CT-scan assessment of lung volumes and effect of gravity on lung ventilation during and after total liquid ventilation in piglets
Bibliographic record
Abstract
Total liquid ventilation (TLV) offers hope for helping nano-preterm infants adapt to the extrauterine environment at birth. We aimed to test whether lung volumes could be maintained over time during TLV and whether the prone position altered the distribution of perflubron during and following TLV. CT scan images were acquired in seven newborn piglets during 180 min of TLV, followed by 120 min of weaning. End-expiratory lung volumes increased from 43.0 [38.1, 44.4] cm 3 /kg at baseline to 53.5 [50.6, 67.8] cm 3 /kg (p = 0.036) at 60 min of TLV through recruitment of the dependent (posterior) lung regions. They remained stable thereafter at 55.4 [45.0, 64.5] cm 3 /kg at 150 min (p = 0.5 vs. 60 min). Change to the prone position during TLV did not alter the proportion of the tidal volume distributed to the anterior lung regions (p = 0.9). However, the prone position during weaning (rotating experimental group, n = 3) favored distribution of the gaseous tidal volume to the nondependent lung regions. In conclusion, perflubron did not accumulate in the lungs after the initial recruitment of gravity-dependent regions. In addition, prone positioning did not affect tidal volume distribution during TLV but increased ventilation of the nondependent lung regions during weaning. • Perflubron did not accumulate in the lungs during liquid ventilation. • Prone positioning did not affect tidal volume distribution during liquid ventilation. • During weaning, gaseous tidal volume favored non-dependent lung regions.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".