Aortic valve interstitial cells: an evaluation of cell viability and cell phenotype over time.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: In investigating the mechanisms of aortic valve disease processes and to accurately construct tissue-engineered heart valve prostheses, a complete understanding of the native interstitial cell population is required. Previously, autopsy samples have been deemed unsuitable for immunocytochemical studies due to the ischemic time before harvesting. In this study, the viability and phenotypic profile of cells explanted from normal porcine heart valves up to 120 h post mortem was examined. METHODS: The aortic valve leaflets of porcine hearts were excised at 0, 6, 24, 48, 72, 96 and 120 h post mortem; one half of the tissue was used to explant cells, and the other half was fixed in 3.7% formalin for sectioning. Samples taken at each time point were cultured and cell viability was determined using a trypan blue exclusion assay. Immunocytochemical and immunohistochemical analyses, using specific markers for fibroblasts, myofibroblasts and smooth muscle cells, were used to compare cell phenotypes both in vitro and in situ. RESULTS: Absolute numbers of cells obtained from each leaflet decreased significantly over time; however, cell viability in culture was unaffected up to 96 h. At each time point, explanted cell populations expressed similar phenotypes when compared with histological samples prepared from the same valves. CONCLUSION: Porcine aortic valve interstitial cells may be explanted up to and including 96 h post mortem, with no statistically significant change in cell viability in vitro, and with a population that phenotypically resembles aortic valve interstitial cells in situ. These data suggest that human aortic valve interstitial cells may be successfully harvested at autopsy for in vitro studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".