Non-destructive mechanical assessment for optimization of 3D bioprinted soft tissue scaffolds
Bibliographic record
Abstract
Characterizing the mechanical properties of engineered tissue constructs provides powerful insight into the function of engineered tissues for their desired application. Current methods of mechanical characterization of soft hydrogels used in tissue engineering are often destructive and ignore the effect of 3D bioprinting on the overall mechanical properties of a whole tissue construct. This work reports on using a non-destructive method of viscoelastic analysis to demonstrate the influence of bioprinting strategy on mechanical properties of hydrogel tissue scaffolds. Structure-function relationships are developed for common 3D bioprinting parameters such as printed fiber size, printed scaffold pattern, and bioink formulation. Further studies include mechanical properties analysis during degradation, real-time monitoring of crosslinking, mechanical characterization of multi-material scaffolds, and monitoring the effect of encapsulated cell growth on the mechanical strength of 3D bioprinted scaffolds. We envision this method of characterization opening a new wave of understanding and strategy in tissue engineering.
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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) | 0.001 | 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 | 0.000 | 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".