Pretreatment with perfluorohexane vapor attenuates fMLP-induced lung injury in isolated perfused rabbit lungs
Bibliographic record
Abstract
The authors investigated the protective effects and dose dependency of perfluorohexane (PFH) vapor on leukocyte-mediated lung injury in isolated, perfused, and ventilated rabbit lungs. Lungs received either 18 vol.% (n = 7), 9 vol.% (n = 7), or 4.5 vol.% (n = 7) PFH. Fifteen minutes after beginning of PFH application, lung injury was induced with formyl-Met-Leu-Phe (fMLP). Control lungs (n = 7) received fMLP only. In addition 5 lungs (PFH-sham) remained uninjured receiving 18 vol.% PFH only. Pulmonary artery pressure (mPAP), peak inspiratory pressure (P(max)), and lung weight were monitored for 90 minutes. Perfusate samples were taken at regular intervals for analysis and representative lungs were fixed for histological analysis. In the control, fMLP application led to a significant increase of mPAP, P(max), lung weight, and lipid mediators. Pretreatment with PFH attenuated the rise in these parameters. This was accompanied by preservation of the structural integrity of the alveolar architecture and air-blood barrier. In uninjured lungs, mPAP, P(max), lung weight, and lipid mediator formation remained uneffected in the presence of PFH. The authors concluded that pretreatment with PFH vapor leads to an attenuation of leukocyte-mediated lung injury. Vaporization of perfluorocarbons (PFCs) offers new therapeutic options, making use of their protective and anti-inflammatory properties in prophylaxis or in early treatment of acute lung injury.
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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) | 0.001 | 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".