Traditional Invasive and Synchrotron-Based Noninvasive Assessments of Three-Dimensional-Printed Hybrid Cartilage Constructs <i>In Situ</i>
Bibliographic record
Abstract
Three-dimensional (3D)-printed constructs made of polycaprolactone and chondrocyte-impregnated alginate hydrogel (hybrid cartilage constructs) can mimic the biphasic nature of articular cartilage, thus offering promise for cartilage tissue engineering applications. Notably, the regulatory pathway for medical device development requires validation of such constructs through in vitro bench tests and in vivo preclinical examinations for premarket approval. For this, noninvasive imaging techniques are required for effective evaluation of the progress of these cartilage constructs, especially when implanted in animal models or human subjects. However, characterization of the individual components of the hybrid cartilage constructs and their associated time-dependent structural changes by currently available noninvasive techniques is challenging as these constructs contain a combination of hydrophobic and hydrophilic biomaterials with different refractive indices. In this study, we report the use of a novel synchrotron radiation inline phase contrast imaging computed tomography (SR-inline-PCI-CT) approach for noninvasive (in situ) characterization of 3D-printed hybrid cartilage constructs that has been implanted subcutaneously in mice over a 21-day period. In parallel, traditional invasive assays were used to evaluate the in vivo performance of the implanted hybrid cartilage constructs with respect to their cell viability and secretion of cartilage-specific extracellular matrix over the 21-day period postimplantation in mice. SR-inline-PCI-CT allowed striking visualization of the individual components within the 3D-printed hybrid cartilage constructs, as well as characterization of the time-dependent structural changes after implantation. In addition, the relationship between the implanted constructs and the surrounding tissues was delineated. Furthermore, traditional assays showed that cell viability within the cartilage constructs was at least 70% at all three time points, and secretion of alcian blue- and collagen type 2-positive matrices increased progressively over the 21-day period postimplantation. Overall, these results demonstrate that the 3D-printed hybrid cartilage constructs have good in vivo performance and validate their potential for regeneration of articular cartilage in vivo. In addition, SR-inline-PCI-CT has demonstrated potential for longitudinal and noninvasive monitoring of the functionality of 3D-printed hybrid cartilage constructs in a way that is translatable to other soft tissue engineering applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".