TRIF is essential for the clearance of Pseudomonas aeruginosa from the mouse lung
Bibliographic record
Abstract
Toll‐IL‐1 receptor domain‐containing adapter‐inducing IFN‐β (TRIF) and MyD88 are adaptor molecules that mediate two distinct Toll‐like receptor (TLR) signaling pathways. The MyD88 pathway is critical for the defense against a number of bacteria. Using a MyD88‐deficient animal model, we showed an important role of the MyD88 pathway in the host defense against Pseudomonas aeruginosa lung infection. Roles of TRIF in the host defense have largely been associated with virus infections. Here we investigated a role of TRIF in P. aeruginosa lung infection. TRIF deficient mice showed severe impairment in the clearance of P. aeruginosa from the lung when compared with wild type mice. A TRIF deficiency impairs production of a selective profile of cytokines and chemokines following P. aeruginosa lung infection. Interestingly, TRIF deficient and TRIF‐MyD88 double deficient mice showed similar levels of impairment in the clearance of P. aeruginosa from the lung and cytokine production, suggesting a major role of TRIF in P. aeruginosa lung infection. This study demonstrates for the first time that TRIF pathway is involved in the host defense against P. aeruginosa lung infection. Thus, the full development of host responses to P. aeruginosa lung infection requires both TRIF‐ and MyD88‐dependent mechanisms. Research funded by grants from CIHR, CCFF amd the IWK Health Centre
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".