Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Hippocampal neurogenesis persists throughout adult life and plays an important role in learning and memory. Although the influence of physical exercise on neurogenesis has been intensively studied, there is controversy in regard to how the impact of exercise may vary with its regime. Less is known about how distinct exercise paradigms may differentially affect the learning behavior. Here we found that, chronic moderate treadmill running led to an increase of cell proliferation, survival, neuronal differentiation, and migration. In contrast, intense running only promoted neuronal differentiation and migration, which was accompanied with lower expressions of vascular endothelial growth factor, brain-derived neurotrophic factor, insulin-like growth factor 1, and erythropoietin. In addition, the intensely but not mildly exercised animals exhibited a lower mitochondrial activity in the dentate gyrus. Correspondingly, neurogenesis induced by moderate but not intense exercise was sufficient to improve the animal’s ability in spatial pattern separation. Our data indicate that the effect of exercise on spatial learning is intensity-dependent and may involve mechanisms other than a simple increase in the number of new neurons.

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Background The ferocious global assault of COVID-19 continues. Critically ill patients witnessed significantly higher mortality than severe and moderate ones. Herein, we aim to comprehensively delineate clinical features of COVID-19 and explore risk factors of developing critical disease. Methods This is a Mini-national multicenter, retrospective, cohort study involving 2,387 consecutive COVID-19 inpatients that underwent discharge or death between January 27 and March 21, 2020. After quality control, 2,044 COVID-19 inpatients were enrolled. Electronic medical records were collected to identify the risk factors of developing critical COVID-19. Findings The severity of COVID-19 climbed up straightly with age. Critical group was characterized by higher proportion of dyspnea, systemic organ damage, and long-lasting inflammatory storm. All-cause mortality of critical group was 85•45%, by contrast with 0•58% for severe group and 0•18% for moderate group. Logistic regression revealed that sex was an effect modifier for hypertension and coronary heart disease (CHD), where hypertension and CHD were risk factors solely in males. Multivariable regression showed increasing odds of critical illness associated with hypertension, CHD, tumor, and age ≥ 60 years for male, and chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), tumor, and age ≥ 60 years for female. Interpretation We provide comprehensive front-line information about different severity of COVID-19 and insights into different risk factors associated with critical COVID-19 between sexes. These results highlight the significance of dividing risk factors between sexes in clinical and epidemiologic works of COVID-19, and perhaps other coronavirus appearing in future. Funding .