Imaging the Interaction of Atelectasis and Overdistension in Surfactant-Depleted Lungs*
Bibliographic record
Abstract
OBJECTIVE: Atelectasis and surfactant depletion may contribute to greater distension-and thereby injury-of aerated lung regions; recruitment of atelectatic lung may protect these regions by attenuating such overdistension. However, the effects of atelectasis (and recruitment) on aerated airspaces remain elusive. We tested the hypothesis that during mechanical ventilation, surfactant depletion increases the dimensions of aerated airspaces and that lung recruitment reverses these changes. DESIGN: Prospective imaging study in an animal model. SETTING: Research imaging facility. SUBJECTS: Twenty-seven healthy Sprague Dawley rats. INTERVENTIONS: Surfactant depletion was obtained by saline lavage in anesthetized, ventilated rats. Alveolar recruitment was accomplished using positive end-expiratory pressure and exogenous surfactant administration. MEASUREMENTS AND MAIN RESULTS: Airspace dimensions were estimated by measuring the apparent diffusion coefficient of He, using diffusion-weighted hyperpolarized gas magnetic resonance imaging. Atelectasis was demonstrated using computerized tomography and by measuring oxygenation. Saline lavage increased atelectasis (increase in nonaerated tissue from 1.2% to 13.8% of imaged area, p < 0.001), and produced a concomitant increase in mean apparent diffusion coefficient (~33%, p < 0.001) vs. baseline; the heterogeneity of the computerized tomography signal and the variance of apparent diffusion coefficient were also increased. Application of positive end-expiratory pressure and surfactant reduced the mean apparent diffusion coefficient (~23%, p < 0.001), and its variance, in parallel to alveolar recruitment (i.e., less computerized tomography densities and heterogeneity, increased oxygenation). CONCLUSIONS: Overdistension of aerated lung occurs during atelectasis is detectable using clinically relevant magnetic resonance imaging technology, and could be a key factor in the generation of lung injury during mechanical ventilation. Lung recruitment by higher positive end-expiratory pressure and surfactant administration reduces airspace distension.
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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) | 0.000 | 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 | 0.000 |
| 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".