Differential Roles of MyD88 and TRIF in Hematopoietic and Resident Cells During Murine Gram-Negative Pneumonia
Bibliographic record
Abstract
BACKGROUND: Pneumonia is frequently caused by gram-negative pathogens, among which Klebsiella pneumoniae prominently features. Recognition of pathogen-associated molecular patterns by Toll-like receptors (TLRs) is important for an appropriate immune response during infection. TLR signaling can proceed via 2 distinct routes that are dependent on the adaptor proteins Myeloid differentiation primary response gene (88) (MyD88) and TIR-domain-containing adaptor-inducing interferon-β (TRIF). The aim of the study was to determine the relative contribution of MyD88 and TRIF signaling in resident and hematopoietic cells to host defense during pneumonia. METHODS: Bone marrow chimeras of MyD88 deficient/wild type and TRIF mutant/wild type mice were created and infected with K. pneumoniae via the airways. RESULTS: MyD88 in both resident and hematopoietic cells contributed to survival and antibacterial defense in late-stage infection, whereas only TRIF in hematopoietic cells was protective. On the other hand, resident MyD88 and hematopoietic TRIF contributed to distant cellular injury. Resident MyD88 was pivotal for early chemokine release and neutrophil recruitment in the bronchoalveolar space. CONCLUSIONS: MyD88- and TRIF-dependent signaling has a differential contribution to host defense in different cell types that changes from early- to late-stage gram-negative pneumonia.
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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".