Influence of Glutaraldehyde Fixation on the Detection of SLA-I and II Antigens and Calcification Tendency in Porcine Cardiac Tissue
Bibliographic record
Abstract
OBJECTIVE: Immunological effects have been addressed as key factors for the long-term results of biological porcine aortic prostheses. In this study we investigated the influence of glutaraldehyde fixation on the expression of SLA (swine leucocyte antigens) and the calcification of porcine cardiac tissue. DESIGN: Deparaffinized sections obtained from porcine aortic tissue were fixed in a buffered glutaraldehyde solution for 1, 2, 3, 24 and 72 hours, respectively, and finally immunostained with monoclonal anti-SLA class I antibody 2.27-3a and anti-SLA-II antibody MSA3. Sixteen samples from fixed porcine cardiac tissue and, for comparison, 8 samples from leaflets of Toronto-SPV and Freestyle valves were implanted subcutaneously in 10 Wistar rats for 12 weeks and their calcium content was measured by atomic absorption spectrophotometry. RESULTS: SLA-I epitopes were no longer detectable using anti-SLA-I antibodies after fixation for 3 h. The SLA-II antigens remained detectable after longer fixation period. Short-time fixation resulted in marked calcification of the porcine cardiac tissue and to destruction of the SLA-I epitopes, whereas, even after longer fixation time, the epitopes of the SLA-II antigen remain unaffected. CONCLUSION: Chelate formation due to glutaraldehyde treatment provides protection against calcification. Short-time fixed porcine cardiac tissue has a tendency towards a greater degree of calcification than longer fixation periods. Based on the present results, it is pointless to set the length of fixation to switch off the immunogenicity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".