Selective perfusion of coronary vasculature in preterm sheep: a methodological innovation undermined by unfavourable operation of the foramen ovale
Bibliographic record
Abstract
Antenatal cardiac intervention affords new prospects for hypoplastic left heart syndrome. Its success, however, may come not only from absence of impediments to blood flow but also from a sufficiently developed cardiac wall. Here, we examined the feasibility to perfuse selectively the fetal coronary circulation for treatment with growth promoting agents. Pregnant sheep (94-114 days gestation, term 145 days) were used. An aortic stop-flow procedure was developed for intracoronary access in the nonexposed fetus and human mesenchymal stem cells and their exosomes served as test agents. We found that aortic stop-flow ensures preferential distribution of fluorescent microspheres to the heart. However, intracoronary administration of stem cells or exosomes was detrimental, with fetal demise occurring around surgery or at variable intervals afterwards. Coincidentally, stop-flow caused by itself a marked rise of intraluminal pressure within the occluded aorta along with histological signs of coronary obstruction. We conclude that it is feasible to perfuse selectively the coronary circulation of the preterm fetus, but treatments are not compatible with survival of the animals. The cause for failure is found in the absence of hemodynamic compensation to stop-flow via a left-to-right shunt. This unexpected event is attributed to a largely membranous foramen ovale, characteristic of sheep, that collapses under pressure.
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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 | 0.001 | 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 | 0.001 |
| 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".