Techniques for isolating and purifying porcine aortic valve endothelial cells.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Existing methods to isolate aortic valve endothelial cells (ECs) are unreliable and often yield populations that are inadequate for long-term studies in vitro because of valve interstitial cell (IC) contamination. The study aim was to test various isolation protocols to improve the yield and purity of isolated ECs, and to assess two purification techniques to further deplete contaminating ICs and improve the quality of long-term EC cultures. METHODS: Porcine aortic valve leaflets were digested in different concentrations of collagenase and dispase over various incubation times. Isolated cells were counted, and the purities of the populations determined by immunocytochemical staining and image analysis. Improvements in purity after magnetic cell sorting (MACS) or clonal expansion were assessed. RESULTS: Enzymatic digestion using 60 U/ml collagenase yielded the largest number of cells. Digestion with 2.0 U/ml dispase and 60 U/ml collagenase produced significantly more pure populations of ECs than solutions containing 0.5 or 1.0 U/ml dispase (p <0.05). A 2-h digestion produced similar yields compared to longer digestion times. MACS improved purity (p <0.01) and was efficient and economical, but purified populations were contaminated with ICs post-confluence. Clonal expansion produced the highest quality EC cultures, with no IC contamination after weeks of post-confluent culturing. CONCLUSION: The results of these studies have provided recommendations for the improved isolation of aortic valve ECs, and guidelines for the further purification of isolated EC populations based on quality, time and economical considerations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".