Primary SARS-CoV-2 exposure by vaccination or infection shapes immune responses to omicron variants among a Spanish cohort
Bibliographic record
Abstract
The comparison between vaccine-induced and infection-acquired adaptive immunity, and their co-occurrence -referred to as "hybrid immunity"- is of great interest and remains an area with significant knowledge gaps. Given that most of the population already has hybrid immunity to COVID-19, a key question is whether the order of infection-acquired and vaccine-induced immunity affects the immune response. Here, we analyze the humoral and T-cell responses in a Spanish cohort with longitudinal blood sampling spanning 2020-2023. We observe higher anti-RBD antibody levels against Omicron in individuals initially exposed to SARS-CoV-2 antigens via vaccination compared to those first exposed through natural infection. This difference diminishes with an increasing number of exposures. The dynamics of antibody levels over time correlate with clinical protection: those first-infected have higher protection early on, whereas those first-vaccinated show greater protection later, especially with the arrival of the Omicron variant. This phenomenon may reflect immune imprinting. In contrast to the humoral response, the T-cell response is higher in individuals first exposed through infection, although T-cell findings may be underpowered because of limited sample size. Our study provides valuable insights into the impact of initial antigen exposure on humoral and cellular responses to SARS-CoV-2.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".