Effects of Stressors of COVID-19 on Chinese College Students' Problematic Social Media Use: A Mediated Moderation Model

Purpose Isolation policies are long-term and strictly enforced in China during the COVID-19 outbreak. Social media might be widely used for communication, work, understanding the development of the epidemic, etc. However, these behaviors might lead to problematic social media use. The present study investigated the effect of stressors of COVID-19 on problematic social media use, as well as the internal mechanisms involved. Methods One thousand three hundred seventy-three Chinese college students (Mage = 19.53, SDage = 1.09) were recruited randomly from four grades who completed Coronavirus Stress Scale, Fear of Missing Out Scale, Problematic Mobile Social Media Usage Assessment Questionnaire, and Regulatory Emotional Self-Efficacy Scale. Results Stressors of COVID-19 were positively related to problematic social media use. The link between stressors of COVID-19 and problematic social media use was mediated by fear of missing out. Additionally, the association between fear of missing out and problematic social media use, as well as the association between stressors of COVID-19 and problematic social media use were moderated by regulatory emotional self-efficacy. Conclusion The current findings reveal the mechanism that may be used to reduce the likelihood of problematic social media use in the context of the COVID-19 outbreak. To prevent and intervene in problematic social media use during the COVID-19 pandemic, this study stressed the importance of decreasing the fear of missing out and enhancing regulatory emotional self-efficacy.

The Effect of Parent Phubbing on Chinese Adolescents’ Smartphone Addiction During COVID-19 Pandemic: Testing a Moderated Mediation Model

Purpose COVID-19 has affected the health and well-being of tens of millions of people and contributed to smartphone addiction. The prior studies found several characteristics that influenced smartphone addiction, but little research was undertaken on the epidemic. This study aims to test a moderated mediation model of smartphone addiction. Methods Three classes in each grade from grade 7 through grade 9 at random were recruited in the target junior high schools. A total of 931 Chinese adolescents (Mage=13.54 years, SDage =1.08) completed valid questionnaires via online surveys from February 5–19, 2021. Results Parent phubbing had a positive effect on smartphone addiction. Boredom proneness played a mediating role in this relationship. Additionally, refusal self-efficacy moderated the effect of parent phubbing on smartphone addiction. Refusal self-efficacy moderated the effect of boredom proneness on smartphone addiction. Conclusion Findings of this study shed light on a correlation between parent phubbing and smartphone addiction. Moreover, this study emphasizes the value of intervening in adolescents’ boredom proneness and increasing the ability of refusal self-efficacy to prevent and intervene in the context of COVID-19.

Breath analysis by ultra-sensitive broadband laser spectroscopy detects SARS-CoV-2 infection (preprint)

Rapid testing is essential to fighting pandemics such as COVID-19, the disease caused by the SARS-CoV-2 virus. Exhaled human breath contains multiple volatile molecules providing powerful potential for non-invasive diagnosis of diverse medical conditions. We investigated breath detection of SARS-CoV-2 infection using cavity-enhanced direct frequency comb spectroscopy (CE-DFCS), a state-of-the-art laser spectroscopic technique capable of a real-time massive collection of broadband molecular absorption features at ro-vibrational quantum state resolution and at parts-per-trillion volume detection sensitivity. Using a total of 170 individual breath samples (83 positive and 87 negative with SARS-CoV-2 based on Reverse Transcription Polymerase Chain Reaction tests), we report excellent discrimination capability for SARS-CoV-2 infection with an area under the Receiver-Operating-Characteristics curve of 0.849(4). Our results support the development of CE-DFCS as an alternative, rapid, non-invasive test for COVID-19 and highlight its remarkable potential for optical diagnoses of diverse biological conditions and disease states.

Clinical Features and Death Risk Factors in COVID-19 Patients with Cancer: A Retrospective Study (preprint)

Purpose: Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors.Methods: From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching analysis, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients were performed. Next, we identified death related risk factors and developed a nomogram for predicting the probability.Results: In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significant different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed.Conclusion: In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significant different. NLR and CRP were the risk factors that could predict death outcome.

Random Network Transmission and Countermeasures in Containing Global Spread of COVID-19-Alike Pandemic: A Hybrid Modelling Approach

Since the outbreak of the novel coronavirus disease (COVID-19) at the beginning of December 2019, there have been more than 28.69 million cumulative confirmed cases worldwide as of 12th September 2020, affecting over 200 countries and regions with more than 920,463 deaths. The COVID-19 pandemic has been sweeping worldwide with unexpected rapidity. In this paper, a hybrid modelling strategy based on tessellation structure- (TS-) configured SEIR model is adopted to estimate the scale of the pandemic spread. Building on the data pertaining to the global pandemic transmission over the last six months around the world, key impact factors in the transmission and control procedure have been analysed, including isolation rate, number of the infected cases before taking prevention measures, degree of contact scope, and medical level, so as to capture the fundamental factor influencing the pandemic. The quantitative evaluation allowed us to illustrate the magnitude of risks of pandemic and to recommend appropriate national health policy of prevention measures for effectively controlling both intra- and interregional pandemic spread. Our modelling results clearly indicate that the early-stage preventive measures are the most effective action to be taken to contain the pandemic spread of the highly contagious nature of the COVID-19.

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

comparative study on the clinical features of COVID-19 pneumonia to other pneumonias ;Clinical Infectious Diseases ;Oxford Academic

Background A novel coronavirus (2019-nCoV) has raised world concern since it emerged in Wuhan Hubei China in December, 2019. The infection may result into severe pneumonia with clusters illness onsets. Its impacts on public health make it paramount to clarify the clinical features with other pneumonias. Methods Nineteen 2019-nCoV pneumonia (NCOVID-19) and fifteen other pneumonia patients (NON-NCOVID-19) in out of Hubei places were involved in this study. Both NCOVID-19 and NON-NCOVID-19 patients were confirmed to be infected in throat swabs or/and sputa with or without 2019-nCoV by real-time RT-PCR. We analyzed the demographic, epidemiological, clinical, and radiological features from those patients, and compared the difference between NCOVID-19 and NON-NCOVID-19. Results All patients had a history of exposure to confirmed case of 2019-nCoV or travel to Hubei before illness. The median duration, respectively, was 8 (IQR:6~11) and 5 (IQR:4~11) days from exposure to onset in NCOVID-19 and NON-NCOVID-19. The clinical symptoms were similar between NCOVID-19 and NON-NCOVID-19. The most common symptoms were fever and cough. Fifteen (78.95%) NCOVID-19 but 4 (26.67%) NON-NCOVID-19 patients had bilateral involvement while 17 (89.47%) NCOVID-19 but 1 (6.67%) NON-NCOVID-19 patients had multiple mottling and ground-glass opacity of chest CT images. Compared to NON-NCOVID-19, NCOVID-19 present remarkably more abnormal laboratory tests including AST, ALT, γ-GT, LDH and α-HBDH. Conclusion The 2019-nCoV infection caused similar onsets to other pneumonias. CT scan may be a reliable test for screening NCOVID-19 cases. Liver function damage is more frequent in NCOVID-19 than NON-NCOVID-19 patients. LDH and α-HBDH may be considerable markers for evaluation of NCOVID-19.