Anti-HIV Antibody Responses and the HIV Reservoir Size during Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: A major challenge to HIV eradication strategies is the lack of an accurate measurement of the total burden of replication-competent HIV (the "reservoir"). We assessed the association of anti-HIV antibody responses and the estimated size of the reservoir during antiretroviral therapy (ART). METHODS: We evaluated anti-HIV antibody profiles using luciferase immunoprecipitation systems (LIPS) assay in relation to several blood-based HIV reservoir measures: total and 2-LTR DNA (rtPCR or droplet digital PCR); integrated DNA (Alu PCR); unspliced RNA (rtPCR), multiply-spliced RNA (TILDA), residual plasma HIV RNA (single copy PCR), and replication-competent virus (outgrowth assay). We also assessed total HIV DNA and RNA in gut-associated lymphoid tissue (rtPCR). Spearman correlations and linear regressions were performed using log-transformed blood- or tissue-based reservoir measurements as predictors and log-transformed antibody levels as outcome variables. RESULTS: Among 51 chronically HIV-infected ART-suppressed participants (median age = 57, nadir CD4+ count = 196 cells/mm3, ART duration = 9 years), the most statistically significant associations were between antibody responses to integrase and HIV RNA in gut-associated lymphoid tissue (1.17 fold-increase per two-fold RNA increase, P = 0.004) and between antibody responses to matrix and integrated HIV DNA in resting CD4+ T cells (0.35 fold-decrease per two-fold DNA increase, P = 0.003). However, these associations were not statistically significant after a stringent Bonferroni-adjustment of P<0.00045. Multivariate models including age and duration of ART did not markedly alter results. CONCLUSIONS: Our findings suggest that anti-HIV antibody responses may reflect the size of the HIV reservoir during chronic treated HIV disease, possibly via antigen recognition in reservoir sites. Larger, prospective studies are needed to validate the utility of antibody levels as a measure of the total body burden of HIV during treatment.
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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 | 0.003 |
| 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".