An in vitro study of the effects of pathophysiologically relevant levels of oxidative damage on cortical bone tissue quality
Bibliographic record
Abstract
This study examined the impact of in vitro oxidative damage on cortical bone tissue quality assessed using mechanical testing, biochemical assays, and thermo-analytical methods. The primary hypothesis of this study was that oxidative damage, caused in vivo by oxidative stress-a key factor in aging and various inflammatory diseases-deteriorates bone tissue quality by damaging bone collagen, the main component of the bone's organic phase. To test this hypothesis in vitro, bovine cortical bone specimens were exposed to neutral hypochlorous acid solution (a potent reactive oxygen species) to induce oxidative damage, which was measured in terms of carbonylation. Carbonylation is a stable biomarker of protein oxidation. Mechanical testing revealed degradation of cortical bone mechanical properties due to oxidation. There was a marked degradation of mechanical properties, specifically pre-yield and post-yield properties such as tensile (30-40 %) and compressive (19-23 %) yield strength, as well as tensile (25-45 %) and compressive (11-16 %) ultimate strength. Exposure to neutral hypochlorous acid solutions increased the carbonyl content (normalized to collagen content), confirming protein oxidative damage. In addition, the observed carbonyl levels in the oxidized groups were similar to those measured in human cortical bone specimens. Thermo-analytical tests including differential scanning calorimetry (DSC) and hydrothermal isothermal tension (HIT) tests, providing measures of collagen nativity and connectivity, revealed negative correlations with carbonyl content (r = -0.46, p = 0.0038). The hypothesis that physiological levels of oxidative damage generated in vitro degrade cortical bone mechanical properties by damaging the bone collagen has been confirmed. This suggests that oxidative damage of bone collagen is important towards understanding the degradation of bone quality in aging and various diseases.
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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 | 0.001 | 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 | 0.001 |
| 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".