RECOMBINANT HUMAN SOLUBLE TUMOR NECROSIS FACTOR-ALPHA RECEPTOR FUSION PROTEIN PARTLY ATTENUATES VENTILATOR-INDUCED LUNG INJURY
Bibliographic record
Abstract
Ventilator-induced lung injury is mediated, at least in part, by TNF-alpha. We determined the effect of a recombinant human soluble TNF receptor fusion protein (etanercept) on mechanical ventilation (MV)-induced changes in a murine ventilator-induced lung injury model. After pretreatment with etanercept or placebo, C57Bl/6 mice were anesthetized and randomized to MV with either low tidal volumes (VT, approximately 7.5 mL/kg) or high VT ( approximately 15 mL/kg) for 5 h. Instrumented but spontaneously breathing mice served as controls. End points were lung wet-to-dry ratios, lung histopathology scores, protein levels, neutrophil cell counts and thrombin-antithrombin complex levels in bronchoalveolar lavage fluid (BALF), and cytokine levels in lung homogenates. The number of caspase 3-positive cells was used as a measure for apoptosis. Etanercept treatment attenuated MV-induced changes, in particular, in MV with high VT. Compared with placebo, etanercept reduced the number of neutrophils in BALF and thrombin-antithrombin complex levels in BALF and cytokine levels in lung homogenates. Lung wet-to-dry ratios, histopathology scores, and local protein levels in BALF, however, were not influenced by etanercept treatment. The number of caspase 3-positive cells was significantly higher in etanercept-treated animals. Inhibition of TNF by etanercept attenuates, in part, MV-induced changes.
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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) | 0.001 | 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) | 0.001 | 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".