Contrast-enhanced x-ray microscopy of articular cartilage
Bibliographic record
Abstract
Osteoarthritis is a common chronic disease of joints characterized by degenerative changes of articular cartilage. An early diagnosis of osteoarthritis may be possible when imaging excised tissue for research in situ at the cellular-molecular scale. Whereas cartilage histopathology is destructive, time-consuming, and limited to 2D views, contrast-enhanced x-ray microscopy (XRM) can image articular cartilage and subchondral bone in 3D. This study evaluates articular cartilage structure ex vivo using both techniques.Osteochondral plugs were excised from non-diseased bovine knees and stained in phosphotungstic acid for 0 to 32 h. XRM imaging revealed an optimal staining time of 16 h and a saturated staining time of 24 h. Histology sections were cut and analyzed by polarized light microscopy (PLM) and second-harmonic-generation dual-photon (SHG-DP) microscopy. Histology photomicrographs were aligned with matching XRM slices and evaluated for features relevant in histopathological scoring of osteoarthritis cartilage, including the tidemark, collagen architecture and chondrocyte morphology.The cartilage collagen network and chondrocytes from the 3D contrast-enhanced XRM were correlated with the 2D histology. This technique has two distinct advantages over routine histopathology: (1) the avoidance of dehydration, demineralization, and deformation of histological sectioning, thereby preserving the intact articular cartilage and subchondral bone plate ex vivo, and (2) the ability to evaluate the entire osteochondral volume in 3D. This work explores several diagnostic features of imaging cartilage, including: visualization of the tidemark in XRM and SHG-DP microscopy, validating the morphology of chondrocytes and nuclei with XRM, SHG-DP and PLM, and correlating collagen birefringence with XRM image intensity.
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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 | 0.001 |
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".