Sleep quality as a mediator of the relationship between screen time and negative emotions among Chinese college freshmen.

College freshmen are special populations facing great challenges in adapting to the brand new environment, and their lifestyle and emotional states are worthy of attention. Especially during the COVID-19 pandemic, their screen time and prevalence of negative emotions were significantly increased, but few studies have focused on such situation of college freshmen and illustrated relevant mechanisms. Thus, based on a sample of Chinese college freshmen during the COVID-19 pandemic, the current study aimed to investigate the association between their screen time and negative emotions (depression, anxiety and stress), and further explore the mediating effects of sleep quality. Data from 2,014 college freshmen was analyzed. The screen time was self-reported by participants using predesigned questionnaires. The Pittsburgh Sleep Quality Index (PSQI) and Chinese Version of Depression Anxiety and Stress Scale-21 (DASS-21) were used to assess sleep quality and emotional states, respectively. The mediation analysis was conducted to examine the meditation effect. Results indicated that participants with negative emotions tended to have longer daily screen time and worse sleep quality, sleep quality partially mediated the association between screen time and negative emotions.The critical role of sleep quality and related intervention measures should be recognized and implemented.

A Case Series of Olfactory Dysfunction in Imported COVID-19 Patients: A 12-Month Follow-Up Study.

Objective: The scientific community knows little about the long-term influence of coronavirus disease 2019 (COVID-19) on olfactory dysfunction (OD). With the COVID-19 pandemic ongoing worldwide, the risk of imported cases remains high. In China, it is necessary to understand OD in imported cases. Methods: A prospective follow-up design was adopted. A total of 11 self-reported patients with COVID-19 and OD from Xi'an No. 8 Hospital were followed between August 19, 2021, and December 12, 2021. Demographics, clinical characteristics, laboratory and radiological findings, and treatment outcomes were analyzed at admission. We surveyed the patients via telephone for recurrence and sequelae at the 1-, 6-, and 12-month follow-up. Results: Eleven patients with OD were enrolled; of these, 54.5% (6/11) had hyposmia and 45.5% (5/11) had anosmia. 63.6% (7/11) reported OD before or on the day of admission as their initial symptom; of these, 42.9% (3/7) described OD as the only symptom. All patients in the study received combined treatment with traditional Chinese medicine and Western medicine, and 72.7% (8/11) had partially or fully recovered at discharge. In terms of OD recovery at the 12-month follow-up, 45.5% (5/11) reported at least one sequela, 81.8% (9/11) had recovered completely, 18.2% (2/11) had recovered partially, and there were no recurrent cases. Conclusions: Our data revealed that OD frequently presented as the initial or even the only symptom among imported cases. Most OD improvements occurred in the first 2 weeks after onset, and patients with COVID-19 and OD had favorable treatment outcomes during long-term follow-up. A better understanding of the pathogenesis and appropriate treatment of OD is needed to guide clinicians in the care of these patients.

A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media.

BACKGROUND: Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people's daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages. METHODS: This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods. RESULTS: Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model.