Cohort profile: A Québec-based plasma donor biobank to study COVID-19 immunity (PlasCoV)
Bibliographic record
Abstract
Abstract Purpose Long-term humoral immunity to COVID-19 is not well understood owing to the continuous emergence of new variants of concern, the evolving vaccine- and infection-induced immunity, and the limited follow-up of previous studies. As the blood service in Québec (Canada), we established in April 2021 a COVID-19-focused biobank. Participants As of January 2022, included 86,229 plasma samples from 15,502 regular donors (age range=18-84 years, female %=49.7%), for an average of 5.6 donations per donor. Nearly two thirds (65.6%) of biobank donors made at least 2 donations, with many donors having provided samples pre- and post-vaccination (3061 [19.75%]) or pre- and post-infection (131 [0.85%]), thus allowing longitudinal studies on vaccine- and infection-induced immunity. Findings to date Comparative analysis of the immune response after the first and second dose of the BNT162b2 COVID-19 vaccine among SARS-CoV-2 naïve and previously infected individuals revealed that a single vaccine dose administered to previously infected individuals yields a maximal immune response. In contrast, SARS-CoV-2 naïve individuals required two vaccine doses to produce a maximal immune response. Furthermore, the results of a four-phase seroprevalence study indicate that the anti-N antibody response wanes quite rapidly, so that up to one third of previously infected donors were seronegative for anti-N. Future plans This plasma biobank from frequent and motivated donors, and the longitudinal nature of the biobank, will provide valuable insights into the anti-SARS-CoV-2 immune response and its persistence in time, and the effect of vaccination and of viral variants on the specificity of the antiSARS-CoV-2 immune response. Strengths and Limitations The herein described biobank has several strengths. To the best of our knowledge, this would be the largest biobank of plasma samples dedicated to COVID-19 research, with >80,000 samples from >15,000 donors and new samples continually being added until at least December 2022. Furthermore, the large subset of donors with ≥2 samples (65.6%) – along with the high frequency of donations in this subset (i.e., median: once every 29.0 days) – enables the conduct of longitudinal analyses on COVID-19 immunity. Another strength is that donors provided a broad consent, which allows researchers to recontact them for other projects (e.g., supplemental questionnaire). Lastly, the cost of establishing the biobank was minimized since the infrastructure and personnel required for sample collection were already in place at our blood collection sites. Given these strengths, our biobank may serve as a model for other blood operators and government partners who would be interested in reproducing our initiative elsewhere. Certain limitations should nonetheless be considered when using our biobank samples. First, only plasma samples are available, such that the biobank cannot be used to study cell-based immunity. Researchers interested in studying cell-based immunity may want to contact BCQ19, which routinely collects peripheral blood mononuclear cells.[1] Second, despite the large sample, the plasma donor population is not fully representative of the overall Québec population, as expected since plasma donors are typically more representative of the healthy adult population. All exclusion criteria for plasma donations were also exclusion criteria for the biobank including immunodeficiencies, active infection, recent cancer among other chronic diseases. Third, the database associated with our biobank does not include information on disease severity, such as hospitalization or intensive care unit admission. Fourth, the database does not include information on socioeconomic status, such as income and education. However, the six-digit zip code can be used to generate a proxy index for socioeconomic status.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".