A Role of Toll-IL-1 Receptor Domain-Containing Adaptor-Inducing IFN-β in the Host Response to <i>Pseudomonas aeruginosa</i> Lung Infection in Mice
Bibliographic record
Abstract
Toll-IL-1R domain-containing adaptor-inducing IFN-beta (TRIF) is an adaptor molecule that mediates a distinct TLR signaling pathway. Roles of TRIF in the host defense have been primarily associated with virus infections owing to the induction of IFN-alphabeta. In this study, we investigated a role of TRIF in Pseudomonas aeruginosa infection. In vitro, TRIF-deficient mouse alveolar and peritoneal macrophages showed a complete inhibition of RANTES (CCL5) production, severely impaired TNF and KC (CXCL1) production, and reduced NF-kappaB activation in response to P. aeruginosa stimulation. In vivo, TRIF-deficient mice showed a complete inhibition of RANTES production, a severely impaired TNF and KC production, and an efficient MIP-2 and IL-1beta production in the lung following P. aeruginosa infection. This outcome was associated with a delayed recruitment of neutrophils into the airways. These results suggest that TRIF mediates a distinct cytokine/chemokine profile in response to P. aeruginosa infection. P. aeruginosa-induced RANTES production is completely dependent on TRIF pathway in mice. Importantly, TRIF deficiency leads to impaired clearance of P. aeruginosa from the lung during the initial 24-48 h of infection. Thus, TRIF represents a novel mechanism involved in the development of host response to P. aeruginosa infection.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".