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Background. We compared neutralizing antibodies in healthy healthcare workers against two different coronavirus 2019 disease (Covid-19) vaccines, a messenger RNA (mRNA)-based vaccine (BNT162b2) and a genetically modified organism (virus vector) vaccine (ChAdOx1), to obtain immunity. In our cohort, we tried to compare the level of neutralizing antibody production after a BNT162b2 vaccine booster and to analyze the level of neutralizing antibody titers for delta and omicron variant. Methods. On November 23 to 25, 2021 and December 23 to 24, 2021, a total of 2,133 HCWs at Soonchunhyang University Bucheon Hospital had been vaccinated with BNT162b2 (Pfizer-BioNTech). Among the 115 HCWs who participated in the preceding study, all participants had no history of Covid-19 infection or suspected symptoms at the time of registration. We collected blood samples from participants four weeks after a third dose. All blood samples were analyzed using the commercial virus neutralization test kit (Genscript Biotech Corporation, Piscataway, NJ, USA). Results. Titers were measured 2 months after the initial vaccination and before the booster of the BNT162b2 vaccine (i.e. 6 months after the initial vaccination), and 4 weeks after the booster. Neutralizing antibody level measured by percentage inhibition of surrogate virus neutralization test (sVNT) readings at negative control (unvaccinated) and 4 weeks after booster vaccination. BNT/BNT/BNT group was defined as a booster dose with BNT162b2 (Pfizer) after two doses of BNT162b2 vaccine (Pfizer) 3 weeks apart. ChA/ChA/BNT group was defined as a booster dose with BNT162b2 (Pfizer) after two doses of ChAdOx1 vaccine (AstraZeneca/Oxford) 12 weeks apart. Conclusion. We found that the neutralizing antibody levels against omicron variant were higher among those who received a booster dose of the BNT162b2 vaccine.
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Background. The coronavirus disease 2019 (COVID-19) outbreak reached peak levels in South Korea as the Delta variant, dominant from late August until the end of 2021, was rapidly overtaken by the Omicron variant at the start of 2022. In studies conducted near the start of the pandemic, the occurrence of bacteremia in COVID-19 patients was relatively low. We aimed to determine if there was a change in the rate of bacteremia in COVID-19 patients with the progression of the pandemic. Methods. We performed a retrospective study of patients who were diagnosed with COVID-19 at a referral hospital between September 2021 and March 2022. Blood culture results were recorded, along with demographic characteristics and clinical outcomes. Contamination was considered when a single blood culture was positive for coagulase-negative staphylococci (CoNS), Corynebacterium species, or Bacillus species. Clinically relevant bacteremia was defined as bacteremia due to clinically significant pathogens between 7 days before COVID-19 diagnosis to 14 days after diagnosis. Results. Among the 360 patients included in the study, 46 cases from 43 (11.9%) patients were considered to be clinically relevant bacteremia. Enterococcus faecalis (17.4%) and CoNS (17.4%) were the most common pathogens, followed by Acinetobacter baumannii (15.2%), Staphylococcus aureus (10.7%) and Escherichia coli (10.7%). The median number of days from COVID-19 diagnosis to identification of bacteremia was 2 days. There was no significant difference in the rate of bacteremia between the Delta (September-December 2021) and Omicron variant eras (January-March 2022) (12.2% vs. 11.7%, P = .88). In the subgroup analysis of patients who received more than 2 days of intensive care, there was no statistical difference in the rate of bacteremia (14.5% [9/62] in the Delta variant era vs. 16.9% [14/83] in the Omicron variant era;P = .70). Mortality was significantly higher in patients with clinically relevant bacteremia (48.8% vs. 19.2%, P < .001). Conclusion. Many COVID-19 patients had bacteremia in the Omicron variant era, especially in the intensive care unit. Clinicians should suspect bacterial coinfection when a COVID-19 patient is clinically aggravated.
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Introduction: The coronavirus pandemic has reset major work trends, making remote teams and working from home the new normal. As workplaces continue to hire, the ability to remotely onboard and train new employees has become essential. Here, we describe Natera’s clinical laboratory experience in creating and implementing a variant curator training plan that integrates shadowing, case-based and simulation-based learning adapted to an individual’s learning preferences. Additionally, it introduces core ACMG/AMP interpretation guidelines and classification concepts critical for curating variants in a carrier screening panel for requisite patient safety. Objective: To describe and explore the efficacy of a virtual training tool for new variant curation employees at Natera, Inc. Materials and Methods: The virtual variant curator training consists of a “MASTER” six-module curriculum and requires the same basic requirements needed for remote working, such as video conferencing capabilities, screen sharing, and document sharing. Results: In 2019, prior to implementing the fully remote MASTER modular training, 5 trainees were hired throughout the year and were trained individually by 2 primary trainers. The final week of training was completed in person and on-site at the San Carlos headquarters such that the complete training time from the first date of hire until completion took an average of 30.20 ± 7.56 days (range: 22–38). In 2020, fully remote virtual variant curator training was conducted with 9 trainees in cohorts of 5, 2, and 2. The data show an average training time of 19.22 ± 6.08 days (range: 14–30 days), which was significantly shorter than the previous year, p = 0.0115;Δ = 10.98;df = 12. The training time required by the first cohort (n = 5), trained by 2 trainers, was shorter at 15.60 ± 1.34 days, compared to the following 2 cohorts (n = 2 each), trained by 3 trainers, 23.75 ± 6.85 days. Of note, the first cohort joined during a period of increased novel variant volumes associated with increasing the panel size from 27 to 274 genes, and a need for new hires to complete training rapidly. Despite the difference in training time due to production demand, the overall curriculum is reinforced through the later module of continued learning. No significant difference in training time was observed between the 2020 trainees who had prior experience (n = 6, mean: 17.83 ± 6.05 days) with variant curation, compared to those with no prior experience (n = 3, mean:22.00 ± 6.24), p = 0.3665;Δ = 4.17;df = 7.(Table Presented)Conclusions: This study provides a practical and applicable training framework for organizations employing variant curation teams who work remotely. The design and implementation of the MASTER module enables seamless training and onboarding of variant curators.
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Background Cardiovascular diseases (CVDs) are consistently ranked among the leading causes of death among older adults in Ireland. COVID-19 and influenza infection are associated with cardiovascular complications. However, percentage of deaths caused by CVD among adults aged 75 and over in Ireland decreased from 32.9% to 31.0% from 2019 to 2020. Government-imposed social distancing measures resulted in abolition of influenza activity (IA). We analysed population data from the 2010/11–2019/20 influenza seasons to estimate the impact of reduced IA on CVD mortality rates during the COVID-19 pandemic season. Methods Quarterly mortality data for acute myocardial infarction (AMI) and cerebrovascular disease from first quarter (Q1) 2010 to fourth quarter (Q4) 2020 was obtained from the Central Statistics Office. Weekly data on influenza-like illness (ILI) rates and positive percentages (PP) (i.e. proportion of influenza-positive sentinel respiratory specimens) from week 40 2010 to week 20 2020 was obtained from the Health Protection Surveillance Centre. Excess mortality rate during influenza season was calculated as the percentage difference between Q4/Q1 and preceding third quarter (Q3) mortality rates. We adopted the Goldstein index (ILI rate × PP) as an indicator of IA. Time series analyses, Pearson correlation coefficients (r) and linear regression models were used to evaluate the relationships between IA and excess AMI and cerebrovascular disease mortality rates. Results Statistically significant positive associations were observed between IA and excess AMI (r = 0.557, p = 0.011) and cerebrovascular disease (r = 0.858, p < 0.001) mortality rates. Linear regression models predicted 0.072% (95% confidence interval 0.019%, 0.125%) and 0.095% (0.067%, 0.123%) increases in excess AMI and cerebrovascular disease mortality rates respectively per unit increase in IA levels. Conclusion Elimination of IA may have contributed towards limiting the effects of COVID-19 on CVD mortality rates, and consequently total excess mortality, among older adults in Ireland.