Computed Tomography Diffraction-Enhanced Imaging for <i>In Situ</i> Visualization of Tissue Scaffolds Implanted in Cartilage
Bibliographic record
Abstract
Long-term in vivo studies on animal models and advances from animal to human studies should rely on noninvasive monitoring methods. Synchrotron radiation (SR)-diffraction enhanced imaging (DEI) has shown great promise as a noninvasive method for visualizing native and/or engineered tissues and bio-microstructures with appreciable details in situ. The objective of this study was to investigate SR-DEI for in situ visualization and characterization of tissue-engineered scaffolds implanted in cartilage. A piglet stifle joint implanted with an engineered scaffold made from poly-ɛ-caprolactone was imaged using SR computed tomography (CT)-DEI at an X-ray energy of 40 keV. For comparison, in situ visualization was also conducted with commonly used SR CT-phase contrast imaging and clinical magnetic resonance imaging techniques. The reconstructed CT-DE images show the implanted scaffold with the structural properties much clearer than those in the CT-PC and MR images. Furthermore, CT-DEI was able to visualize microstructures within the cartilage as well as different soft tissues surrounding the joint. These microstructural details were not recognizable using other imaging techniques. Taken together, the results of this study suggest that CT-DEI can be used for noninvasive visualization and characterization of scaffolds in cartilage, representing an advance in tissue engineering to track the success of tissue scaffolds for cartilage repair.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".