About Chemical Strategies to Fabricate Cell‐Instructive Biointerfaces with Static and Dynamic Complexity
Bibliographic record
Abstract
Properly functioning cell-instructive biointerfaces are critical for healthy integration of biomedical devices in the body and serve as decisive tools for the advancement of our understanding of fundamental cell biological phenomena. Studies are reviewed that use covalent chemistries to fabricate cell-instructive biointerfaces. These types of biointerfaces typically result in a static presentation of predefined cell-instructive cues. Chemically defined, but dynamic cell-instructive biointerfaces introduce spatiotemporal control over cell-instructive cues and present another type of biointerface, which promises a more biomimetic way to guide cell behavior. Therefore, strategies that offer control over the lateral sorting of ligands, the availability and molecular structure of bioactive ligands, and strategies that offer the ability to induce physical, chemical and mechanical changes in situ are reviewed. Specific attention is paid to state-of-the-art studies on dynamic, cell-instructive 3D materials. Future work is expected to further deepen our understanding of molecular and cellular biological processes investigating cell-type specific responses and the translational steps toward targeted in vivo applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".