Inhibition of pulmonary nuclear factor kappa-B decreases the severity of acute Escherichia coli pneumonia but worsens prolonged pneumonia
Bibliographic record
Abstract
INTRODUCTION: Nuclear factor (NF)-κB is central to the pathogenesis of inflammation in acute lung injury, but also to inflammation resolution and repair. We wished to determine whether overexpression of the NF-κB inhibitor IκBα could modulate the severity of acute and prolonged pneumonia-induced lung injury in a series of prospective randomized animal studies. METHODS: Adult male Sprague-Dawley rats were randomized to undergo intratracheal instillation of (a) 5 × 10⁹ adenoassociated virus (AAV) vectors encoding the IκBα transgene (5 × 10⁹ AAV-IκBα); (b) 1 × 10¹⁰ AAV-IκBα; (c) 5 × 10¹⁰ AAV-IκBα; or (d) vehicle alone. After intratracheal inoculation with Escherichia coli, the severity of the lung injury was measured in one series over a 4-hour period (acute pneumonia), and in a second series after 72 hours (prolonged pneumonia). Additional experiments examined the effects of IκBα and null-gene overexpression on E. coli-induced and sham pneumonia. RESULTS: In acute pneumonia, IκBα dose-dependently decreased lung injury, improving arterial oxygenation and lung static compliance, reducing alveolar protein leak and histologic injury, and decreasing alveolar IL-1β concentrations. Benefit was maximal at the intermediate (1 × 10¹⁰) IκBα vector dose; however, efficacy was diminished at the higher (5 × 10¹⁰) IκBα vector dose. In contrast, IκBα worsened prolonged pneumonia-induced lung injury, increased lung bacterial load, decreased lung compliance, and delayed resolution of the acute inflammatory response. CONCLUSIONS: Inhibition of pulmonary NF-κB activity reduces early pneumonia-induced injury, but worsens injury and bacterial load during prolonged 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".