Stimuli-responsive biomaterials: smart avenue toward 4D bioprinting
Bibliographic record
Abstract
3D bioprinting is an advanced technology combining cells and bioactive molecules within a single bioscaffold; however, this scaffold cannot change, modify or grow in response to a dynamic implemented environment. Lately, a new era of smart polymers and hydrogels has emerged, which can add another dimension, e.g., time to 3D bioprinting, to address some of the current approaches' limitations. This concept is indicated as 4D bioprinting. This approach may assist in fabricating tissue-like structures with a configuration and function that mimic the natural tissue. These scaffolds can change and reform as the tissue are transformed with the potential of specific drug or biomolecules released for various biomedical applications, such as biosensing, wound healing, soft robotics, drug delivery, and tissue engineering, though 4D bioprinting is still in its early stages and more works are required to advance it. In this review article, the critical challenge in the field of 4D bioprinting and transformations from 3D bioprinting to 4D phases is reviewed. Also, the mechanistic aspects from the chemistry and material science point of view are discussed too.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".