Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media.

BACKGROUND: COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic's effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. OBJECTIVE: This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. METHODS: From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated. RESULTS: In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively. CONCLUSIONS: The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

A rare case of mediastinal mass with massive pleural effusion misdiagnosed as neurogenic tumor.

BACKGROUND: Mediastinal cavernous hemangiomas are extremely rare vascular tumors. To the best of our knowledge less than 20 cases of posterior mediastinal hemangioma have been reported in literature, and this is the first case of mediastinal cavernous hemangioma presenting with massive pleural effusion. CASE PRESENTATION: We report a case of a 56-year-old female who presented with cough and chest tightness and was found with a massive pleural effusion in chest CT. It was mistaken for a malignant pleural effusion. A posterior mediastinal lesion was observed after thoracic drainage and misdiagnosed again as neurofibroma. The lesion was resected and post-operative histopathology suggested that it was a cavernous hemangioma. Post-operative recovery was uneventful, and a follow-up examination nearly 14 months later showed the patient had no recurrence. CONCLUSIONS: Due to the lack of diagnostic specificity and variety of clinical manifestations, CHM is often misdiagnosed prior to resection. This is the first description of mediastinal hemangioma presenting with massive pleural effusion. It is very important to consider mediastinal hemangioma before operation to reduce surgical complications, and it should be in the differential diagnosis of posterior mediastinal masses.

Beliefs and perceptions of electronic cigarettes among medical staff in respiratory departments of Fujian Province, China, in 2021.

INTRODUCTION: Due to the popularity of e-cigarettes, more and more patients ask about e-cigarettes, and it is particularly important to understand doctors' beliefs and perceptions on e-cigarettes. The aim was to evaluate the belief and perception of electronic cigarettes among medical staff in the respiratory department of medical institutions located in Fujian Province. METHODS: The electronic questionnaires were conveyed to the medical staff of the respiratory department in Fujian Province during March to April 2021. Descriptive statistics were calculated for all questions, and the relationship between relevant factors and the perception of e-cigarette-related statements was analyzed by logistic regression analysis. RESULTS: Among 1028 medical staff in the respiratory departments of Fujian Province, 90.5% of medical staff agreed that electronic cigarettes are harmful to the human body; 61.4% of medical staff agreed that e-cigarettes cannot be regarded as a type of smoking cessation treatment; 71.7% of medical staff agreed that e-cigarettes could be a 'gateway' to other tobacco use; and 69.2% of medical staff agreed that electronic cigarettes are in 'Three No' states. The multivariate logistic regression analysis showed that the respondents' perception of 'e-cigarettes cannot be regarded as a type of smoking cessation treatment' were related to gender, professional title and whether they participated in the cessation clinic. CONCLUSIONS: The medical staff of the respiratory department in Fujian Province put more emphasis on the adverse effects of e-cigarettes on health, but lack the cognition of the effect of e-cigarette smoking cessation. In order to better carry out smoking cessation work, it is necessary to strengthen the training of respiratory medical staff at all levels of medical institutions on e-cigarette knowledge.