Inducible Activation of TLR4 Confers Resistance to Hyperoxia-Induced Pulmonary Apoptosis
Bibliographic record
Abstract
TLRs are essential mediators of host defense against infection via recognition of unique microbial structures. Recent observations indicate that TLR4, the principal receptor for bacterial LPS, may also be activated by noninfectious stimuli including host-derived molecules and environmental oxidant stress. In mice, susceptibility to ozone-induced lung permeability has been linked to the wild-type allele of TLR4, whereas deficiency of TLR4 predisposes to lethal lung injury in hyperoxia. To precisely characterize the role of lung epithelial TLR4 expression in the host response to oxidant stress, we have created an inducible transgenic mouse model that targets the human TLR4 signaling domain to the airways. Exposure of induced transgenic mice to hyperoxia revealed a significant reduction in pulmonary apoptosis compared with controls. This phenotype was associated with sustained up-regulation of antiapoptotic molecules such as heme oxygenase-1 and Bcl-2, yet only transient activation of the transcription factor NF-kappaB. Specific in vivo knockdown of pulmonary heme oxygenase-1 or Bcl-2 expression by intranasal administration of short interfering RNA blocked the effect of TLR4 signaling on hyperoxia-induced lung apoptosis. These results define a novel role for lung epithelial TLR4 as a modulator of cellular apoptosis in response to oxidant stress.
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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) | 0.002 | 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".