Amyloid substance within stenotic aortic valves promotes mineralization
Bibliographic record
Abstract
AIMS: Accumulation of apolipoproteins may play an important role in the pathobiology of calcific aortic valve disease (CAVD). We aimed to explore the hypothesis that apolipoprotein-derived amyloid could play a role in the development of CAVD. METHODS AND RESULTS: In 70 explanted CAVD valves and 15 control non-calcified aortic valves, we assessed the presence of amyloid by using Congo red staining. Immunohistochemistry was performed to document the presence of apolipoprotein AI (Apo-AI). Apoptosis was documented by terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) studies performed in control and CAVD valves. Control valves were free of amyloid. Deposition of amyloid was detected in all CAVD valves, and the amount was positively correlated with plasma high-density lipoprotein and Apo-AI levels. Apo-AI within CAVD valves co-localized with intense staining of fibrillar amyloid. In turn, deposition of amyloid co-localized with apoptosis near mineralized areas. Isolation of amyloid fibrils confirmed that Apo-AI is a major component of amyloid deposits in CAVD. In vitro, CAVD-derived amyloid extracts increased apoptosis and mineralization of isolated aortic valvular interstitial cells. CONCLUSION: Apo-AI is a major component of amyloid substance present within CAVD valves. Furthermore, amyloid deposits participate in mineralization in CAVD by promoting apoptosis of valvular interstitial cells.
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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".