Mechanical ventilation causes airway distension with proinflammatory sequelae in mice
Bibliographic record
Abstract
The pathogenesis of ventilator-induced lung injury has predominantly been attributed to overdistension or mechanical opening and collapse of alveoli, whereas mechanical strain on the airways is rarely taken into consideration. Here, we hypothesized that mechanical ventilation may cause significant airway distension, which may contribute to the pathological features of ventilator-induced lung injury. C57BL/6J mice were anesthetized and mechanically ventilated at tidal volumes of 6, 10, or 15 ml/kg body wt. Mice were imaged by flat-panel volume computer tomography, and central airways were segmented and rendered in 3D for quantitative assessment of airway distension. Alveolar distension was imaged by intravital microscopy. Functional dead space was analyzed in vivo, and proinflammatory cytokine release was analyzed in isolated, ventilated tracheae. CT scans revealed a reversible, up to 2.5-fold increase in upper airway volume during mechanical ventilation compared with spontaneous breathing. Airway distension was most pronounced in main bronchi, which showed the largest volumes at tidal volumes of 10 ml/kg body wt. Conversely, airway distension in segmental bronchi and functional dead space increased almost linearly, and alveolar distension increased even disproportionately with higher tidal volumes. In isolated tracheae, mechanical ventilation stimulated the release of the early-response cytokines TNF-α and IL-1β. Mechanical ventilation causes a rapid, pronounced, and reversible distension of upper airways in mice that is associated with an increase in functional dead space. Upper airway distension is most pronounced at moderate tidal volumes, whereas higher tidal volumes redistribute preferentially to the alveolar compartment. Airway distension triggers proinflammatory responses and may thus contribute relevantly to ventilator-induced pathologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".