Bibliographic record
Abstract
A fundamental understanding of the nanoscale details of the environment is essential for designing biomaterials that mimic the natural cellular milieu. Many features of the local environment have profound influences on cell adhesion, proliferation, maturation, and differentiation. As such, small differences in nanostructure can have macroscale impacts on tissue function. In this review, we highlight the importance of nanostructure of cellulose-based biomaterials to allow cellular adhesion, the contribution of nanostructure to macroscale mechanical properties, and several key applications of these materials for fundamental scientific research and biomedical engineering. Cellulose is a diverse material with tunable properties and can be applied to systems with vastly different biochemical and biophysical environments. Cellulose-based biomaterials offer some important advantages over conventional synthetic materials and show great promise to advance the frontier of scientific knowledge. Here we provide an up-to-date summary of the status of the field of cellulose-based biomaterials in the context of bottom-up approaches for tissue engineering. We anticipate that cellulose-based material research will continue to expand because of the diversity and versatility of biochemical and biophysical characteristics highlighted in this review.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".