Evaluation of quantitative magnetic resonance imaging, biochemical and mechanical properties of trypsin‐treated intervertebral discs under physiological compression loading
Bibliographic record
Abstract
PURPOSE: To investigate the influence of targeted trypsin digestion and 16 hours compression loading on MR parameters and the mechanical and biochemical properties of bovine disc segments. MATERIALS AND METHODS: Twenty-two 3-disc bovine coccygeal segments underwent compression loading for 16 hours after the nucleus pulposus (NP) of each disc was injected with a solution of trypsin or buffer. The properties of the NP and annulus fibrosus (AF) tissues of each disc were analyzed by quantitative MRI, biochemical tests, and confined compression tests. RESULTS: Loading had a significant effect on the MR properties (T(1), T(2), T(1rho), MTR, ADC) of both the NP and AF tissues. Loading had a greater effect on the MR parameters and biochemical composition of the NP than trypsin. In contrast, trypsin had a larger effect on the mechanical properties. Our data also indicated that localized trypsin injection predominantly affected the NP. T(1rho) was sensitive to loading and correlated with the water content of the NP and AF but not with their proteoglycan content. CONCLUSION: Our studies indicate that physiological loading is an important parameter to consider and that T(1rho) contributes new information in efforts to develop quantitative MRI as a noninvasive diagnostic tool to detect changes in early disc degeneration.
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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 | 0.001 |
| 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 | 0.001 |
| 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".