Chronic kidney disease (CKD) is characterized by persistent inflammation and tubulointerstitial fibrosis leading to end-stage renal disease. Transient receptor potential canonical 6 (TRPC6) channel inhibition mitigates tubular injury and renal fibrosis in murine models of unilateral ureteral obstruction (UUO) and 2-month chronic post-ischemia-reperfusion injury (2m post-I/R). Through integrated analysis of single-cell-RNA-sequencing (scRNA-Seq) data from UUO mice treated with the selective TRPC6 inhibitor SH045, here the renoprotective cell composition and cell type-specific transcriptional programs are defined. We explored translational aspects by conducting an in-depth scRNA-Seq analysis of kidney samples from patients with CKD. These results reveal global transcriptional shifts with a dramatic diversification of inflammatory cells, endothelial cells and fibroblasts. Notably, a distinct subpopulation of novel endothelial cells is delineated, which is termed ECRIN, that regulate inflammatory networks implicating VEGF and GAS signaling pathways. The data also indicates that inhibition of TRPC6 channels triggers a Prnp transcription factor regulatory network, which contributes to the alleviation of renal fibrosis. The key findings are supported at the protein level by immunofluorescence and western blot analysis. We observed similar patterns in the chronic 2m postI/R injury model. These findings provide novel insights into the potential therapeutic benefits of TRPC6 inhibition in CKD.

Aging is a complex process that significantly contributes to age-related diseases and poses significant challenges for effective interventions, with few holistic anti-aging approaches successfully reversing its signs. Heterochronic parabiosis studies illuminated the potential for rejuvenation through blood-borne factors, yet the specific drivers including underlying mechanisms remain largely unknown and until today insights have not been successfully translated to humans. In this study, we were able to recreate rejuvenation of the human skin via systemic factors using a microphysiological system including a 3D skin model and a 3D bone marrow model. Addition of young human serum in comparison to aged human serum resulted in an improvement of proliferation and a reduction of the biological age as measured by methylation-based age clocks in the skin tissue. Interestingly, this effect was only visible in the presence of bone marrow-derived cells. Further investigation of the bone marrow model revealed changes in the cell population in response to young versus aged human serum treatment. Using proteome analysis, we identified 55 potential systemic rejuvenating proteins produced by bone marrow-derived cells. For seven of these proteins, we were able to verify a rejuvenating effect on human skin cells using hallmarks of aging assays, supporting their role as systemic factors rejuvenating human skin tissue.

Post-COVID syndrome affects at least 10% of individuals recovering from COVID-19. Currently, there is no causal treatment. This retrospective cohort study aimed to evaluate the potential of traditional Chinese medicine (TCM) in treating post-COVID symptoms. TCM physicians in Germany and Austria completed online questionnaires to retrospectively record symptoms, treatment approaches, and outcomes for patients diagnosed with post-COVID. Nine physicians collected data from 79 patients (65% female, 47 ± 16 SD). The most common TCM treatments for post-COVID were acupuncture (n = 66; 85%), Chinese pharmacological therapy (n = 61; 77%), and Chinese dietary counseling (n = 32; 41%). After an average of 7 ± 4 TCM consultations, physicians rated global symptom improvement as 62% ± 29%. Significant alleviation from the start of TCM treatment was observed in major symptoms, such as fatigue (P < .001), impaired physical performance (P < .001), and exertional dyspnea (P < .001). TCM treatment was associated with significant improvements in post-COVID symptoms, warranting further evaluation through randomized controlled studies.

BACKGROUND: Alterations in pre-mRNA splicing are crucial to the pathophysiology of various diseases. However, the effects of alternative splicing of mRNA on podocytes in hypertensive nephropathy are still unknown. The Sys_CARE project aimed to identify alternative splicing events involved in the development and progression of glomerular hypertension. METHODS: Murine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA sequencing and several bioinformatic alternative splicing tools. RESULTS: Using transcriptomic and proteomic analysis, we identified significant changes in gene expression and protein abundance due to mechanical stretch. RNA-Seq identified over 3,000 alternative spliced genes after mechanical stretch, including all types of alternative splicing events. Among these, 17 genes exhibited an alternative splicing event across four different splicing analysis tools. From this group, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein essential for podocyte function. We identified two Shroom3 isoforms with significant expression changes under mechanical stretch, which was validated by qRT-PCR and in situ hybridization. Additionally, we observed an expression switch of two Myl6 isoforms after mechanical stretch, accompanied by an alteration in the C-terminal amino acid sequence. CONCLUSIONS: A comprehensive RNA-Seq analysis of mechanically stretched podocytes identified novel potential podocyte-specific biomarkers and highlighted significant alternative splicing events, notably in the mRNA of Shroom3 and Myl6.

Background/Objectives: The prediction of cancer types is primarily reliant on driver genes and their specific mutations. The advancement in novel omics technologies has led to the acquisition of additional genetic data. When integrated with artificial intelligence models, there is considerable potential for this to enhance the accuracy of cancer diagnosis. As mutational signatures can provide insights into repair mechanism malfunctions, they also have the potential for more accurate cancer diagnosis. Methods: First, we compared unsupervised and supervised machine learning approaches to predict cancer types. We employed deep and artificial neural network architectures with an explainable component like layerwise relevance propagation to extract the most relevant features for the cancer-type prediction. Ten-fold cross-validation and an extensive grid search were used to optimize the neural network architecture using driver gene mutations, mutational signatures and topological mutation information as input. The PCAWG dataset was used as input to discriminate between 17 primary sites and 24 cancer types. Results: Overall, our approach showed that the most relevant mutation information to discriminate between cancer types is increased by >10% using the whole genome or intergenic and intronic genome regions instead of exome information. Furthermore, the most relevant features for most cancer types, except for two, are in the mutational signatures and not the topological mutation information. Conclusions: Informative mutational signatures outperformed the prediction of cancer types in comparison to driver gene mutations and added a new layer of diagnostic information. As the degree of information within the mutational signatures is not solely based on the frequency of occurrence, it is even possible to separate cancer types from the same primary site by the different relevant mutations. Furthermore, the comparison of informative mutational signatures allowed the cancer-type assignment of specific impaired repair mechanisms.

Estimating the magnitude of placebo responses across pharmacological and nonpharmacological trials is important for understanding their influence on trial outcomes. Yet, the extent to which more intense placebo interventions like sham acupuncture yield larger analgesic responses than placebo pills, and the factors predicting these responses, remain unclear. This meta-analysis investigated the magnitude and predictors of placebo analgesia responses in pharmacological vs acupuncture trials. Analyses included individual patient data from the placebo arm of 11 randomized controlled trials (RCTs): 9 pharmacological RCTs using placebo pills (N = 2021) and 2 acupuncture RCTs using sham acupuncture (N = 747). All trials were conducted in patients with chronic nociceptive pain (osteoarthritis, N = 2068; low back pain, N = 700). The placebo response was calculated as the change in pain intensity (0-100) between baseline and week 12. A random effects model demonstrated that placebo pills and patients with osteoarthritis exhibited smaller placebo responses than sham acupuncture and patients with low back pain (both P < 0.001, marginal effects). A mixed effects model showed that route of administration interacted significantly with baseline pain, premature termination, and the presence of adverse events. Together, predictors explained 20% to 25% of the individual variance in placebo responses, whereas 75% to 80% remained unaccounted for. In summary, sham acupuncture accounted for slightly larger placebo responses than placebo pills. Since basic trial and patient parameters explained only a small portion of this variability, we might need to start considering the patient's perception of the treatment—including cognition and emotions—to better predict placebo analgesia responses.

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.