Investigating the Antibody Response to SARS-CoV-2 After Infection or Vaccination
Bibliographic record
Abstract
As of January 2024, the ongoing SARS-CoV-2 pandemic has resulted in over 700 million confirmed infections and 6.9 million deaths worldwide since its emergence in late 2019 as coronavirus disease 2019 (COVID-19). At its onset, there was a lack of high throughput testing for prior infections and a paucity in knowledge regarding the durability of immunity from infection. I worked collaboratively to establish and optimize a scalable enzyme-linked immunosorbent assay (ELISA) that uses a chemiluminescent readout for detecting antibodies of three classes [immunoglobulin G (IgG), IgM & IgA]. This platform detects antibodies that recognize three antigenic targets of SARS-CoV-2: the spike (S) trimer, the S receptor binding domain (RBD) and the nucleocapsid (N) protein. After scaling this assay to an automated platform, we determined that antibodies to natural infection persist for ≥ 3 months and that IgG antibodies in blood are correlated with levels found in saliva. Further, I established and optimized a surrogate neutralization ELISA that serves as a quick and simple protein-based assay to assess neutralizing antibody levels in contrast to other labor-intensive and time-consuming methods. In early 2021, mass public vaccination against COVID-19 began, and I assessed the durability of antibody levels in two vaccinated vulnerable cohorts: dialysis patients and long-term care home (LTCH) residents. A vaccine shortage prompted the delay between two vaccine doses for the general population including dialysis patients but not other vulnerable groups. Our findings showed that the delay in scheduled doses reduced antibody persistence in dialysis patients. Moreover, those who received the mRNA-1273 (Moderna) vaccine had higher binding antibody levels and a more durable response over 12 weeks than those receiving BNT162b2 (Pfizer). In contrast, LTCH residents were prioritized for vaccination, and residents were protected from severe disease; however, outbreaks started to re-emerge upon the circulation of variants of concern. We assessed the potential factors that may have reduced vaccine efficacy and found that the type of vaccine administered, relative age of participant, time since vaccination, and variant of concern were cumulative factors that affected neutralizing antibody titers. The platforms I co-developed have been utilized to investigate the durability of the antibody response to infection and vaccination among a diverse set of cohorts, and these data have been used to direct changes in health policies in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".