Humoral Responses Elicited by SARS-CoV-2 mRNA Vaccine in People Living with HIV
Bibliographic record
Abstract
While mRNA SARS-CoV-2 vaccination elicits strong humoral responses in the general population, humoral responses in people living with HIV (PLWH) remain to be clarified. Here, we conducted a longitudinal study of vaccine immunogenicity elicited after two and three doses of mRNA SARS-CoV-2 vaccine in PLWH stratified by their CD4 count. We measured the capacity of the antibodies elicited by vaccination to bind the Spike glycoprotein of different variants of concern (VOCs). We also evaluated the Fc-mediated effector functions of these antibodies by measuring their ability to eliminate CEM.NKr cells stably expressing SARS-CoV-2 Spikes. Finally, we measured the relative capacity of the antibodies to neutralize authentic SARS-CoV-2 virus after the third dose of mRNA vaccine. We found that after two doses of SARS-CoV-2 mRNA vaccine, PLWH with a CD4 count < 250/mm3 had lower levels of anti-RBD IgG antibodies compared to PLWH with a CD4 count > 250/mm3 (p < 0.05). A third dose increased these levels and importantly, no major differences were observed in their capacity to mediate Fc-effector functions and neutralize authentic SARS-CoV-2. Overall, our work demonstrates the importance of mRNA vaccine boosting in immuno-compromised individuals presenting low levels of CD4.
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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 | 0.001 |
| 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 | 0.001 |
| 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".