Injurious ventilation induces widespread pulmonary epithelial expression of tumor necrosis factor-α and interleukin-6 messenger RNA*
Bibliographic record
Abstract
OBJECTIVE: We examined the hypothesis that injurious strategies of mechanical ventilation alter the expression and distribution within the lung of tumor necrosis factor-alpha and interleukin-6 that are both duration and ventilation strategy dependent. SUBJECTS: Male Sprague Dawley rats. INTERVENTIONS: Lungs from rats were preserved immediately after death or were randomized to ex vivo ventilation with either a) noninjurious ventilation; b) high end-inspiratory lung volume with positive end-expiratory pressure (PEEP); c) high end-inspiratory lung volume without PEEP; or d) intermediate lung distension without PEEP, for periods ranging from 30 mins to 3 hrs. MEASUREMENT AND MAIN RESULTS: Changes in cytokines were assessed by in situ hybridization, immunocytochemistry, simultaneous in situ hybridization and immunocytochemistry, Northern analysis, and enzyme-linked immunosorbent assay. Whereas minimal expression of tumor necrosis factor-alpha and interleukin-6 mRNA was found in lungs subjected to noninjurious ventilation, the three injurious strategies resulted in a diffuse increase in expression of tumor necrosis factor-alpha and interleukin-6. The principal cells involved were the bronchial, bronchiolar, and alveolar epithelium. The changes in tumor necrosis factor-alpha mRNA and protein expression were dependent on both duration of ventilation and the ventilation strategy used. CONCLUSIONS: The vast pulmonary epithelium is a major contributor to ventilation-induced changes in cytokine production and may play an important role in the pathogenesis of lung injury and systemic sequelae in ventilated subjects.
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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 | 0.000 |
| 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".