Lipoteichoic acid is able to inhibit leukocyte recruitment in vivo
Bibliographic record
Abstract
Toll‐like receptors (TLR) are sentinel receptors of the immune system that alert the host to pathogen infiltration and initiate an inflammatory response. Contrary to this paradigm we have shown that the TLR2 ligand, lipoteichoic acid (LTA), isolated from Staphylococcus aureus, is able to actively inhibit acute leukocyte recruitment in vivo. We have employed intravital microscopy of the murine cremaster muscle to monitor leukocyte recruitment into tissue following the induction of acute inflammation. Using this method we have shown that LTA is able to inhibit leukocyte recruitment induced by the TLR4‐ligand, lipopolysaccharide, the TLR2‐ligand, Pam3CSK4, as well as the cytokine, TNFalpha, and the chemokine, MIP2. LTA is unique in this capacity among the TLR2 ligands that we have tested, including; LTA, Pam3CSK4, S‐ and R‐FSL1. Not only do these other TLR2‐ligands not inhibit leukocyte recruitment but they actively induce significant leukocyte recruitment into the tissue. While we were able to demonstrate that the inhibitory capacity of LTA was dependent on TLR2, we have found that the classic adaptor proteins that are associated with TLRs, MyD88 and TRIF, are not required to send this inhibitory signal. Therefore we have identified a physiologically relevant, TLR2‐dependent, anti‐inflammatory pathway that is independent of known TLR signaling cascades.
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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) | 0.001 | 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 | 0.001 |
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".