Recent Advances in Modeling Tissues Using 3D Bioprinted Nanocellulose Bioinks
Bibliographic record
Abstract
Abstract Bioprinting creates 3D tissue models by depositing cells encapsulated in biocompatible materials. These 3D printed models can better emulate physiological conditions in comparison with traditional 2D cell cultures or animal models. Such models can be produced from human cells, possessing human genetics and replicating the 3D microenvironment found in vivo. Many different types of biocompatible materials serve as bioinks, including gelatin methacryloyl (GelMA), alginate, fibrin, and gelatin. Nanocellulose has emerged as a promising addition to these materials. Nanocellulose─composed of cellulose chain bundles with lateral dimensions ranging from a few to several tens of nanometers─possesses key properties for 3D bioprinting applications. It can form biocompatible hydrogels, which have excellent physical properties, and its structure resembles collagen, making it useful for modeling tissues with high collagen content such as bone, cartilage, sink, and muscle. Here we review some of the recent advances in the use of nanocellulose in bioinks for the creation of bone, cartilage, skin, and muscle tissue specific models and identify areas for future progress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".