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2.
Nat Microbiol ; 7(8): 1161-1179, 2022 08.
Article in English | MEDLINE | ID: covidwho-1921616

ABSTRACT

Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant.


Subject(s)
COVID-19 , Spike Glycoprotein, Coronavirus , Antibodies, Viral , BNT162 Vaccine , Humans , Membrane Glycoproteins/metabolism , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Viral Envelope Proteins/metabolism , Virus Internalization
3.
Int J Mol Sci ; 23(13)2022 Jun 30.
Article in English | MEDLINE | ID: covidwho-1917517

ABSTRACT

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694-0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740-0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.


Subject(s)
Acute Kidney Injury , COVID-19 , Acute Kidney Injury/diagnosis , Acute Kidney Injury/etiology , Adult , Biomarkers , COVID-19/diagnosis , Hospital Mortality , Humans , Kidney , Retrospective Studies , SARS-CoV-2 , State Medicine
4.
Nat Commun ; 13(1): 2877, 2022 05 26.
Article in English | MEDLINE | ID: covidwho-1864740

ABSTRACT

Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but can lack sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-testing with PCR validation was performed on 1172 symptomatically-identified individuals in their homes. Statistical models were fitted to predict PCR-status using rapid-antigen-test results, syndromic data, and their combination. Under contrasting epidemiological scenarios, the models' predictive and classification performance was evaluated. Models combining rapid-antigen-testing and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios with their best performance in the epidemic growth scenario. These results show that drawing on complementary strengths across rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.


Subject(s)
COVID-19 , Epidemics , Bangladesh/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Humans , Models, Statistical , Sentinel Surveillance
5.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-331670

ABSTRACT

Objective To determine how the severity of successively dominant SARS-CoV-2 variants has changed over the course of the COVID-19 pandemic. Design Prospective cohort analysis. Setting Community- and hospital- sequenced COVID-19 cases in the NHS Greater Glasgow and Clyde (NHS GG&C) Health Board (1.2 million people). Participants All sequenced non-nosocomial adult COVID-19 cases in NHS GG&C identified to be infected with the relevant SARS-CoV-2 lineage during the following analysis periods. B.1.177/Alpha analysis: 1st November 2020 - 30th January 2021 (n = 1640). Alpha/Delta analysis: 1st April - 30th June 2021 (n = 5552). AY.4.2 Delta/non-AY.4.2 Delta analysis: 1st July – 31st October 2021 (n = 9613). Non-AY.4.2 Delta/Omicron analysis: 1st – 31st December 2021 (n = 3858). Main outcome measures Admission to hospital, admission to ICU, or death within 28 days of first positive COVID-19 test Results In the B.1.177/Alpha analysis, 300 of 807 (37.2%) B.1.177 cases were recorded as hospitalised or having a more severe outcome, compared to 232 of 833 (27.9%) Alpha cases. After adjusting for the following covariates: age, sex, time of positive test, comorbidities and partial postcode, the cumulative odds ratio was 1.51 (95% central credible interval 1.08-2.11) for Alpha versus B.1.177. In the Alpha/Delta analysis, 113 of 2104 (5.4%) Alpha cases were recorded as hospitalised or having a more severe outcome, compared to 230 of 3448 (6.7%) Delta cases. After adjusting for the above covariates plus number of vaccine doses and reinfection, the cumulative odds ratio was 2.09 (95% central credible interval 1.42-3.08) for Delta versus Alpha. In the non-AY.4.2 Delta/AY.4.2 Delta analysis, 845 of 8644 (9.8%) non-AY.4.2 Delta cases were recorded as hospitalised or having a more severe outcome, compared to 101 of 969 (10.4%) AY.4.2 Delta cases. After adjusting for the previously stated covariates, the cumulative odds ratio was 0.99 (95% central credible interval 0.76-1.27) for AY.4.2 Delta versus non-AY.4.2 Delta. In the non-AY.4.2 Delta/Omicron analysis, 30 of 1164 (2.6%) non-AY.4.2 Delta cases were recorded as hospitalised or having a more severe outcome, compared to 26 of 2694 (1.0%) Omicron cases. After adjusting for the previously listed covariates, the median cumulative odds ratio was 0.49 (95% central credible interval 0.22-1.06) for Omicron versus non-AY.4.2 Delta. Conclusions The direction of change in disease severity between successively emerging SARS-CoV-2 variants of concern was inconsistent. This heterogeneity in virulence between variants, coupled with independent evolutionary emergence, demonstrates that severity associated with future SARS-CoV-2 variants is inherently unpredictable.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-307962

ABSTRACT

Background: Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases, despite lack of specificity and asymptomatic cases. Rapid antigen testing is inexpensive and easy-to-deploy but concerns remain about sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions.Methods: Community-based volunteers were trained to syndromically assess potential COVID-19 cases in low-income communities in Dhaka, Bangladesh. Rapid antigen tests and PCR validation was performed on 1172 syndromically-identified individuals at their households. Statistical models were fit to predict PCR status using rapid-antigen-test results, syndromic data, and their combination. Model predictive and classification performance was examined under contrasting epidemiological scenarios to evaluate their potential for improving diagnoses.Findings: Models combining rapid-antigen-test and syndromic data yielded equal performance to rapid-antigen-test-only models in the “Agnostic” scenario and performed better under scenarios of “Low Incidence” and “Epidemic Growth”. Under “Epidemic Growth”, the combined model’s false negative rate is 26 (IQR:24-29) percentage points lower than the rapid-antigen-test-only model’s, with a false positive rate <20%. Under “Low Incidence” the combined model’s false positive rate is 27 (IQR:23-32) percentage points lower than the rapid-antigen-test-only model’s, with a false negative rate <20%.Interpretation: Drawing on complementary strengths across two rapid diagnostics, we demonstrate improved COVID-19 detection, and reduced false-positive and -negative diagnoses to match local requirements;improvements achievable without additional expense or changes for patients or practitioners. Widespread mobile health applications facilitate these scalable improvements in accessible diagnostics for use in low-income communities.Funding: This work is supported by a grant from the Bill and Melinda Gates Foun dation to FAO (INV-022851). FJC is funded by EPSRC (EP/R513222/1), DJP by the JUNIPER consortium (MR/V038613/1), and KH by Wellcome (207569/Z/17/Z).Declaration of Interest: The authors declare no competing interests.

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