Machine Learning-Based Prediction of COVID-19 Prognosis Using Clinical and Hematologic Data.

The coronavirus disease 2019 (COVID-19) pandemic is challenging healthcare systems worldwide. The prediction of disease prognosis has a critical role in confronting the burden of COVID-19. We aimed to investigate the feasibility of predicting COVID-19 patient outcomes and disease severity based on clinical and hematological parameters using machine learning techniques. This multicenter retrospective study analyzed records of 485 patients with COVID-19, including demographic information, symptoms, hematological variables, treatment information, and clinical outcomes. Different machine learning approaches, including random forest, multilayer perceptron, and support vector machine, were examined in this study. All models showed a comparable performance, yielding the best area under the curve of 0.96, in predicting the severity of disease and clinical outcome. We also identified the most relevant features in predicting COVID-19 patient outcomes, and we concluded that hematological parameters (neutrophils, lymphocytes, D-dimer, and monocytes) are the most predictive features of severity and patient outcome.

Rapamycin treatment correlates changes in primary cilia expression with cell cycle regulation in epithelial cells.

Primary cilia are sensory organelles that regulate cell cycle and signaling pathways. In addition to its association with cancer, dysfunction of primary cilia is responsible for the pathogenesis of polycystic kidney disease (PKD) and other ciliopathies. Because the association between cilia formation or length and cell cycle or division is poorly understood, we here evaluated their correlation in this study. Using Spectral Karyotyping (SKY) technique, we showed that PKD and the cancer/tumorigenic epithelial cells PC3, DU145, and NL20-TA were associated with abnormal ploidy. We also showed that PKD and the cancer epithelia were highly proliferative. Importantly, the cancer epithelial cells had a reduction in the presence and/or length of primary cilia relative to the normal kidney (NK) cells. We then used rapamycin to restore the expression and length of primary cilia in these cells. Our subsequent analyses indicated that both the presence and length of primary cilia were inversely correlated with cell proliferation. Collectively, our data suggest that restoring the presence and/or length of primary cilia may serve as a novel approach to inhibit cancer cell proliferation.