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Record W3044593649 · doi:10.1101/2020.07.16.20155663

Humoral Response Dynamics Following Infection with SARS-CoV-2

2020· preprint· en· W3044593649 on OpenAlex
Louis Grandjean, Anja Saso, Arturo Fraile Torres, Tanya Lam, James Hatcher, Rosie Thistlethwayte, Mark Harris, Timothy Best, Marina Johnson, Helen R. Wagstaffe, Elizabeth Ralph, Annabelle Mai, Caroline Colijn, Judith Breuer, Matthew Buckland, Kimberly Gilmour, David Goldblatt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsSimon Fraser University
FundersGreat Ormond Street Hospital CharityGreat Ormond Street Institute of Child HealthNational Institute for Health and Care ResearchGovernment of the United KingdomWellcome Trust
KeywordsSeroprevalenceAntibodySerologyAntibody titerImmunologyMedicineTiterVirologyPopulationSeroconversionBiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) specific antibodies have been shown to neutralize the virus in-vitro. Understanding antibody dynamics following SARS-CoV-2 infection is therefore crucial. Sensitive measurement of SARS-CoV-2 antibodies is also vital for large seroprevalence surveys which inform government policies and public health interventions. However, rapidly waning antibodies following SARS-CoV-2 infection could jeopardize the sensitivity of serological testing on which these surveys depend. Methods This prospective cohort study of SARS-CoV-2 humoral dynamics in a central London hospital analyzed 137 serial samples collected from 67 participants seropositive to SARS-CoV-2 by the Meso-Scale Discovery assay. Antibody titers were quantified to the SARS-CoV-2 nucleoprotein (N), spike (S-)protein and the receptor-binding-domain (RBD) of the S-protein. Titers were log-transformed and a multivariate log-linear model with time-since-infection and clinical variables was fitted by Bayesian methods. Results The mean estimated half-life of the N-antibody was 52 days (95% CI 42-65). The S- and RBD-antibody had significantly longer mean half-lives of 81 days (95% CI 61-111) and 83 days (95% CI 55-137) respectively. An ACE-2-receptor competition assay demonstrated significant correlation between the S and RBD-antibody titers and ACE2-receptor blocking in-vitro. The time-to-a-negative N-antibody test for 50% of the seropositive population was predicted to be 195 days (95% CI 163-236). Discussion After SARS-CoV-2 infection, the predicted half-life of N-antibody was 52 days with 50% of seropositive participants becoming seronegative to this antibody at 195 days. Widely used serological tests that depend on the N-antibody will therefore significantly underestimate the prevalence of infection following the majority of infections. Significance statement We believe that our study has significant and urgent public health and translational impact. Firstly, our findings demonstrate that the half-life of the SARS-CoV-2 nucleoprotein antibody is only 52 days. This has immediate and important implications for large-scale seroprevalence surveys, government policy and mathematical modelling predictions which rely on serological tests that target this antibody. Secondly, the slower decay of the SARS-CoV-2 spike protein antibody identified in this study makes assays to the spike protein a more reliable target for serological assays in the longer term. We demonstrate a strong positive linear correlation between spike/RBD antibody and ACE-2 receptor binding in vitro. Our findings are therefore likely to reflect the time to loss of a functional antibody response in SARS-CoV-2. Funding GOSH charity, Wellcome Trust (201470/Z/16/Z and 220565/Z/20/Z). GOSH NIHR Funded Biomedical Research Centre. Trial registration number NCT04380896.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.368
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it