The Application of Biosensors in the Detection of SARS-CoV-2

Since the outbreak of COVID-19, it is becoming important to screen SARS-CoV-2 with high accuracy, high efficiency, and rapidness, for epidemic prevention and control. Conventional detection technologies can not satisfy the requirements of examining massive people in a very short time. Biosensor technology, with the advantages of high sensitivity, good selectivity, low cost, easy miniaturization, and short detection time, is being used to develop real-time detection equipment, thus as a potential alternative for real-time detection of SARS-CoV-2 in clinical diagnosis. In the present study, the authors summarized the construction methods and principles for optical biosensors, electrochemical biosensors, wearable biosensors, magnetic biosensors, gold nanoparticle biosensors, and aptamer biosensors, followed by the introduction of the current application of multiple biosensors in SARS-CoV-2 detection. Conclusively, the technical bottlenecks and future development trends of biosensors in SARS-CoV-2 detection are proposed.

Partially Linear Additive Functional Regression

We consider a novel partially linear additive functional regression model in which both a functional predictor and some scalar predictors appear. The functional part has a semiparametric continuously additive form, while the scalar predictors appear in the linear part. The functional part has the optimal convergence rate, and the asymptotic normality of the nonfunctional part is also shown. Simulations and an empirical analysis of a Covid-19 data set demonstrate the performance of the proposed estimator.

A Dashboard for Mitigating the COVID-19 Misinfodemic

This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of, and effective interventions against the COVID-19 misinfodemic on Twitter. Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places. Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions. The paper explains how to use BERT-based models to match tweets with the facts and misinformation and to detect their stance towards such information. The paper also discusses the results of preliminary experiments on analyzing the spatiotemporal spread of misinformation.

Psychological distress and associated factors of the primary caregivers of offspring with eating disorder during the coronavirus disease 2019 pandemic

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) is a global pandemic and posed serious challenges in many countries. A number of studies before the COVID-19 pandemic have shown that the primary caregivers of the ED patients are subjected to great burden, psychological pressure, and serious emotional problems. This study aimed to investigate the psychological distress level of the primary caregivers of ED offspring during the COVID-19 pandemic. METHODS: From March 6 to April 20, 2020, 378 questionnaires for primary caregivers of ED offspring and 1163 questionnaires for primary caregivers of healthy offspring were collected through an online crowdsourcing platform in mainland China. Valid questionnaires that met the criteria included 343 (90.74%) primary caregivers of ED offspring and 1085 (93.29%) primary caregivers of healthy offspring. Using Propensity Score Matching (PSM), 315 (83.33%) primary caregivers of ED offspring and 315 matched primary caregivers of healthy offspring were included in the statistical analysis. Depression, anxiety, perceived stress and social support were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Perceived Stress Scale-10 and Social Support Rating Scale, respectively. RESULTS: The rates of depression and anxiety of the primary caregivers of ED offspring were 20.6 and 16.5%, which were significantly higher than those of primary caregivers of healthy offspring (4.1 and 2.2%), all P < 0.001. Regression analysis found that perceived stress, social support, previous or present mental illness, family conflicts during the COVID-19 pandemic had a significant impact on the severity of depression (P < 0.001, P = 0.002, P = 0.041, P = 0.014);Perceived stress, social support, family conflicts during the COVID-19 pandemic and years of education had a significant impact on the severity of anxiety (P < 0.001, P = 0.002, P = 0.002, P = 0.003). CONCLUSIONS: During the COVID-19 pandemic, primary caregivers of ED offspring experienced more psychological distress than that of primary caregivers of healthy offspring. ED caregivers with high perceived stress may have higher levels of depression and anxiety. ED caregivers with high social support, no mental illness and no family conflicts may have lower levels of depression. ED caregivers with high social support, no family conflicts, and high years of education may have lower levels of anxiety.

[Clinical characteristics and death risk factors of severe COVID-19].

Objective: To analyze the clinical features and death-related risk factors of COVID-19. Methods: We enrolled 891 COVID-19 patients admitted to the Affiliated Hospital of Jianghan University from December 2019 to February 2020, including 427 men and 464 women. Of the 891 cases, 582 were severe or critical, including 423(73%)severe and 159 (27%) critical cases. We compared the demographics, laboratory findings, clinical characteristics, treatments and prognosis data of the 582 severe patients. Univariate and multivariate logistic regression analysis was conducted to explore the risk factors associated with death in COVID-19 patients. Results: The 582 severe patients included 293 males and 289 females, with a median age of 64(range 24 to 106). Sixty-three patients died, including 45 males and 18 females, with a median age of 71(range 37 to 90). The average onset time of the 582 patients was 8 days, of whom 461 (79%) had fever, 358 (62%) dry cough, 274 (47%) fatigue. There were 206 cases with shortness of breath (35%), 155 cases with expectoration (27%), 83 cases with muscle pain or joint pain (14%), 71 cases with diarrhea (12%), and 29 cases with headache (4%). Underlying diseases were present in 267 (46%) patients, most commonly hypertension (194, 33%), followed by diabetes (69, 12%), coronary atherosclerotic heart disease (37, 6%), tumor (18, 3%), and chronic obstructive pulmonary disease (5, 1%). Chest CT showed bilateral lung involvement in 505 patients (87%). Upon admission, the median lymphocyte count of the 582 patients was 0.8(IQR, 0.6-1.1)×10(9)/L, the median D-dimer was 0.5 (IQR, 0.4- 0.8) mg/L, the median N-terminal brain natriuretic peptide precursor (NT-proBNP) was 433 (IQR, 141- 806) pg/L, and the median creatinine was 70.3 (IQR, 56.9-87.9) µmol/L. The death group had a median lymphocyte count of 0.5 (0.4-0.8)×10(9)/L, D-dimer 1.1 (0.7-10.0)mg/L, N-terminal brain natriuretic peptide precursor 1479(893-5 087) pg/ml, and creatinine 89.9(67.1-125.3) µmol/L. Multivariate logistic analysis showed that increased D-dimer (OR: 1.095, 95% CI: 1.045-1.148, P<0.001), increased NT-proBNP (OR: 4.759, 95% CI: 2.437-9.291, P<0.001), and decreased lymphocyte count (OR: 0.180, 95% CI: 0.059-0.550, P=0.003) were the risk factors of death in COVID-19 patients. Conclusions: The average onset time of severe COVID-19 was 8 days, and the most common symptoms were fever, dry cough and fatigue. Comorbidities such as hypertension were common and mostly accompanied by impaired organ functions on admission. Higher D-dimer, higher NT-proBNP, and lower lymphocyte count were the independent risk factors of death in COVID-19 patients.