Bio-Rapid-Prototyping of Tissue Engineering Scaffolds and the Process-Induced Cell Damage
Bibliographic record
Abstract
Tissue scaffolds play a vital role in tissue engineering by providing a native tissue-mimicking environment for cell proliferation and differentiation as well as tissue regeneration. Fabrication of tissue scaffolds has been drawing increasing research attention and a number of fabrication techniques have been developed. To better mimic the microenvironment of native tissues, novel techniques have emerged in recent years to encapsulate cells into the engineered scaffolds during the scaffold fabrication process. Among them, bio-Rapid-Prototyping (bioRP) techniques, by which scaffolds with encapsulated cells can be fabricated with controlled internal microstructure and external shape, shows significant promise. It is noted in the bioRP processes, cells may be continuously subjected to environmental stresses such as mechanical, electrical forces and laser exposure. If the stress is greater than a certain level, the cell membrane may be ruptured, leading to the so-called process-induced cell damage. This paper reviews various cell encapsulation techniques for tissue scaffold fabrication, with emphasis on the bioRP technologies and their technical features. To understand the process-induced cell damage in the bioRP processes, this paper also surveys the cell damage mechanisms under different stresses. The process-induced cell damage models are also examined to provide a cue to the cell viability preservation in the fabrication process. Discussions on further improvements of bioRP technologies are given and ongoing research into mechanical cell damage mechanism are also suggested in this review.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".