Classification aware neural topic model for COVID-19 disinformation categorisation

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for (a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation;(b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: (1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories;(2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery;(3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.

A Direct Injection Technique to Improve Biosafety to Analyze Levetiracetam Concentrations in Human Serum and Its Application in Therapeutic Drug Monitoring.

BACKGROUND: With the outbreak of COVID-19, it has become very important to improve biosafety measures taken by medical staff. Fewer pretreatment steps correspond to lower chances of infection. The authors established a direct injection technique to analyze levetiracetam (LEV) concentrations in human serum and studied its application in therapeutic drug monitoring. METHODS: Serum samples were prepared by hollow fiber centrifugal ultrafiltration and the filtrate was directly injected into a ultra-high performance liquid chromatography apparatus (Waters UPLC BEH C18 column: 50 × 2.1 mm, 1.7 µm) for analysis. The mobile phase consisted of acetonitrile and water (8:92) at a flow rate of 1.0 mL/min. The column temperature was maintained at 30°C. The detected wavelength was 210 nm. RESULTS: A linear relationship was obtained for LEV from 0.625 to 80 mcg/mL (r2 = 0.999). The limit of detection for the analysis of LEV was 0.125 mcg/mL. The analysis time was shortened to 4 minutes. The recovery rate of LEV based on the current method was 96.6%-100.1%, whereas the absolute recovery rate was 93.2%-96.8%. The relative SD of intraday and interday precision was <7.3%. Stability was achieved at room temperature for 24 hours after 3 freeze-thaw cycles and at -80°C for 21 days. The method was successfully applied to determine LEV concentrations in the serum of 19 patients. CONCLUSIONS: The present method is simple, accurate, and sensitive, and can improve biosafety with the direct injection technique. It is suitable for the analysis of LEV concentrations in therapeutic drug monitoring.

Psychological and occupational impact on healthcare workers and its associated factors during the COVID-19 outbreak in China.

PURPOSE: China was affected by an outbreak of coronavirus disease 2019 (COVID-19) in 2019-2020. Research data are needed to develop evidence-driven strategies to reduce the adverse psychological and occupational impacts on healthcare workers (HCWs). METHODS: From March 1, 2020, to March 8, 2020, 946 HCWs in China completed a survey consist of sociodemographic data, precautionary measures against COVID-19, and concerns about COVID-19. Self-administered questionnaire were collected to assess psychological and occupational adverse outcomes of HCWs. Multivariable logistic regression analysis was performed to identify factors associated with the outcomes. RESULTS: A total of 55.0%, 56.0% and 48.3% of the HCWs experienced burnout, psychological distress and posttraumatic stress, respectively. A total of seven factors were independently associated with burnout: good health status (OR 0.51, 95% CI 0.36-0.71), fear of contagion (OR 1.31, 95% CI 1.003-1.79), avoiding contact with children (OR 1.40, 95% CI 1.03-1.91), enough staff support at the workplace (OR 0.59, 95% CI 0.38-0.92), having to work overtime (OR 1.37, 95% CI 1.03-1.83), maladaptive coping (OR 3.28, 95% CI 2.42-4.45) and adaptive coping (OR 0.47, 95% CI 0.35-0.62). A total of 11 factors were independently associated with high psychological distress: having one child (OR 0.54, 95% CI 0.38-0.77), good health status (OR 0.57, 95% CI 0.39-0.83), alcohol abuse (OR 1.51, 95% CI 1.02-2.25), thinking the epidemic would continue for quite a long time (OR 1.59, 95% CI 1.08-2.34), wearing extra-work clothes (OR 1.51, 95% CI 1.06-2.15),effective protective equipment (OR 0.45, 95% CI 0.22-0.90), enough staff support at the workplace (OR 0.55, 95% CI 0.34-0.89), unable to take care of families (OR 1.99, 95% CI 1.42-2.78), economic losses (OR 1.62, 95% CI 1.14-2.31), maladaptive coping (OR 6.88, 95% CI 4.75-9.97),and adaptive coping (OR 0.29, 95% CI 0.21-0.41). These factors were independently associated with posttraumatic stress: living with the elderly (OR 1.46, 95% CI 1.04-2.05), alcohol abuse (OR 1.41, 95% CI 1.002-1.98), working at a 3A hospital(OR 0.66, 95% CI 0.49-0.88), acquaintances confirmed COVID-19 (OR 2.14, 95% CI 1.20-3.84), fear of contagion (OR 1.87, 95% CI 1.40-2.50), believing they would survive if infected (OR 0.63, 95% CI 0.46-0.86), self-disinfected after arriving home (OR 1.43, 95% CI 1.01-2.02), interpersonal isolation (OR 1.65, 95% CI 1.21-2.26), unable to take care of families (OR 1.41, 95% CI 1.05-1.88) and maladaptive coping (OR 3.09, 95% CI 2.32-4.11). CONCLUSION: The variance in adverse outcomes was explained by the effect of various factors, which will help policymakers better prepare for subsequent potential outbreaks of COVID-19.

Comorbidity and its impact on 1,590 patients with COVID-19 in China: A Nationwide Analysis (preprint)

Objective: To evaluate the spectrum of comorbidities and its impact on the clinical outcome in patients with coronavirus disease 2019 (COVID-19). Design: Retrospective case studies Setting: 575 hospitals in 31 province/autonomous regions/provincial municipalities across China Participants: 1,590 laboratory-confirmed hospitalized patients. Data were collected from November 21st, 2019 to January 31st, 2020. Main outcomes and measures: Epidemiological and clinical variables (in particular, comorbidities) were extracted from medical charts. The disease severity was categorized based on the American Thoracic Society guidelines for community-acquired pneumonia. The primary endpoint was the composite endpoints, which consisted of the admission to intensive care unit (ICU), or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared among patients with COVID-19 according to the presence and number of comorbidities. Results: Of the 1,590 cases, the mean age was 48.9 years. 686 patients (42.7%) were females. 647 (40.7%) patients were managed inside Hubei province, and 1,334 (83.9%) patients had a contact history of Wuhan city. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. 269 (16.9%), 59 (3.7%), 30 (1.9%), 130 (8.2%), 28 (1.8%), 24 (1.5%), 21 (1.3%), 18 (1.1%) and 3 (0.2%) patients reported having hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, chronic obstructive pulmonary disease, chronic kidney diseases, malignancy and immunodeficiency, respectively. 130 (8.2%) patients reported having two or more comorbidities. Patients with two or more comorbidities had significantly escalated risks of reaching to the composite endpoint compared with those who had a single comorbidity, and even more so as compared with those without (all P<0.05). After adjusting for age and smoking status, patients with COPD (HR 2.681, 95%CI 1.424-5.048), diabetes (HR 1.59, 95%CI 1.03-2.45), hypertension (HR 1.58, 95%CI 1.07-2.32) and malignancy (HR 3.50, 95%CI 1.60-7.64) were more likely to reach to the composite endpoints than those without. As compared with patients without comorbidity, the HR (95%CI) was 1.79 (95%CI 1.16-2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61-4.17) among patients with two or more comorbidities. Conclusion: Comorbidities are present in around one fourth of patients with COVID-19 in China, and predispose to poorer clinical outcomes.

Clinical characteristics of 2019 novel coronavirus infection in China (preprint)

Background: Since December 2019, acute respiratory disease (ARD) due to 2019 novel coronavirus (2019-nCoV) emerged in Wuhan city and rapidly spread throughout China. We sought to delineate the clinical characteristics of these cases. Methods: We extracted the data on 1,099 patients with laboratory-confirmed 2019-nCoV ARD from 552 hospitals in 31 provinces/provincial municipalities through January 29th, 2020. Results: The median age was 47.0 years, and 41.90% were females. Only 1.18% of patients had a direct contact with wildlife, whereas 31.30% had been to Wuhan and 71.80% had contacted with people from Wuhan. Fever (87.9%) and cough (67.7%) were the most common symptoms. Diarrhea is uncommon. The median incubation period was 3.0 days (range, 0 to 24.0 days). On admission, ground-glass opacity was the typical radiological finding on chest computed tomography (50.00%). Significantly more severe cases were diagnosed by symptoms plus reverse-transcriptase polymerase-chain-reaction without abnormal radiological findings than non-severe cases (23.87% vs. 5.20%, P<0.001). Lymphopenia was observed in 82.1% of patients. 55 patients (5.00%) were admitted to intensive care unit and 15 (1.36%) succumbed. Severe pneumonia was independently associated with either the admission to intensive care unit, mechanical ventilation, or death in multivariate competing-risk model (sub-distribution hazards ratio, 9.80; 95% confidence interval, 4.06 to 23.67). Conclusions: The 2019-nCoV epidemic spreads rapidly by human-to-human transmission. Normal radiologic findings are present among some patients with 2019-nCoV infection. The disease severity (including oxygen saturation, respiratory rate, blood leukocyte/lymphocyte count and chest X-ray/CT manifestations) predict poor clinical outcomes.