Proteoglycan degradation mimics static compression by altering the natural gradients in fibrillar organisation in cartilage
Bibliographic record
Abstract
Structural and associated biomechanical gradients within biological tissues are important for tissue functionality and preventing damaging interfacial stress concentrations. Articular cartilage possesses an inhomogeneous structure throughout its thickness, driving the associated variation in the biomechanical strain profile within the tissue under physiological compressive loading. However, little is known experimentally about the nanostructural mechanical role of the collagen fibrils and how this varies with depth. Utilising a high-brilliance synchrotron X-ray source, we have measured the depth-wise nanostructural parameters of the collagen network in terms of the periodic fibrillar banding (D-period) and associated parameters. We show that there is a depth dependent variation in D-period reflecting the pre-strain and concurrent with changes in the level of intrafibrillar order. Further, prolonged static compression leads to fibrillar changes mirroring those caused by removal of extrafibrillar proteoglycans (as may occur in aging or disease). We suggest that fibrillar D-period is a sensitive indicator of localised changes to the mechanical environment at the nanoscale in soft connective tissues. STATEMENT OF SIGNIFICANCE: Collagen plays a significant role in both the structural and mechanical integrity of articular cartilage, allowing the tissue to withstand highly repetitive loading. However, the fibrillar mechanics of the collagen network in cartilage are not clear. Here we find that cartilage has a spatial gradient in the nanostructural collagen fibril pre-strain, with an increase in the fibrillar pre-strain with depth. Further, the fibrillar gradient changes similarly under compression when compared to an enzymatically degraded tissue which mimics age-related changes. Given that the fibrils potentially have a finite capacity to mechanically respond and alter their configuration, these findings are significant in understanding how collagen may alter in structure and gradient in diseased cartilage, and in informing the design of cartilage replacements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".