Fluid pressure driven fibril reinforcement in creep and relaxation tests of articular cartilage
Bibliographic record
Abstract
Biological tissues exhibit diverse mechanical behaviors because of complex material properties. As has been shown for ligaments and intervertebral discs, mathematical models often appear to well predict load responses individually by adjusting model parameters, but likely fail to describe several different load responses simultaneously using the same model parameters. In the present study, we attempted to describe and explain both creep and relaxation responses of articular cartilage using a fibril-reinforced model, which has been successfully used to account for the load response of the relaxation tests of articular cartilage. Experiments were performed on bovine articular cartilage disks (n=8) using multi-step loading protocols, involving both creep and relaxation in each protocol. The experimental results indicated that mechanical changes, such as fiber recruitment in collagen network during stretch, recovered fully upon unloading. Creep loading did not affect relaxation properties, and vice versa. Relaxation proceeded much faster than creep, because of different fluid pressure profiles. The load sharing among the proteoglycan matrix, collagen network and fluid pressurization was predicted to differ for the creep and relaxation testing. The experimentally observed strong creep and relaxation responses in unconfined compression could not be predicted if either fibril reinforcement or fluid pressurization were neglected. It was essential to consider the interplay between nonlinear fibril reinforcement and fluid pressurization for the transient response (this interplay may be best termed as fluid pressure driven fibril reinforcement). Fibril reinforcement played a relatively insignificant role in the compressive load response at equilibrium, in agreement with previous findings for cartilage stress relaxation testing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".