Natural and cross-inducible anti-SIV antibodies in Mauritian cynomolgus macaques
Bibliographic record
Abstract
Cynomolgus macaques are an increasingly important nonhuman primate model for HIV vaccine research. SIV-free animals without pre-existing anti-SIV immune responses are generally needed to evaluate the effect of vaccine-induced immune responses against the vaccine epitopes. Here, in order to select such animals for vaccine studies, we screened 108 naïve female Mauritian cynomolgus macaques for natural (baseline) antibodies to SIV antigens using a Bio-Plex multiplex system. The antigens included twelve 20mer peptides overlapping the twelve SIV protease cleavage sites (-10/+10), respectively (PCS peptides), and three non-PCS Gag or Env peptides. Natural antibodies to SIV antigens were detected in subsets of monkeys. The antibody reactivity to SIV was further confirmed by Western blot using purified recombinant SIV Gag and Env proteins. As expected, the immunization of monkeys with PCS antigens elicited anti-PCS antibodies. However, unexpectedly, antibodies to non-PCS peptides were also induced, as shown by both Bio-Plex and Western blot analyses, while the non-PCS peptides do not share sequence homology with PCS peptides. The presence of natural and vaccine cross-inducible SIV antibodies in Mauritian cynomolgus macaques should be considered in animal selection, experimental design and result interpretation, for their best use in HIV vaccine research.
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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 | 0.001 | 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) | 0.001 | 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".