Glycation and Serum Albumin Infiltration Contribute to the Structural Degeneration of Bioprosthetic Heart Valves
Bibliographic record
Abstract
Abstract Background Bioprosthetic heart valves (BHV) are widely used to treat heart valve disease but are fundamentally limited by structural valve degeneration (SVD). Non-calcific mechanisms of SVD entirely account for approximately 30% of SVD cases and contribute to calcific SVD but remain understudied. Glycation mechanisms have not been previously associated with SVD, despite being established as degenerative in collagenous native tissues. Objectives To determine whether blood component infiltration-based glycation and concomitant human serum albumin (HSA) deposition contribute mechanistically to SVD. Methods Immunohistochemistry (IHC) was used to identify advanced glycation end-products (AGEs) and serum albumin accumulation in 45 aortic valve BHV explanted due to SVD, glutaraldehyde-treated bovine pericardium (BP) incubated in vitro in glyoxal and HSA, and rat subcutaneous BP implants. Structural impacts of glycation-related mechanisms were evaluated by second harmonic generation (SHG) collagen imaging. Hydrodynamic effects of valve glycation and concomitant HSA exposure were studied with an ISO-5840-compliant pulse duplicator system using surgical grade BHV. Results All 45 clinical explants and in vitro-incubated BP demonstrated accumulated AGE and HSA compared to un-implanted, un-exposed BHV. SHG revealed instigation of collagen malalignment similar to that in SVD explants by glycation and HSA infiltration. Rat subdermal explants also showed AGE and serum albumin accumulation. Pulse duplication demonstrated significantly reduced orifice area and increased pressure gradient and peak fluid velocity following glyoxal and HSA incubations. Conclusions Glycation and concomitant HSA infiltration occur in clinical BHV and contribute to structural and functional degeneration of leaflet tissue, thus representing novel, interacting mechanisms of BHV SVD.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".