Changes in proteoglycans and lung tissue mechanics during excessive mechanical ventilation in rats
Bibliographic record
Abstract
Excessive mechanical ventilation results in changes in lung tissue mechanics. We hypothesized that changes in tissue properties might be related to changes in the extracellular matrix component proteoglycans (PGs). The effect of different ventilation regimens on lung tissue mechanics and PGs was examined in an in vivo rat model. Animals were anesthetized, tracheostomized, and ventilated at a tidal volume of 8 (VT(8)), 20, or 30 (VT(30)) ml/kg, positive end-expiratory pressure of 0 (PEEP(0)) or 1.5 (PEEP(1.5)) cmH(2)O, and frequency of 1.5 Hz for 2 h. The constant-phase model was used to derive airway resistance, tissue elastance, and tissue damping. After physiological measurements, one lung was frozen for immunohistochemistry and the other was reserved for PG extraction and Western blotting. After 2 h of mechanical ventilation, tissue elastance and damping were significantly increased in rats ventilated at VT(30)PEEP(0) compared with control rats (ventilated at VT(8)PEEP(1.5)). Versican, basement membrane heparan sulfate PG, and biglycan were all increased in rat lungs ventilated at VT(30)PEEP(0) compared with control rats. At VT(30)PEEP(0), heparan sulfate PG and versican staining became prominent in the alveolar wall and airspace; biglycan was mostly localized in the airway wall. These data demonstrate that alterations in lung tissue mechanics with excessive mechanical ventilation are accompanied by changes in all classes of extracellular matrix PG.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".