Dynamic cell patterning of photoresponsive hyaluronic acid hydrogels
Bibliographic record
Abstract
Techniques to pattern cells on biocompatible hydrogels allow for the creation of highly controlled cell microenvironments within materials that mimic the physicochemical properties of native tissues. Such technology has the potential to further enhance our knowledge of cell biology and to play a role in the development of novel tissue engineering devices. Light is an ideal stimulus to catalyze pattern formation since it can be controlled spatially as well as temporally. Herein, we have developed and enhanced a hydrogel cell patterning strategy. It is based on photoactive caged RGDS peptides incorporated into a hyaluronic acid (HA) hydrogel, which can be subsequently activated with near-UV light to create cell-adhesive regions within an otherwise non-adhesive hydrogel. With this strategy, we have been able to pattern multiple cell populations-either in contact with one another or held apart-on an underlying chemically patterned HA hydrogel. Furthermore, the hydrogel cell pattern could be altered with time, even 2 weeks after initial seeding, to create additional adhesive regions to regulate the direction of cell growth and migration. These dynamic hydrogel cell patterns, created with a standard fluorescence microscope, were shown to be robust and lasted at least 3 weeks in vitro.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".