Comparison of the TLR7 and TLR9 innate immune signaling pathways in african green monkeys and rhesus macaques
Bibliographic record
Abstract
Abstract Innate immunity is an evolutionary mechanism which is involved in the initial detection of pathogens and stimulates the first line of host defense. The Toll-like receptor (TLR) family has been the most extensively studied innate immune pathway and it has been shown that TLRs can be activated in response to virtually any microbe which invades the host. These innate immune responses play a critical role in the development of pathogen-specific adaptive immune responses and disease pathology and are targeted by the use of adjuvants in modern vaccines. Importantly, divergent TLR7 and TLR9 signaling pathways between rhesus macaques and sooty mangabeys have been shown to distinguish between pathogenic and nonpathogenic AIDS virus infections in those species. Therefore, it is important to understand the TLR signaling pathways of non-human primate (NHP) species that are currently used for infectious disease and vaccine research. In the present study, we administered defined TLR7 and TLR9 agonists to two distinct NHP species, african green monkeys and rhesus macaques. We evaluated B cell, T cell, NK cell, monocyte, plasmacytoid dendritic cell and myeloid dendritic cell activation at 0, 24, 72 and 168 hours after dosing. Gene expression profiling and full pathway analysis was also evaluated to illuminate fundamental aspects of innate immune responses that underlie the mechanism of action of TLR-agonist adjuvants in these two NHP species.
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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 | 0.000 |
| 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".