Design and fabrication of 3D‐plotted polymeric scaffolds in functional tissue engineering
Bibliographic record
Abstract
Abstract Regenerating the load‐bearing tissues requires 3D scaffolds that balance the temporary mechanical function with the biological requirements. In functional tissue engineering, designing scaffolds with biomimetic mechanical properties could promote tissue ingrowth since the cells are sensitive to their local mechanical environment. This work aims to design scaffolds that mimic the mechanical response of the biological tissues under physiological loading conditions. Poly( L ‐lactide) (PLLA) scaffolds with varying porosities and pore sizes were made by the 3D‐plotting technique. The scaffolds were tested under unconfined ramp compression to compare their stress profile under load with that of bovine cartilage. A comparison between the material parameters estimated for the scaffolds and for the bovine cartilage based on the biphasic theory enabled the definition of an optimum window for the porosity and pore size of these constructs. Moreover, the finite element prediction for the stress distribution inside the scaffolds, surrounded by the host cartilaginous tissue, demonstrated a negligible perturbation of the stress field at the site of implantation. The finite element modeling tools in combination with the developed methodology for optimal porosity/pore size determination can be used to improve the design of biomimetic scaffolds. POLYM. ENG. SCI., 47:608–618, 2007. © 2007 Society of Plastics Engineers.
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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 | 0.000 | 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 | 0.000 |
| 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".