Surrogate neutralization responses following severe acute respiratory syndrome coronavirus 2 vaccination in people with HIV: comparison between inactivated and mRNA vaccine
Bibliographic record
Abstract
OBJECTIVE: People with HIV (PWH) co-infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are at higher odds of severe diseases. Whereas the immunogenicity of mRNA vaccine and adenovirus-vectored vaccine was similar between PWH in stable condition and healthy adults, the effects of inactivated vaccines are not known. DESIGN: Prospective longitudinal observational study in real-world setting. METHODS: Adult PWH in care and planning to receive either inactivated (day 0 and day 28) or mRNA-based (day 0 and day 21) vaccine against SARS-CoV-2 were recruited, with blood samples collected over 6 months for surrogate virus neutralization test (sVNT). Demographic and clinical data including age, sex, CD4 + cell count, and suppressed viral load (SVL) status were transcribed for analyses, by simple and multivariable linear regression models, and multivariable linear generalized estimating equations (GEE). RESULTS: A total of 611 HIV patients, 91% male patients, were recruited, of whom 423 and 184 have received mRNA-based and inactivated vaccine, respectively. The seroconversion rate was 99% for mRNA-based vs, 86% for inactivated vaccine [odds ratio (OR) = 21.56, P = 0.004]. At 6 months, mRNA-based vaccine continued to give a higher response (94 vs. 57%, P < 0.001). The temporal pattern varied between the two vaccines. By GEE, mRNA-based vaccine ( B = 40.59, P < 0.001) and latest SVL status ( B = 10.76, P = 0.01) were positively associated with sVNT level, but not latest CD4 + cell count. CONCLUSION: In HIV patients, inactivated vaccine gave a lower peak and shorter duration of sVNT responses compared with mRNA vaccine. The results suggested that different strategies may be needed in boosting the immunity in anticipation of the emergence of variants in the community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".