Light-Activated Immobilization of Biomolecules to Agarose Hydrogels for Controlled Cellular Response
Bibliographic record
Abstract
We describe a new method of synthesizing photolabile hydrogel materials for convenient photoimmobilization of biomolecules on surfaces or in 3-D matrixes. Dissolved agarose was modified with photolabile S-(2-nitrobenzyl)cysteine (S-NBC) via 1,1'-carbonyldiimidazole (CDI) activation of primary hydroxyl groups. S-NBC-modified agarose remained soluble and gelable with up to 5% S-NBC substitution, yet gelation was slower and the elastic modulus of the resulting gel was lower than those of unmodified agarose. Irradiating S-NBC-grafted agarose resulted in the loss of the protecting 2-nitrobenzyl groups, thereby exposing free sulfhydryl groups for biomolecular coupling. When appropriately activated with sulfhydryl-reactive groups, either peptides or proteins were effectively immobilized to the photoirradiated hydrogel matrixes, with the irradiation energy dose (i.e., irradiation time) used to control the amount of biomolecule immobilization. When the GRGDS peptide was immobilized on agarose, it was shown to be cell-adhesive and to promote neurite outgrowth from primary, embryonic chick dorsal root ganglion neurons. The immobilized GRGDS surface ligand concentration affected the cellular response: neurite length and density increased with GRGDS surface concentration at low adhesion ligand concentration and then plateaued at higher GRGDS concentration. Grafting 2-nitrobenzyl-protected compounds to hydrogel materials is useful for creating new photolabile hydrogel substrates for light-activated functional group generation and biomolecular immobilization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".