In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.

The advent of micro-physiological systems (MPS) in biomedical research has enabled the introduction of more complex and relevant physiological into in vitro models. The recreation of complex morphological features in three-dimensional environments can recapitulate otherwise absent dynamic interactions in conventional models. In this study we developed an advanced in vitro Renal Cell Carcinoma (RCC) that mimics the interplay between healthy and malignant renal tissue. Based on the TissUse Humimic platform our model combines healthy renal proximal tubule epithelial cells (RPTEC) and RCC. Co-culturing reconstructed RPTEC tubules with RCC spheroids in a closed micro-perfused circuit resulted in significant phenotypical changes to the tubules. Expression of immune factors revealed that interleukin-8 (IL-8) and tumor necrosis factor-alfa (TNF-α) were upregulated in the non-malignant cells while neutrophil gelatinase-associated lipocalin (NGAL) was downregulated in both RCC and RPTEC. Metabolic analysis showed that RCC prompted a shift in the energy production of RPTEC tubules, inducing glycolysis, in a metabolic adaptation that likely supports RCC growth and immunogenicity. In contrast, RCC maintained stable metabolic activity, emphasizing their resilience to external factors. RNA-seq and biological process analysis of primary RTPTEC tubules demonstrated that the 3D tubular architecture and MPS conditions reverted cells to a predominant oxidative phosphorylate state, a departure from the glycolytic metabolism observed in 2D culture. This dynamic RCC co-culture model, approximates the physiology of healthy renal tubules to that of RCC, providing new insights into tumor-host interactions. Our approach can show that an RCC-MPS can expand the complexity and scope of pathophysiology and biomarker studies in kidney cancer research.

Podocyte detachment due to mechanical stress is a common issue in hypertension-induced kidney disease. This study highlights the role of zyxin for podocyte stability and function. We have found that zyxin is significantly up-regulated in podocytes after mechanical stretch and relocalizes from focal adhesions to actin filaments. In zyxin knockout podocytes, we found that the loss of zyxin reduced the expression of vinculin and VASP as well as the expression of matrix proteins, such as fibronectin. This suggests that zyxin is a central player in the translation of mechanical forces in podocytes. In vivo, zyxin is highly up-regulated in patients suffering from diabetic nephropathy and in hypertensive DOCA-salt treated mice. Furthermore, zyxin loss in mice resulted in proteinuria and effacement of podocyte foot processes that was measured by super resolution microscopy. This highlights the essential role of zyxin for podocyte maintenance in vitro and in vivo, especially under mechanical stretch.

Arabidopsis contains hundreds of ribosomal DNA copies organized within the nucleolar organizing regions (NORs) in chromosomes 2 and 4. There are four major types of variants of rDNA, VAR1-4, based on the polymorphisms of 3’ external transcribed sequences. The variants are known to be differentially expressed during plant development. We created a mutant by the CRISPR-Cas9-mediated excision of ~ 25 nt from predominantly NOR4 ribosomal DNA copies, obtaining mosaic mutational events on ~ 5% of all rDNA copies. The excised region consists of P-loop and Helix-82 segments of 25S rRNA. The mutation led to allelic, dosage-dependent defects marked by lateral root inhibition, reduced size, and pointy leaves, all previously observed for defective ribosomal function. The mutation in NOR4 led to dosage compensation from the NOR2 copies by elevated expression of VAR1 in mutants and further associated single-nucleotide variants, thus, resulting in altered rRNA sub-population. Furthermore, the mutants exhibited rRNA maturation defects specifically in the minor pathway typified by 32S pre-rRNA accumulation. Density-gradient fractionation and subsequent RT-PCR of rRNA analyses revealed that mutated copies were not incorporated into the translating ribosomes. The mutants in addition displayed an elevated autophagic flux as shown by the autophagic marker GFP-ATG8e, likely related to ribophagy.

BACKGROUND: FSGS affects the complex three-dimensional morphology of podocytes, resulting in loss of filtration barrier function and the development of sclerotic lesions. Therapies to treat FSGS are limited, and podocyte-specific drugs are unavailable. To address the need for treatments to delay or stop FSGS progression, researchers are exploring the repurposing of drugs that have been approved by the US Food and Drug Administration (FDA) for other purposes. METHODS: To identify drugs with potential to treat FSGS, we used a specific zebrafish screening strain to combine a high-content screening (HCS) approach with an in vivo model. This zebrafish screening strain expresses nitroreductase and the red fluorescent protein mCherry exclusively in podocytes (providing an indicator for podocyte depletion), as well as a circulating 78 kDa vitamin D-binding enhanced green fluorescent protein fusion protein (as a readout for proteinuria). To produce FSGS-like lesions in the zebrafish, we added 80 µ M metronidazole into the fish water. We used a specific screening microscope in conjunction with advanced image analysis methods to screen a library of 138 drugs and compounds (including some FDA-approved drugs) for podocyte-protective effects. Promising candidates were validated to be suitable for translational studies. RESULTS: After establishing this novel in vivo HCS assay, we identified seven drugs or compounds that were protective in our FSGS-like model. Validation experiments confirmed that the FDA-approved drug belinostat was protective against larval FSGS. Similar pan-histone deacetylase inhibitors also showed potential to reproduce this effect. CONCLUSIONS: Using an FSGS-like zebrafish model, we developed a novel in vivo HCS assay that identified belinostat and related pan-histone deacetylase inhibitors as potential candidates for treating FSGS.

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.