Designer Scaffolds for Interfacial Bioengineering
Bibliographic record
Abstract
In regenerative medicine, the healing of the interfacial zone between tissues is a major challenge, yet approaches for studying the complex microenvironment of this interface remain lacking. Herein, these complex living interfaces by manufacturing modular “blocks” of naturally porous decellularized plant‐derived scaffolds with a computer numerical controlled mill are studied. How each scaffold can be seeded with different cell types and easily assembled in a manner akin to LEGO bricks to create an engineered tissue interface (ETI) is demonstrated. Cells migrate across the interface formed between an empty scaffold and a scaffold preseeded with cells. However, when both scaffolds contain cells, only a shallow cross‐over zone of cell infiltration forms at the interface. As a proof‐of‐concept study, ETIs to investigate the interaction between lab grown bone and connective tissues are used. Consistent with the above, a cross‐over zone of the two distinct cell types forms at the interface between scaffolds, otherwise the populations remain distinct. Finally, how ETIs are biocompatible in vivo are demonstrated, becoming vascularized and integrated into surrounding tissue after implantation. Herein, new tissue design avenues for understanding biological processes or the development of synthetic artificial tissues are created.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.002 |
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".