Modulating physical, chemical, and biological properties in 3D printing for tissue engineering applications
Bibliographic record
Abstract
Over the years, 3D printing technologies have transformed the field of tissue engineering and regenerative medicine by providing a tool that enables unprecedented flexibility, speed, control, and precision over conventional manufacturing methods. As a result, there has been a growing body of research focused on the development of complex biomimetic tissues and organs produced via 3D printing to serve in various applications ranging from models for drug development to translational research and biological studies. With the eventual goal to produce functional tissues, an important feature in 3D printing is the ability to tune and modulate the microenvironment to better mimic in vivo conditions to improve tissue maturation and performance. This paper reviews various strategies and techniques employed in 3D printing from the perspective of achieving control over physical, chemical, and biological properties to provide a conducive microenvironment for the development of physiologically relevant tissues. We will also highlight the current limitations associated with attaining each of these properties in addition to introducing challenges that need to be addressed for advancing future 3D printing approaches.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".