Customizing the Shape and Microenvironment Biochemistry of Biocompatible Macroscopic Plant-Derived Cellulose Scaffolds
Bibliographic record
Abstract
Plant-derived cellulose scaffolds constitute a highly viable and interesting biomaterial. They retain a high flexibility in shape and structure, present the ability to tune surface biochemistry, display a high degree of biocompatibility, exhibit vascularization, and are widely available and easily produced. What is also immediately clear is that pre-existing cellulose structures in plants can also provide candidates for specific tissue engineering applications. Here, we report a new preparation and fabrication approach for producing large scale scaffolds with customizable macroscopic structures that support cell attachment and invasion both in vitro and in vivo. This new fabrication method significantly improves cell attachment compared to that in our previous work. Moreover, the materials remain highly biocompatible and retain vascularization properties in vivo. We present proof-of-concept studies that demonstrate how hydrogels can be temporarily or permanently cast onto the macroscopic scaffolds to create composite plant-derived cellulose biomaterials. This inverse molding approach allows us to provide temporary or permanent biochemical cues to invading cells in vitro. The development of a new-generation of rapidly and efficiently produced composite plant-derived biomaterials provides an important proof that such biomaterials have the potential for numerous applications in tissue engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".