Tissue engineering and regenerative therapeutics: The nexus of chemical engineering and translational medicine
Bibliographic record
Abstract
Abstract Since its emergence nearly 30 years ago with the goal of fabricating replacement human tissues or assisting the body in repairing itself, several advances have been made in the fields of tissue engineering and regenerative medicine. The assembly of living cells into tissues is akin to the scale‐up operation of a chemical or biochemical process with several modules or units affecting the final product. The major modules or units in tissue engineering and regenerative medicine are cells, biodegradable scaffolds, bioreactors, and biomolecules. Each module is analogous to unit operations, where individual units must come together to successfully design engineered tissues for therapeutic and drug discovery applications. Basic principles of biological sciences, chemical engineering, materials chemistry, and polymer processing are frequently employed. More specifically, knowledge about viscous fluid flow, mass transfer, and reaction engineering is essential in tissue engineering. In this review article, we present the nexus between chemical engineering and tissue engineering for the rational design of engineered tissues or injectable cell‐laden hydrogel systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".