auf relevante Zielgruppen in verschiedenen Lebenswelten empirisch untersucht haben, vor allem in der kommunalen Gesundheitsförderung. Evaluation von Projekten der GfP in Kommunen wurden sowohl in Städten als auch im ländlichen Raum durchgeführt. Evaluationsforschung in GKV-geförderten Projekten der GfP ist auch 10 Jahre nach Inkrafttreten des Präventionsgesetzes kein wissenschaftlicher Selbstzweck. Evidenzbasierung und Wirkungsorientierung tragen - auch vor dem Hintergrund eines zunehmenden legitimatorischen (Kosten-)Drucks - zur Nachhaltigkeit von Projekten der GfP bei. Im folgenden Beitrag wird ein Best-Practice-Beispiel vorgestellt, mit dem Ziel aufzuzeigen, wie externe Begleitevaluationen in GKV-geförderten Projekten der kommunalen Gesundheits-förderung integriert werden können.

Renal cell carcinoma (RCC) is the most common form of kidney cancer, known for its immune evasion and resistance to chemotherapy. Evidence indicates that the SARS-CoV-2 virus may worsen outcomes for RCC patients, as well as patients with diminished renal function. Evidence suggests that the SARS-CoV-2 virus may exacerbate outcomes in RCC patients and those with impaired renal function. This study explored the unidirectional effects of RCC cells and the SARS-CoV-2 spike protein (S protein) on human renal proximal tubule epithelial cells (RPTECs) using a microphysiological approach. We co-cultured RCC cells (Caki-1) with RPTEC and exposed them to the SARS-CoV-2 S protein under dynamic 3D conditions. The impact on metabolic activity, gene expression, immune secretions, and S protein internalization was evaluated. The SARS-CoV-2 S protein was internalized by RPTEC but poorly interacted with RCC cells. RPTECs exposed to RCC cells and the S protein exhibited upregulated expression of genes involved in immunogenic pathways, particularly those related to antigen processing and presentation via the major histocompatibility complex I (MHCI). Additionally, increased TNF-α secretion suggested a pro-inflammatory response. Metabolic shifts toward glycolysis were observed in RCC co-culture, while the presence of the S protein led to minor changes. The presence of RCC cells amplified the immune-modulatory effects of the SARS-CoV-2 S protein on the renal epithelium, potentially exacerbating renal inflammation and fostering tumor-supportive conditions. These findings suggest that COVID-19 infections can impact renal function in the presence of kidney cancer.

The tricellular tight junctions are crucial for the regulation of paracellular flux at tricellular junctions, where tricellulin (MARVELD2) and angulins (ILDR1, ILDR2, or LSR) are localized. The role of ILDR2 in podocytes, specialized epithelial cells in the kidney, is still unknown. We investigated the role of ILDR2 in glomeruli and its influence on blood filtration. Western blots, single-cell RNA sequencing (scRNA-seq), and superresolution microscopy showed a strong expression of ILDR2 in podocytes that colocalized with the podocyte-specific claudin CLDN5. Co-immunoprecipitation revealed that ILDR2 interacts with CLDN5. In glomerulopathies, induced by nephrotoxic serum and by desoxycorticosterone acetate (DOCA)-salt heminephrectomy, ILDR2 was strongly up-regulated. Furthermore, Ildr2 knockout mice exhibited glomerular hypertrophy and decreased podocyte density. However, they did not develop effacement of podocyte foot processes or proteinuria. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic analysis of isolated glomeruli showed an increase in matrix proteins, such as fibronectin and collagens. This suggests a protective role of ILDR2 in glomerulopathies.

The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic-mesenchymal cell states in neuroblastoma cancer.