Cervico-Vaginal Inflammatory Cytokine and Chemokine Responses to Two Different SIV Immunogens
Bibliographic record
Abstract
Studies have shown that vaccine vectors and route of immunization can differentially activate different arms of the immune system. However, the effects of different HIV vaccine immunogens on mucosal inflammation have not yet been studied. Because mucosal sites are the primary route of HIV infection, we evaluated the cervico-vaginal inflammatory cytokine and chemokine levels of Mauritian cynomolgus macaques following immunization and boost using two different SIV vaccine immunogens. The PCS vaccine delivers 12 20-amino acid peptides overlapping the 12 protease cleavage sites, and the Gag/Env vaccine delivers the full Gag and full Env proteins of simian immunodeficiency virus. We showed that the PCS vaccine prime and boosts induced short-lived, lower level increases of a few pro-inflammatory/chemotactic cytokines. In the PCS-vaccine group only the levels of MCP-1 were significantly increased above the baseline (P=0.0078, Week 6; P=0.0078, Week 17; P=0.0234; Week 51) following multiple boosts. In contrast, immunizations with the Gag/Env vaccine persistently increased the levels of multiple cytokines/chemokines. In the Gag/Env group, higher than baseline levels were consistently observed for IL-8 (P=0.0078, Week 16; P=0.0078, Week 17; P=0.0156, Week 52), IL-1β (P=0.0234, Week 16; P=0.0156, Week 17; P=0.0156, Week 52), and MIP-1α (P=0.0313, Week 16; P=0.0156, Week 17; P=0.0313, Week 52). Over time, repeated boosts altered the relative levels of these cytokines between the Gag/Env and PCS vaccine group. 18 weeks after final boost with a higher dosage, IP-10 levels (P=0.0313) in the Gag/Env group remained higher than baseline. Thus, the influence of vaccine immunogens on mucosal inflammation needs to be considered when developing and evaluating candidate HIV vaccines.
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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 | 0.000 |
| 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) | 0.002 | 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".