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Objective: To explore the clinical safety of lopinavir/ritonavir (LPV/r) by mining the risk signals of adverse events (AEs) related to LPV/r for the safe application of the drug in the treatment of novel coronavirus pneumonia (COVID-19). Method(s): The risk signals related to LPV/r in AE reports of US FDA Adverse Event Reporting System (FAERS) from the first quarter of 2010 to the third quarter of 2019 were mined by reporting odds ratio (ROR). An AE with reports more than 3 and 95% confidence interval (CI) lower limit of ROR greater than 1 was defined as a positive signal. AEs were counted and classified using the preferred system organ class (SOC) and preferred term (PT) of Medical Dictionary for Regulatory Activities (MedDRA). The PTs of top 50 adverse event reports and signal strength were selected and analyzed. Result(s): From the first quarter of 2010 to the third quarter of 2019, a total of 13 335 AE reports with LPV/r as the primary suspicious drug were reported in the FAERS database. Four hundred and fifty-five AE risk signals with reports more than 3 and the 95%CI lower limit of ROR greater than 1 were detected, involving 7 718 AE reports. The top 2 system organs involved in AE reports were "injury, poisoning and procedural complications" [13.6% (1 051/7 718)] and "pregnancy, puerperium and perinatal conditions" [11.7% (899/7 718)]. However, 998 (95.0%) of 1051 AE reports involved in "injury, poisoning and procedural complications" were related to drug exposure during pregnancy. The system organ with the highest signals was "congenital, familial and genetic disorders" [16.3% (74/455)]. In addition, 144 AEs caused by drug interactions were detected, which ranked the 7th in the AE reports. Conclusion(s): The risk signals of fetal, neonatal and infant abnormalities related to LPV/r during pregnancy were detected, suggesting that attention should be paid to the risk of using LPV/r in pregnant women and infants. The interaction between LPV/r and other drugs was also worthy of attention.Copyright © 2020 by the Chinese Medical Association.
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Background: SARS-CoV-2 is responsible for the respiratory illness, coronavirus infection disease 2019 (COVID-19). Case reporting may underestimate infection in a population because some infections will not cause illness, others may not be severe enough for people to seek testing and testing may be disproportionately directed to outbreaks. Therefore, seroprevalence studies that monitor SARS-CoV-2 antibodies bridge the gap left from case detection and vaccination records, are important to understand what proportion of the population have detectable antibodies (the seroprevalence), and to monitor trajectories over the course of the pandemic. Aims: Using residual blood from healthy Canadian Blood Services blood donors, the aim of this study was to evaluate Canada-wide SARS-CoV-2 seroprevalence of the spike and nucleocapsid antibodies over vaccine deployment. Methods: Between January and November 2021 serial cross-sectional samples from blood donors at all Canadian Blood Services locations were included. The Roche Elecsys anti-SARS-CoV-2 antibody assays detect total antibodies to spike (S) and nucleocapsid (N) proteins. Anti-S was analysed neat and 1:10 dilution from January until August;from September onward, the dilution changed to 1:400. Seroprevalence was standardized to population-level demographics and assay characteristics adjusted using the Rogan-Gladen equation. Results: Of 149,522 samples, the percentage anti-S positive increased from 2.78% (95%CI 2.6, 3.0) to 97.0% (95%CI 96.6, 97.4) and anti-N from 2.24% (95%CI 2.2, 2.4) to 5.1% (95%CI 4.6, 5.5). The percentage anti-N positive peaked highest in Alberta (9.3%;95% CI 7.7, 10.8) and the Prairies (8.6%;95% CI 6.6, 10.6);lowest in Atlantic region (1.0%;95% CI 0.2, 1.8);was higher in 17-24-year old's (8.1%;95% CI 7.2, 9.0) and racialized donors (8.9%;95% CI 7.5, 10.4). The percentage anti-S positive increased in donors over 60 first;all peaked by July. Beginning in September, the median anti-S concentration (IQ range) was: September 3652 U/ml (2041,6245);October 2807 U/ml (1616,4855);November 2460 U/ml (1253,4388). In a linear regression model of September to November data, with anti-S concentrations as the dependent variable, there was a negative slope with older age group and later month (p < 0.001). Summary/Conclusions: SARS-CoV-2 seroprevalence due to natural infection was below or around 5% from January to November 2021, but cumulative seroprevalence was likely higher as waning antibody was not considered. Anti-S seroprevalence was largely due to vaccination. The early increase in older donors is consistent with vaccine roll-out policies prioritized by age. Lower anti-S concentrations in older individuals are likely related to waning antibody with longer time since their second vaccine dose;a muted response with older age has not been ruled out. Ongoing monitoring of seroprevalence continues to be important for public health policies including potential third dose of vaccine.
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Objective: To explore the implementation and management measures of drug clinical trials during the period of COVID-19 epidemic, protect the safety and rights of subjects, ensure the smooth implementation of clinical trials, and provide reference and suggestions for the management work of clinical trial institutions. Methods: According to the requirements of COVID-19 epidemic prevention and control policies and the national guiding principles for drug clinical trial management, combining the experience of our hospital, we optimized the working process and proposed management measures in four aspects including project and personnel management, subject follow-up management, drug distribution management, and communication between all parties involved in clinical trials. Results and conclusions: During the period of COVID-19 epidemic, our hospital has taken a series of measures which ensured the smooth implementation of more than 200 drug clinical trials and protected the safety and rights of subjects and researchers.
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The coronavirus disease 2019 (COVID-19) has been identified as the main cause of the outbreak of the respiratory disease in Wuhan, Hubei Province of China in December 2019. Since then, the epidemic has spread rapidly throughout China and many other countries in the world. This study, therefore, examines the spatiotemporal distribution of the confirmed cases of COVID-19 and its effect on human development in China, and suggested social and non-pharmaceutical preventive interventions to help curb the further spread of the disease. The public open data available from January to February 2020, from the National Health Commission of the People's Republic of China and a medical knowledge sharing website were used, and spatial analysis was performed to visualize the spatial distribution pattern of COVID-19 in China. The results showed among others that COVID-19 had entered a dispersed spatial pattern, resulting in increased pressure to control the spread of the disease. In early March, there was a significant reduction in the existing number of cases, and the number of deaths also decreased. At the provincial level, the spatial distribution of the number of cumulative confirmed cases in China was divided into four patterns: Hubei was the initial core region;the eastern provinces adjacent to Hubei formed the second concentrated pattern;the western provinces adjacent to Hubei and the northeastern and southeastern provinces which were separated from Hubei by one province belonged to the third distribution pattern;while the rest of the provinces in the north, south and west showing sporadic distribution patterns formed the fourth. It has been estimated that about 80% of students' online learning at all schools were not effective due to lack of access to reliable and uninterrupted internet services especially in the rural areas of China.
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Background/Case Studies: Multiple assays to detect SARS-COV-2 antibodies are available but no gold standard exists. Due to many factors including waning antibodies and differences in test designs, discordance between SARS-CoV-2 serology assays is common. Given these limitations we used multiple assays and methodological approaches to estimate SARS-COV-2 seroprevalence during the first COVID-19 wave in Canada. Study Design/Methods: This serial cross-sectional study was conducted using residual plasma from healthy blood donors between April-September 2020. Qualitative (Table Presented) assessment of SARS-CoV-2 IgG antibodies was based on four assays: Abbott Architect SARS-Cov-2 IgG assay (target nucleocapsid) (Abbott-NP) and three in-house IgG ELISA assays (target spike glycoprotein (Spike), spike receptor binding domain (RBD), and nucleocapsid (NP)) based on thresholds set by the manufacture or 3-standarddeviations from the negative mean. We compared seroprevalence rates by multiple composite reference standards (CRS) and by a series of Bayesian Latent Class Models (BLCM) (using uninformative, weakly and informative priors). Using the BLCM we estimated assay characteristics, bimonthly to evaluate changes over time. Results/Findings: In total, 8999 blood samples were tested. The Abbott-NP assay consistently estimated seroprevalence to be lower than the ELISA-based assays. A priori, choosing a combination of 2 assays resulted in a range of seroprevalence estimates that ranged from 0.2% to 0.5% in April to 0.4% to 1.5% in September. From 16 possible diagnostic phenotypes, 13 were observed, only 33 samples (0.4%) were positive by all four assays. BLCM with non-informative priors provided the best model fit and predicted seroprevalence increased from 0.7% (95% CrI;0.6, 0.8%) in April/May to 1.0% (0.8, 1.1%) in June/ July to 1.5% (1.3, 1.8) in August/September. Assay characteristics varied considerably over time. Overall RBD had the highest sensitivity 82.2% (69.3, 92.9%) with a specificity of 99.6% (99.4, 99.7%). In contrast the sensitivity of the Abbott-NP assay was the lowest and waned from 63.2% (41.4, 83.1%) in April/May to 33.9% (19.7, 53.1%) by August/September. Conclusions: Regardless of the analytical method we found at the end of the first COVID-19 wave, SARSCoV- 2 seroprevalence among a healthy population of blood donors was low (<2%). While the sensitivity of all assays waned, the rates did vary. We found significant limitations to using a single assay to estimate SARSCoV- 2 seroprevalence in a low prevalence setting, such as healthy Canadian blood donors during the first wave of the COVID-19 pandemic.
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COVID-19 Pandemic facilitates the development of online courses on a large scale. As a supplementary teaching in a special period, online courses play a gratifying role in enabling students to learn repeatedly and master effectively. However, it cannot be ignored that there are still some problems, and we need to find better solutions. This article takes the 'Artificial Intelligence' course as an example, through a set of data analysis, puts forward the improvement plan of the network course, and discusses the 'before and after class' teaching mode in blended learning. Through this way we could have a glimpse of the future education, which makes full use of online courses and uses online courses as an icing on the cake for traditional classrooms. © 2020 IEEE.
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Background: COVID-19(2019 novel coronavirus disease)has brought tremendous pressure to the prevention and control of the national epidemic due to its concealed onset, strong infectivity and fast transmission speed. Methods: In this retrospective study, 226 patients diagnosed with 2019 novel coronavirus pneumonia (NCP) in the Chongqing University Three Gorges Hospital were included. The patients' clinical data, including general information, initial symptoms at the onset, time of disease diagnosis, time to treatment in hospital, time of nucleic acid conversion to negative, disease classification, total time of hospitalization were collected. The clinical data of the mild and severe patients were compared. Results: Fever, cough, sore throat, poor appetite andfatigue were the main symptoms of the diagnosed patients. The time of diagnosis was significantly shorter in the mild patients (4.96 ± 4.10 days) than severe patients (7.63 ± 9.17 days) (P=0.004). Mild patients had shorter time to treatment in hospital (6.09 ± 4.47 vs. 8.71 ± 9.04 days) and less time of nucleic acid conversion to negative (7.58 ± 2.51 vs. 11.6 ± 4.67 days) compared to the severe patients. Conclusion: The above results can be used as a quantitative basis for the “five-early"(early detection, early screening, early diagnosis, early isolation treatment, and early recovery) model. The government, the masses, and the hospitals' joint prevention and optimization of the "five-early" model will provide important scientific reference for further prevention and control of the epidemics. © 2020, Iranian Journal of Public Health. All rights reserved.