Quantitative Magnetic Resonance Imaging of Enzymatically Induced Degradation of the Nucleus Pulposus of Intervertebral Discs
Bibliographic record
Abstract
STUDY DESIGN: The structural integrity of the nucleus pulposus (NP) of intervertebral discs was targeted by enzyme-specific degradations to correlate their effects to the magnetic resonance (MR) signal. OBJECTIVE: To develop quantitative MR imaging as an accurate and noninvasive diagnostic tool to better understand and treat disc degeneration. SUMMARY OF BACKGROUND DATA: Quantitative MR analysis has been previously shown to reflect not only the disc matrix composition, but also the structural integrity of the disc matrix. Further work is required to identify the contribution of the structural integrity versus the matrix composition to the MR signal. METHODS: The bovine coccygeal NPs were injected with either enzyme or buffer, incubated at 37 degrees C as static, unloaded and closed 3-disc segments, and analyzed by a 1.5-Tesla MR scanner to measure MR parameters. RESULTS: Collagenase degradation of the NP significantly decreased the relaxation times, slightly decreased the magnetization transfer ratio, and slightly increased the apparent diffusion coefficient. Targeting the proteoglycan and/or hyaluronan integrity by trypsin and hyaluronidase did not significantly affect the MR parameters, except for an increase in the apparent diffusion coefficient of the disc after trypsin treatment. CONCLUSIONS: Our results demonstrate that changes in the structural integrity of matrix proteins can be assessed by quantitative MR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".