Alterations in expression of elastogenic and angiogenic genes by different conditions of mechanical ventilation in newborn rat lung
Bibliographic record
Abstract
Mechanical ventilation is an important risk factor for development of bronchopulmonary dysplasia. Here we investigated the effects of different tidal volumes (VT) and duration of ventilation on expression of genes involved in alveolarization [tropoelastin (Eln), lysyloxidase-like 1 (Loxl1), fibulin5 (Fbln5), and tenascin-C (Tnc)] and angiogenesis [platelet derived growth factors (Pdgf) and vascular endothelial growth factors (Vegf) and their receptors] in 8-day-old rats. First, pups were ventilated for 8 h with low (LVT: 3.5 ml/kg), moderate (MVT: 8.5 ml/kg), or high (HVT: 25 ml/kg) tidal volumes. LVT and MVT decreased Tnc expression, whereas HVT increased expression of all three elastogenic genes and Tnc. PDGF α-receptor mRNA was increased in all ventilation groups, while Pdgfb expression was decreased after MVT and HVT ventilation. Only HVT ventilation upregulated Vegf expression. Independent of VT, ventilation upregulated Vegfr1 expression, while MVT and HVT downregulated Vegfr2 expression. Next, we evaluated duration (0-24 h) of MVT ventilation on gene expression. Although expression of all elastogenic genes peaked at 12 h of ventilation, only Fbln5 was negatively affected at 24 h. Tnc expression decreased with duration of ventilation. Changes in expression of Pdgfr and Vegfr were maximal at 8 h of ventilation. Disturbed elastin fiber deposition and decrease in small vessel density was only observed after 24 h. Thus, an imbalance between Fbln5 and Eln expression may trigger dysregulated elastin fiber deposition during the first 24 h of mechanical ventilation. Furthermore, ventilation-induced alterations in Pdgf and Vegf receptor expression are tidal volume dependent and may affect pulmonary vessel formation.
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 | 0.000 |
| 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 | 0.001 |
| 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".