Covid-19 vaccine immunogenicity in people living with HIV-1
Bibliographic record
Abstract
Abstract Introduction COVID-19 vaccine efficacy has been evaluated in large clinical trials and in real-world situation. Although they have proven to be very effective in the general population, little is known about their efficacy in immunocompromised patients. HIV-infected individuals’ response to vaccine may vary according to the type of vaccine and their level of immunosuppression. We evaluated immunogenicity of an mRNA anti-SARS CoV-2 vaccine in HIV-positive individuals. Methods HIV-positive individuals (n=121) were recruited from HIV clinics in Montreal and stratified according to their CD4 counts. A control group of 20 health care workers naïve to SARS CoV-2 was used. The participants’ Anti-RBD IgG responses were measured by ELISA at baseline and 3 to 4 weeks after receiving the first dose of an mRNA vaccine). Results Eleven of 121 participants had anti-COVID-19 antibodies at baseline, and a further 4 had incomplete data for the analysis. Mean anti-RBD IgG responses were similar between between the HIV negative control group (n=20) and the combined HIV+ group (n=106) (p = 0.72). However, these responses were significantly lower in the group with <250 CD4 cells/mm 3 . (p<0.0001). Increasing age was independently associated with decreased immunogenicity. Conclusion HIV-positive individuals with CD4 counts over 250 cells/mm 3 have an anti-RBD IgG response similar to the general population. However, HIV-positive individuals with the lowest CD4 counts (<250 cells/mm 3 ) have a weaker response. These data would support the hypothesis that a booster dose might be needed in this subgroup of HIV-positive individuals, depending on their response to the second dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".