Using synchrotron radiation inline phase-contrast imaging computed tomography to visualize three-dimensional printed hybrid constructs for cartilage tissue engineering
Bibliographic record
Abstract
Synchrotron radiation inline phase-contrast imaging combined with computed tomography (SR-inline-PCI-CT) offers great potential for non-invasive characterization and three-dimensional visualization of fine features in weakly absorbing materials and tissues. For cartilage tissue engineering, the biomaterials and any associated cartilage extracellular matrix (ECM) that is secreted over time are difficult to image using conventional absorption-based imaging techniques. For example, three-dimensional printed polycaprolactone (PCL)/alginate/cell hybrid constructs have low, but different, refractive indices and thicknesses. This paper presents a study on the optimization and utilization of inline-PCI-CT for visualizing the components of three-dimensional printed PCL/alginate/cell hybrid constructs for cartilage tissue engineering. First, histological analysis using Alcian blue staining and immunofluorescent staining assessed the secretion of sulfated glycosaminoglycan (GAGs) and collagen type II (Col2) in the cell-laden hybrid constructs over time. Second, optimization of inline PCI-CT was performed by investigating three sample-to-detector distances (SDD): 0.25, 1 and 3 m. Then, the optimal SDD was utilized to visualize structural changes in the constructs over a 42-day culture period. The results showed that there was progressive secretion of cartilage-specific ECM by ATDC5 cells in the hybrid constructs over time. An SDD of 3 m provided edge-enhancement fringes that enabled simultaneous visualization of all components of hybrid constructs in aqueous solution. Structural changes that might reflect formation of ECM also were evident in SR-inline-PCI-CT images. Summarily, SR-inline-PCI-CT images captured at the optimized SDD enables visualization of the different components in hybrid cartilage constructs over a 42-day culture period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".