New Evidence Implicating 4‐Hydroxynonenal in the Pathogenesis of Osteoarthritis In Vivo
Bibliographic record
Abstract
OBJECTIVE: To demonstrate the involvement of 4-hydroxynonenal (HNE), a very reactive aldehyde derived from lipid peroxidation, in the pathogenesis of osteoarthritis (OA) in vivo. METHODS: In the first experimental protocol, OA was induced by anterior cruciate ligament transection (ACLT) of the right knees of crossbred dogs (n = 6 per group). The animals were treated with placebo or HNE-trapping carnosine (5 or 20 mg/kg/day) orally for 8 weeks. Another group of dogs was treated for 4 weeks with 20 mg/kg/day of carnosine starting 4 weeks after surgery. Sham-operated dogs served as controls. In the second experimental protocol, a pathophysiologic dose of HNE (80 nmoles/ml) or vehicle was injected weekly into the right knee joints of crossbred dogs (n = 6 per group) for 8 weeks. Articular cartilage was subjected to macroscopic, histomorphologic, and immunohistochemical analyses. Cartilage-degrading enzymes and oxidative stress-related products were assessed in synovial fluid and cartilage explants. Markers of inflammation were evaluated in synovium and synovial fluid. RESULTS: In dogs that had undergone ACLT, carnosine treatment reduced the severity and histopathology score of OA cartilage lesions and also decreased HNE-protein adducts, pentosidine, nitrosylated proteins, cartilage-degrading enzymes, and markers of inflammation. Intraarticular injection of HNE induced cartilage lesions, as assessed by macroscopic and microscopic criteria. Cartilage-degrading enzymes and markers of inflammation increased in HNE-treated dogs. CONCLUSION: This is the first in vivo study to demonstrate the pathophysiologic role of HNE in OA. That carnosine abolishes HNE production and a number of factors known to be involved in OA pathogenesis renders it a clinically valuable agent in prevention of the disease.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".