ABSTRACT
AIMS: To understand the status quo of self-directed learning ability, self-efficacy and academic burnout of junior nursing college students, since the closed management mode was implemented during COVID-19 pandemic disease. DESIGN: Cross-sectional study. METHODS: Participants came from 3,051 junior nursing college students of a college in Zhengzhou City, Henan Province, China. Data were collected by online questionnaire and analysed by SPSS25.0 (a statistical package for social science) and AMOS24.0 software. RESULTS: High self-directed learning ability and self-efficacy were related to low levels of academic burnout (p < .01). In addition, the analysis of mediation effect indicated that the influence of self-directed learning ability on academic burnout was not mediated by self-efficacy.
ABSTRACT
The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.
Subject(s)
COVID-19/genetics , COVID-19/metabolism , Critical Illness , Genomics/methods , Humans , Lipidomics/methods , Metabolomics/methods , Neutrophils/metabolism , Transcriptome/geneticsSubject(s)
COVID-19 , Machine Learning , SARS-CoV-2/metabolism , Severity of Illness Index , COVID-19/diagnosis , COVID-19/metabolism , Female , Humans , MaleABSTRACT
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.
Subject(s)
Coronavirus Infections/mortality , Machine Learning , Pandemics , Pneumonia, Viral/mortality , Aged , Betacoronavirus , COVID-19 , China/epidemiology , Female , Humans , Logistic Models , Male , Middle Aged , Neural Networks, Computer , Risk Assessment , SARS-CoV-2 , Support Vector MachineSubject(s)
Betacoronavirus , Coronavirus Infections/immunology , Lymphocytes/ultrastructure , Monocytes/ultrastructure , Pneumonia, Viral/immunology , Adult , Aged , COVID-19 , Case-Control Studies , Cell Size , Coronavirus Infections/blood , Electric Conductivity , Female , Flow Cytometry , Humans , Lymphocytes/physiology , Male , Middle Aged , Pandemics , Pneumonia, Viral/blood , Retrospective Studies , SARS-CoV-2 , Sensitivity and SpecificitySubject(s)
Coronavirus Infections/epidemiology , Coronavirus , Pandemics , Pneumonia, Viral/epidemiology , Beijing , Betacoronavirus , COVID-19 , China , Humans , SARS-CoV-2ABSTRACT
Background As the number of patients increases, there is a growing understanding of the form of pneumonia sustained by the 2019 novel coronavirus (SARS-CoV-2), which has caused an outbreak in China. Up to now, clinical features and treatment of patients infected with SARS-CoV-2 have been reported in detail. However, the relationship between SARS-CoV-2 and coagulation has been scarcely addressed. Our aim is to investigate the blood coagulation function of patients with SARS-CoV-2 infection. Methods In our study, 94 patients with confirmed SARS-CoV-2 infection were admitted in Renmin Hospital of Wuhan University. We prospectively collect blood coagulation data in these patients and in 40 healthy controls during the same period. Results Antithrombin values in patients were lower than that in the control group (p < 0.001). The values of D-dimer, fibrin/fibrinogen degradation products (FDP), and fibrinogen (FIB) in all SARS-CoV-2 cases were substantially higher than those in healthy controls. Moreover, D-dimer and FDP values in patients with severe SARS-CoV-2 infection were higher than those in patients with milder forms. Compared with healthy controls, prothrombin time activity (PT-act) was lower in SARS-CoV-2 patients. Thrombin time in critical SARS-CoV-2 patients was also shorter than that in controls. Conclusions The coagulation function in patients with SARS-CoV-2 is significantly deranged compared with healthy people, but monitoring D-dimer and FDP values may be helpful for the early identification of severe cases.