Three-dimensional bioprinting is not only about cell-laden structures
Bibliographic record
Abstract
In this review, we focused on a few obstacles that hinder three-dimensional (3D) bioprinting process in tissue engineering. One of the obstacles is the bioinks used to deliver cells. Hydrogels are the most widely used bioink materials; however, they aremechanically weak in nature and cannot meet the requirements for supporting structures, especially when the tissues, such as cartilage, require extracellular matrix to be mechanically strong. Secondly and more importantly, tissue regeneration is not only about building all the components in a way that mimics the structures of living tissues, but also about how to make the constructs function normally in the long term. One of the key issues is sufficient nutrient and oxygen supply to the engineered living constructs. The other is to coordinate the interplays between cells, bioactive agents and extracellular matrix in a natural way. This article reviews the approaches to improve the mechanical strength of hydrogels and their suitability for 3D bioprinting; moreover, the key issues of multiple cell lines coprinting with multiple growth factors, vascularization within engineered living constructs etc. were also reviewed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".