Development and Validation of a Machine learning prediction model of respiratory failure within 48 hours of patient admission for COVID-19.

Background Predicting early respiratory failure in COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating patients at greatest risk for deterioration. Given the complexity of COVID-19 disease, machine learning (ML) approaches may support clinical decision making for patients with this disease. Objective Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department (ED). Methods Data was collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and discharged, died, or spent a minimum of 48 hours in the hospital between March 1, 2020 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the ED. We trained and validated three predictive models (two based on XGBoost, one that utilized logistic regression) using cross hospitals validation. We compared model performance between all three models as well as an established early warning score (Modified Early Warning Score (MEWS)) using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, and other metrics. Results The XGBoost model had the highest mean accuracy of 0.919 (AUC = 0.77), outperforming the other two models as well as MEWS. Important predictor variables included the type of oxygen delivery used in the ED, patient age, Emergency Severity Index (ESI), respiratory rate, serum lactate, and demographic characteristics. Conclusions XGBoost has high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19. Clinicaltria

Ras Proteins Have Multiple Functions in Vegetative Cells of Dictyostelium ▿

During the aggregation of Dictyostelium cells, signaling through RasG is more important in regulating cyclic AMP (cAMP) chemotaxis, whereas signaling through RasC is more important in regulating the cAMP relay. However, RasC is capable of substituting for RasG for chemotaxis, since rasG− cells are only partially deficient in chemotaxis, whereas rasC−/rasG− cells are totally incapable of chemotaxis. In this study we have examined the possible functional overlap between RasG and RasC in vegetative cells by comparing the vegetative cell properties of rasG−, rasC−, and rasC−/rasG− cells. In addition, since RasD, a protein not normally found in vegetative cells, is expressed in vegetative rasG− and rasC−/rasG− cells and appears to partially compensate for the absence of RasG, we have also examined the possible functional overlap between RasG and RasD by comparing the properties of rasG− and rasC−/rasG− cells with those of the mutant cells expressing higher levels of RasD. The results of these two lines of investigation show that RasD is capable of totally substituting for RasG for cytokinesis and growth in suspension, whereas RasC is without effect. In contrast, for chemotaxis to folate, RasC is capable of partially substituting for RasG, but RasD is totally without effect. Finally, neither RasC nor RasD is able to substitute for the role that RasG plays in regulating actin distribution and random motility. These specificity studies therefore delineate three distinct and none-overlapping functions for RasG in vegetative cells

The small GTPases Ras and Rap1 bind to and control TORC2 activity

Target of Rapamycin Complex 2 (TORC2) has conserved roles in regulating cytoskeleton dynamics and cell migration and has been linked to cancer metastasis. However, little is known about the mechanisms regulating TORC2 activity and function in any system. In Dictyostelium, TORC2 functions at the front of migrating cells downstream of the Ras protein RasC, controlling F-actin dynamics and cAMP production. Here, we report the identification of the small GTPase Rap1 as a conserved binding partner of the TORC2 component RIP3/SIN1, and that Rap1 positively regulates the RasC-mediated activation of TORC2 in Dictyostelium. Moreover, we show that active RasC binds to the catalytic domain of TOR, suggesting a mechanism of TORC2 activation that is similar to Rheb activation of TOR complex 1. Dual Ras/Rap1 regulation of TORC2 may allow for integration of Ras and Rap1 signaling pathways in directed cell migration

A Receptor-associated Protein/Phosphatidylinositol 3-Kinase Pathway Controls Pseudopod Formation

GbpD, a guanine exchange factor specific for Rap1, has been implicated in adhesion, cell polarity, and chemotaxis of Dictyostelium cells. Here it is shown that activated Rap1 directly binds to PI3K. The activation of PI3K by Rap1 and RasG regulates basal and chemoattractant-stimulated PIP3 levels and pseudopod formation