In vitro method for 3D morphometry of human articular cartilage chondrons based on micro-computed tomography
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were: to 1) develop a novel sample processing protocol to visualize human articular cartilage (AC) chondrons using micro-computed tomography (μCT), 2) develop and validate an algorithm to quantify the chondron morphology in 3D, and 3) compare the differences in chondron morphology between intact and osteoarthritic AC. METHOD: The developed protocol is based on the dehydration of samples with hexamethyldisilazane (HMDS), followed by imaging with a desktop μCT. Chondron density and depth, as well as volume and sphericity, were calculated in 3D with a custom-made and validated algorithm employing semi-automatic chondron selection and segmentation. The quantitative parameters were analyzed at three AC depth zones (zone 1: 0-10%; zone 2: 10-40%; zone 3: 40-100%) and grouped by the OARSI histological grades (OARSI grades 0-1.0, n = 6; OARSI grades 3.0-3.5, n = 6). RESULTS: After semi-automatic chondron selection and segmentation, 1510 chondrons were approved for 3D morphometric analyses. The chondrons especially in the deeper tissue (zones 2 and 3) were significantly larger (P < 0.001) and less spherical (P < 0.001), respectively, in the OARSI grade 3-3.5 group compared to the OARSI grade 0-1.0 group. No statistically significant difference in chondron density between the OARSI grade groups was observed at different depths. CONCLUSION: We have developed a novel sample processing protocol for chondron imaging in 3D, as well as a high-throughput algorithm to semi-automatically quantify chondron/chondrocyte 3D morphology in AC. Our results also suggest that 3D chondron morphology is affected by the progression of osteoarthritis (OA).
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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) | 0.001 | 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) | 0.000 | 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".