Biointerface Materials for Cellular Adhesion: Recent Progress and Future Prospects
Bibliographic record
Abstract
While many natural instances of adhesion between cells and biological macromolecules have been elucidated, understanding how to mimic these adhesion events remains to be a challenge. Discovering new biointerface materials that can provide an appropriate environment, and in some cases, also providing function similar to the body’s own extracellular matrix, would be highly beneficial to multiple existing applications in biomedical and biological engineering, and provide the necessary insight for the advancement of new technology. Such examples of current applications that would benefit include biosensors, high-throughput screening and tissue engineering. From a mechanical perspective, these biointerfaces would function as bioactuators that apply focal adhesion points onto cells, allowing them to move and migrate along a surface, making biointerfaces a very relevant application in the field of actuators. While it is evident that great strides in progress have been made in the area of synthetic biointerfaces, we must also acknowledge their current limitations as described in the literature, leading to an inability to completely function and dynamically respond like natural biointerfaces. In this review, we discuss the methods, materials and, possible applications of biointerface materials used in the current literature, and the trends for future research in this area.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".