An Analysis of the Learning Effects and Differences of College Students Using English Vocabulary APP

With the gradual popularization of mobile learning, it has become a trend for college students to use learning applications (APPs) for learning, but the learning effect has always been a concern. Since college students have different learning purposes, strategies, skills, and habits, responses to these differences have been an urgent problem. This paper takes college students using an English vocabulary APP to study as the survey object. It sets up the influencing factors model of the English vocabulary learning effect based on UTAUT2. Then, we analyzed the factors influencing the learning effect of using an English vocabulary APP and studied its mechanism. The results show that on the one hand, the influence degree of influencing factors is habit, facilitation condition, price value, effort expectancy, and performance expectancy from high to low, and all the above factors have a significant positive impact on the learning effect of college students using English vocabulary APP. On the other hand, gender, grade, and major have a moderating effect on English vocabulary learning, and there are differences among different genders, grades, and majors. Finally, suggestions are put forward from the perspective of APP construction and students’ differences to enhance and improve the learning effect of English vocabulary.

Effects of mesenchymal stromal cell-derived extracellular vesicles in acute respiratory distress syndrome (ARDS): Current understanding and future perspectives.

Acute respiratory distress syndrome (ARDS) is a devastating and life-threatening syndrome that results in high morbidity and mortality. Current pharmacologic treatments and mechanical ventilation have limited value in targeting the underlying pathophysiology of ARDS. Mesenchymal stromal cells (MSCs) have shown potent therapeutic advantages in experimental and clinical trials through direct cell-to-cell interaction and paracrine signaling. However, safety concerns and the indeterminate effects of MSCs have resulted in the investigation of MSC-derived extracellular vesicles (MSC-EVs) due to their low immunogenicity and tumorigenicity. Over the past decades, soluble proteins, microRNAs, and organelles packaged in EVs have been identified as efficacious molecules to orchestrate nearby immune responses, which attenuate acute lung injury by facilitating pulmonary epithelium repair, reducing acute inflammation, and restoring pulmonary vascular leakage. Even though MSC-EVs possess similar bio-functional effects to their parental cells, there remains existing barriers to employing this alternative from bench to bedside. Here, we summarize the current established research in respect of molecular mechanisms of MSC-EV effects in ARDS and highlight the future challenges of MSC-EVs for clinical application.

Early triage of critically ill COVID-19 patients using deep learning.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19.

Importance: Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective: To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants: Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures: Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results: The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/). Conclusions and Relevance: In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.