Metabolic profile of plasma before and after induction of an isolated intra-articular bone injury in the rabbit knee: Potential to characterize the onset of osteoarthritis?
Bibliographic record
Abstract
Background: Surgical models of bone injury-induced joint damage provide relevant insights into the biological pathways involved in the response to injury and development of subsequent degenerative joint conditions. Objective: To determine metabolic changes acutely following a bone injury to the rab bit knee in order to reveal key metabolites potentially associated with the chronic phase post-injury leading to post-traumatic osteoarthritis. Methods: Nine skeletally mature rabbits underwent surgery to create a repeatable, isolated intra-articular bone injury with intra-articular bleeding, without destabilizing the knee. Plasma samples were collected pre-operatively (baseline) and at 3 weeks post-injury. The samples were analyzed using nuclear magnetic resonance spectroscopy-based metabolic profiling approach and multivariate statistical analysis. Results: Metabolic profiling found clear separation between pre-surgical and post-injury rabbits. The predictive ability of the statistical model reached 75%. The levels of twelve metabolites (adenine, choline, glutamine, glycine, pyroglutamate, ornithine, 1-methylhistidine, creatinine, acetate, glucose, taurine and glutamate) significantly changed in plasma samples collected from the rabbits 3 weeks post-injury compared to their baseline levels. Conclusions: Our study indicates that metabolomics may have important applications in the detection of early systemic changes following a localized joint injury, possibly enabling early intervention and preventing progression to more serious joint degeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".