TLR engagement prior to virus infection influences MHC-I antigen presentation in an epitope-dependent manner as a result of nitric oxide release
Bibliographic record
Abstract
Microorganisms contain PAMPs that can interact with different TLR-Ls. Cooperative signals from these receptors may modify innate and adaptive immune responses to invading pathogens. Therefore, a better understanding of the role TLRs play in initiating host defense during infections requires assessing the influence of multiple TLR engagement on pAPC activation and antigen presentation. In this study, we investigated the effects of combined TLR2, TLR3, or TLR4 engagement on DC activation and the presentation of LCMV antigens focusing on the major epitopes derived from NP and GP proteins encoded by the virus. Our results demonstrate that combined TLR ligation affected antigen presentation of NP(205-212), GP(33-41), and GP(276-286), but not NP(396-404). The altered antigen presentation was associated with changes in proteasomal activities and NO production as a result of TLR engagement. Taken together, the data demonstrate that combined TLR ligation could result in changes of innate effectors that may directly influence the adaptive immune response.
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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) | 0.001 | 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".