Cellular Changes during Renal Failure-Induced Inflammatory Aortic Valve Disease
Bibliographic record
Abstract
BACKGROUND: Aortic valve calcification (AVC) secondary to renal failure (RF) is an inflammation-regulated process, but its pathogenesis remains unknown. We sought to assess the cellular processes that are involved in the early phases of aortic valve disease using a unique animal model of RF-associated AVC. METHODS: Aortic valves were obtained from rats that were fed a uremia-inducing diet exclusively for 2, 3, 4, 5, and 6 weeks as well as from controls. Pathological examination of the valves included histological characterization, von Kossa staining, and antigen expression analyses. RESULTS: After 2 weeks, we noted a significant increase in urea and creatinine levels, reflecting RF. RF parameters exacerbated until the Week 5 and plateaued. Whereas no histological changes or calcification was observed in the valves of any study group, macrophage accumulation became apparent as early as 2 weeks after the diet was started and rose after 3 weeks. By western blot, osteoblast markers were expressed after 2 weeks on the diet and decreased after 6 weeks. Collagen 3 was up-regulated after 3 weeks, plateauing at 4 weeks, whereas collagen 1 levels peaked at 2 and 4 weeks. Fibronectin levels increased gradually until Week 5 and decreased at 6 weeks. We observed early activation of the ERK pathway, whereas other pathways remained unchanged. CONCLUSIONS: We concluded that RF induces dramatic changes at the cellular level, including macrophage accumulation, activation of cell signaling pathway and extracellular matrix modification. These changes precede valve calcification and may increase propensity for calcification, and have to be investigated further.
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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".