Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study.

OBJECTIVE: To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients. METHOD: We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set. RESULT: For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set. CONCLUSION: Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.

The impact of self-regulated learning strategies on academic performance for online learning during COVID-19.

The COVID-19 pandemic led higher education institutions to transition to online learning. The present study was designed to investigate students' self-regulated learning strategies on academic performance in online learning. We analyzed the differences in college students' self-regulated learning (SRL) strategies according to their grade point average (GPA). The study included 1,163 students at a distance education university in China. Two online questionnaires were used to determine online SRL strategies. GPA scores were obtained from the university exam database to determine academic performance. The analysis showed that there are great differences between different self-regulated strategies and between different students when accepting the online learning. The analysis also showed that self-evaluation, metacognitive self-regulation, and effort regulation were positive predictors of academic progress, besides, self-evaluation and effort regulation had mutual influence effect on the improvement of GPA in online learning. These data will help teachers, education policymakers, and education administrators adopt and implement online learning services to improve students' academic performance.

Impact of the COVID-19 pandemic on career intention amongst undergraduate medical students: a single-centre cross-sectional study conducted in Hubei Province.

BACKGROUND: Undergraduate medical (UM) students faced the difficulties inherent in medical careers due to the coronavirus (COVID-19) outbreak. Thus, imperative containment measures might affect UM students' career intentions. Information on the factors that may be associated with these students' career change intentions is limited. METHODS: We conducted a cross-sectional survey in August 2020 to investigate the impact of the COVID-19 pandemic on career intention and the associated factors in UM students. Univariate analyses and logistic regression analysis were performed to identify said factors. RESULTS: A total of 2040 medical students from the Hubei University of Medicine were surveyed. Univariate analyses showed that grade, attitude towards healthcare, and the degree of the COVID-19 pandemic's impact on the students' lives were associated with changes in career choice (P<0.05). Logistic regression analysis showed that Grade 2, Grade 5, attitude towards a medical career, and having relatives with a medical background were associated with changes in career choice. The degree of the COVID-19 pandemic's impact was a common and significant factor associated with career preference, career perspective, and ideal workplace. CONCLUSIONS: Changes in career intentions were particularly influenced by grade, attitude towards being a health worker, and the degree of COVID-19's impact on the participants' lives. Treating large-scale public health emergencies rationally, setting up correct views of occupation choice, and building reasonable career planning may reduce the loss of medical talent.

An imperialist competition algorithm using a global search strategy for physical examination scheduling.

The outbreak of the novel coronavirus clearly highlights the importance of the need of effective physical examination scheduling. As treatment times for patients are uncertain, this remains a strongly NP-hard problem. Therefore, we introduce a complex flexible job shop scheduling model. In the process of physical examination for suspected patients, the physical examiner is considered a job, and the physical examination item and equipment correspond to an operation and a machine, respectively. We incorporate the processing time of the patient during the physical examination, the transportation time between equipment, and the setup time of the patient. A unique scheduling algorithm, called imperialist competition algorithm with global search strategy (ICA_GS) is developed for solving the physical examination scheduling problem. A local search strategy is embedded into ICA_GS for enhancing the searching behaviors, and a global search strategy is investigated to prevent falling into local optimality. Finally, the proposed algorithm is tested by simulating the execution of the physical examination scheduling processes, which verify that the proposed algorithm can better solve the physical examination scheduling problem.

Effects of metformin, insulin on COVID-19 patients with pre-existed type 2 diabetes: A multicentral retrospective study.

AIMS: Type 2 diabetes is considered to be one of the essential risks of adverse outcomes in coronavirus disease 2019 (COVID-19).1 Metformin and insulin were suggested to affect the outcomes. However, divergent views are still expressed. We aim to gain further insight into metformin and insulin in both pre-admission and in-hospital usage in COVID-19 patients with pre-existed type 2 diabetes. MAIN METHODS: This is a multicentral retrospective study of the hospital confirmed COVID-19 patients between January 19 to April 09, 2020, who admitted to 3 main hospitals in Xiangyang city, China. The effect of type 2 diabetes, metformin, and insulin on COVID-19 were analyzed, respectively. Clinical characteristics, blood laboratory indices, clinical observational indices, and outcomes of these cases were collected. KEY FINDINGS: A total of 407 confirmed COVID-19 patients (including 50 pre-existed type 2 diabetes) were eligible in our study. COVID-19 patients with type 2 diabetes had more adverse outcomes than non-diabetes (OR2: mortality: 1.46 [95% CI3 1.11, 1.93]; P < 0.001). Pre-admission metformin usage showed a declined intensive care unit admission rate in a dose-dependent fashion (OR 0.04 [95% CI 0.00, 0.99]; adjust P = 0.049). While in-hospital insulin usage attempted to increase the invasive ventilation (8 [34.8%] vs. 1 [3.7%], adjust P = 0.043), independent of age and blood glucose. SIGNIFICANCE: Our study indicated that pre-admitted metformin usage may have beneficial effects on COVID-19 with pre-existed type 2 diabetes, insulin should be used sparingly in the hospital stay.

Potential risks of SARS-CoV-2 infection on reproductive health.

The outbreak of 2019 novel coronavirus disease (COVID-19) has become a major pandemic threat worldwide. Such a public health emergency can greatly impact various aspects of people's health and lives. This paper focuses on its potential risks for reproductive health, including the reproductive system and its functioning, as well as gamete and embryo development, which could be affected by the virus itself, drug treatments, chemical disinfectants and psychological effects related to panic during the COVID-19 outbreak.