Preconditioning Enhances the Paracrine Effect of Mesenchymal Stem Cells in Preventing Oxygen-Induced Neonatal Lung Injury in Rats
Bibliographic record
Abstract
Bronchopulmonary dysplasia (BPD) remains a main complication of extreme prematurity. Bone marrow derived-mesenchymal stem cells (BM-MSC) prevent lung injury in an O(2)-induced model of BPD. The low level of lung BM-MSC engraftment suggests alternate mechanisms-beyond cell replacement-to account for their therapeutic benefit. We hypothesized that BM-MSC prevent O(2)-induced BPD through a paracrine-mediated mechanism and that preconditioning of BM-MSC would further enhance this paracrine effect. To this end, conditioned medium (CM) from BM-MSC (MSCcm) or preconditioned CM harvested after 24 h of BM-MSC exposure to 95% O(2) (MSC-O2cm) were administrated for 21 days to newborn rats exposed to 95% O(2) from birth until postnatal day (P)14. Rat pups exposed to hyperoxia had fewer and enlarged air spaces and exhibited signs of pulmonary hypertension (PH), assessed by echo-Doppler, right ventricular hypertrophy, and pulmonary artery medial wall thickness. Daily intraperitoneal administration of both CM preserved alveolar growth. MSC-O2cm exerted the most potent therapeutic benefit and also prevented PH. CM of lung fibroblasts (control cells) had no effect. MSCcm had higher antioxidant capacity than control fibroblast CM. Preconditioning did not increase the antioxidant capacity in MSC-O2cm but produced higher levels of the naturally occurring antioxidant stanniocalcin-1 in MSC-O2cm. Ex vivo preconditioning enhances the paracrine effect of BM-MSC and opens new therapeutic options for cell-based therapies. Ex vivo preconditioning may also facilitate the discovery of MSC-derived repair molecules.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".