Comparison of aortic valve allograft decellularization techniques in the rat
Bibliographic record
Abstract
Rodent models have been essential to understanding the immune-mediated failure of aortic valve allografts (AVAs). Decellularization has been proposed to reduce the immunogenicity of AVAs. The objective of this study was to determine the most effective method to decellularize AVAs for use in a rat model. Three different decellularization techniques were compared in Lewis aortic valves. Detergent decellularization involved a series of hypotonic and hypertonic Tris buffers at 4 degrees C for 48 h/buffer containing 0.5% Triton X-100 followed by a 72 h washout in phosphate-buffered saline. Osmotic decellularization was performed in similar manner to the detergent-based technique except without the addition of Triton X-100. Enzymatic decellularization consisted of trypsin/EDTA at 37 degrees C for 48 h. Assessment was performed with light microscopy (H&E, Movat's pentachrome), immunohistochemistry for residual cellular elements, and hydroxyproline assays. Detergent-based methodology effected near-complete decellularization of both the leaflets and aortic wall in addition to preservation of the extracellular matrix (ECM). Osmotic lysis was associated with preservation of ECM and moderate decellularization. Enzymatic decellularization resulted in complete decellularization but extensive degeneration and fragmentation of the ECM. When implanted into the infrarenal aorta of allogeneic rats for 1 week, valves decellularized with detergent-based and osmotic methodology failed to stimulate an allogeneic immune response as evidenced by an absence of T cell infiltrates. Osmotic lysis protocols with low dose detergent appear to be most effective at both removing antigenic cellular elements and preserving ECM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".