Active regression model for clinical grading of COVID-19.

Background: In the therapeutic process of COVID-19, the majority of indicators that physicians have for assisting treatment have come from clinical tests represented by proteins, metabolites, and immune levels in patients' blood. Therefore, this study constructs an individualized treatment model based on deep learning methods, aiming to realize timely intervention based on clinical test indicator data of COVID-19 patients and provide an important theoretical basis for optimizing medical resource allocation. Methods: This study collected clinical data from a total of 1,799 individuals, including 560 controls for non-respiratory infectious diseases (Negative), 681 controls for other respiratory virus infections (Other), and 558 coronavirus infections (Positive) for COVID-19. We first used the Student T-test to screen for statistically significant differences (Pvalue<0.05); we then used the Adaptive-Lasso method stepwise regression to screen the characteristic variables and filter the features with low importance; we then used analysis of covariance to calculate the correlation between variables and filter the highly correlated features; and finally, we analyzed the feature contribution and screened the best combination of features. Results: Feature engineering reduced the feature set to 13 feature combinations. The correlation coefficient between the projected results of the artificial intelligence-based individualized diagnostic model and the fitted curve of the actual values in the test group was 0.9449 which could be applied to the clinical prognosis of COVID-19. In addition, the depletion of platelets in patients with COVID-19 is an important factor affecting their severe deterioration. With the progression of COVID-19, there is a slight decrease in the total number of platelets in the patient's body, particularly as the volume of larger platelets sharply decreases. The importance of plateletCV (count*mean platelet volume) in evaluating the severity of COVID-19 patients is higher than the count of platelets and mean platelet volume. Conclusion: In general, we found that for patients with COVID-19, the increase in mean platelet volume was a predictor for SARS-Cov-2. The rapid decrease of platelet volume and the decrease of total platelet volume are dangerous signals for the aggravation of SARS-Cov-2 infection. The analysis and modeling results of this study provide a new perspective for individualized accurate diagnosis and treatment of clinical COVID-19 patients.

Integrin α5 Is Regulated by miR-218-5p in Endothelial Progenitor Cells.

BACKGROUND: Endothelial cell injury is a common nidus of renal injury in patients and consistent with the high prevalence of AKI reported during the coronavirus disease 2019 pandemic. This cell type expresses integrin α5 (ITGA5), which is essential to the Tie2 signaling pathway. The microRNA miR-218-5p is upregulated in endothelial progenitor cells (EPCs) after hypoxia, but microRNA regulation of Tie2 in the EPC lineage is unclear. METHODS: We isolated human kidney-derived EPCs (hkEPCs) and surveyed microRNA target transcripts. A preclinical model of ischemic kidney injury was used to evaluate the effect of hkEPCs on capillary repair. We used a genetic knockout model to evaluate the effect of deleting endogenous expression of miR-218 specifically in angioblasts. RESULTS: After ischemic in vitro preconditioning, miR-218-5p was elevated in hkEPCs. We found miR-218-5p bound to ITGA5 mRNA transcript and decreased ITGA5 protein expression. Phosphorylation of 42/44 MAPK decreased by 73.6% in hkEPCs treated with miR-218-5p. Cells supplemented with miR-218-5p downregulated ITGA5 synthesis and decreased 42/44 MAPK phosphorylation. In a CD309-Cre/miR-218-2-LoxP mammalian model (a conditional knockout mouse model designed to delete pre-miR-218-2 exclusively in CD309+ cells), homozygotes at e18.5 contained avascular glomeruli, whereas heterozygote adults showed susceptibility to kidney injury. Isolated EPCs from the mouse kidney contained high amounts of ITGA5 and showed decreased migratory capacity in three-dimensional cell culture. CONCLUSIONS: These results demonstrate the critical regulatory role of miR-218-5p in kidney EPC migration, a finding that may inform efforts to treat microvascular kidney injury via therapeutic cell delivery.