Profiling Humoral Immunity After Mixing and Matching COVID-19 Vaccines Using SARS-CoV-2 Variant Protein Microarrays
Bibliographic record
Abstract
In November 2022, 68% of the population received at least one dose of COVID-19 vaccines. Owing to the ongoing mutations, especially for the variants of concern (VOCs), it is important to monitor the humoral immune responses after different vaccination strategies. In this study, we developed a SARS-CoV-2 variant protein microarray that contained the spike proteins from the VOCs, e.g., alpha, beta, gamma, delta, and omicron, to quantify the binding antibody and surrogate neutralizing antibody. Plasmas were collected after two doses of matching AZD1222 (AZx2), two doses of matching mRNA-1273 (Mx2), or mixing AZD1222 and mRNA-1273 (AZ+M). The results showed a significant decrease of surrogate neutralizing antibodies against the receptor-binding domain in all VOCs in AZx2 and Mx2 but not AZ+M. A similar but minor reduction pattern of surrogate neutralizing antibodies against the extracellular domain was observed. While Mx2 exhibited a higher surrogate neutralizing level against all VOCs compared with AZx2, AZ+M showed an even higher surrogate neutralizing level in gamma and omicron compared with Mx2. It is worth noting that the binding antibody displayed a low correlation to the surrogate neutralizing antibody (R-square 0.130-0.382). This study delivers insights into humoral immunities, SARS-CoV-2 mutations, and mixing and matching vaccine strategies, which may provide a more effective vaccine strategy especially in preventing omicron.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".