High driving pressure ventilation induces pulmonary hypertension in a rabbit model of acute lung injury
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background Mechanical ventilation may cause pulmonary hypertension in patients with acute lung injury (ALI), but the underlying mechanism remains elucidated. Methods ALI was induced in rabbits by a two-hit injury, i.e., hydrochloric acid aspiration followed by mechanical ventilation for 1 h. Rabbits were then ventilated with driving pressure of 10, 15, 20, or 25 cmH 2 O for 7 h. Clinicopathological parameters were measured at baseline and different timepoints of ventilation. RNA sequencing was conducted to identify the differentially expressed genes in high driving pressure ventilated lung tissue. Results The two-hit injury induced ALI in rabbits was evidenced by dramatically decreased PaO 2 /FiO 2 in the ALI group compared with that in the control group (144.5 ± 23.8 mmHg vs. 391.6 ± 26.6 mmHg, P < 0.001). High driving pressure ventilation (20 and 25 cmH 2 O) significantly elevated the parameters of acute pulmonary hypertension at different timepoints compared with low driving pressure (10 and 15 cmH 2 O), along with significant increases in lung wet/dry ratios, total protein contents in bronchoalveolar lavage fluid, and lung injury scores. The high driving pressure groups showed more pronounced histopathological abnormalities in the lung compared with the low driving pressure groups, accompanied by significant increases in the cross-sectional areas of myocytes, right ventricular weight/body weight value, and Fulton’s index. Furthermore, the expression of the genes related to ferroptosis induction was generally upregulated in high driving pressure groups compared with those in low driving pressure groups. Conclusions A rabbit model of ventilation-induced pulmonary hypertension in ALI was successfully established. Our results open a new research direction investigating the exact role of ferroptosis in ventilation-induced pulmonary hypertension in ALI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it