Collagen integrity of the annulus fibrosus in degenerative disc disease individuals quantified with collagen hybridizing peptide
Bibliographic record
Abstract
Introduction: Degenerative disc disease (DDD) is accompanied by structural changes in the intervertebral discs (IVD). Extra-cellular matrix degradation of the annulus fibrosus (AF) has been linked with degeneration of the IVD. Collagen is a vital component of the IVD. Collagen hybridizing peptide (CHP) is an engineered protein that binds to degraded collagen, which we used to quantify collagen damage in AF. This method was used to compare AF samples obtained from donors with no DDD to AF samples from patients undergoing surgery for symptomatic DDD. Methods: Fresh AF tissue was embedded in an optimal cutting temperature compound and cryosectioned at a thickness of 8 μm. Hematoxylin and Eosin staining was performed on sections for general histomorphological assessment. Serial sections were stained with Cy3-conjugated CHP and the mean fluorescence intensity and areal fraction of Cy3-positive staining were averaged for three regions of interest (ROI) on each CHP-stained section. Results: = 0.0012) samples. No significant differences were detected between sex and the lumbar disc level subgroups of the non-DDD and DDD groups. Only tissue pathology (non-DDD versus DDD) influenced the measured parameters. No three-way interactions between tissue pathology, sex, and lumbar disc level were observed. Discussion and Conclusions: These findings suggest that AF collagen degradation is greater in DDD samples compared to non-DDD samples, as evidenced by the increased CHP staining. Strong positive correlations between the two measured parameters suggest that when collagen degradation occurs, it is detected by this technique and is widespread throughout the tissue. This study provides new insights into the structural alterations associated with collagen degradation in the AF that occur during DDD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".