Photoimmobilization of biomolecules within a 3-dimensional hydrogel matrix
Bibliographic record
Abstract
It has been recognized that a three-dimensional cell invasive scaffold that provides both topographical and chemical cues is desirable in regenerative tissue engineering to encourage cell attachment, migration, regrowth and ultimately tissue repair. Carbohydrate hydrogels are attractive for such applications because they are generally biocompatible and able to match the mechanical properties of most soft tissues. Although carbohydrate hydrogels have been previously modified with cell adhesive peptides and proteins, complicated hydrogel matrix activation was required prior to biomolecule coupling and, perhaps more importantly, the overall immobilization yield was low at approximately 1%. In this study, we report the photo-immobilization of a model biomolecule, ovalbumin (OVA), to agarose gel. We describe two methods of modification where the photoactive moiety is coupled to either the protein (i.e. OVA) or the matrix (i.e. agarose) prior to immobilization. We found that the photo-immobilization yield depends on the location of the photoactive moiety. Using photoactive OVA, 1.8% of the OVA initially incorporated into the agarose gel is immobilized; using photoactive agarose, 9.3% of the OVA initially mixed with the agarose is immobilized. The latter is a significant improvement over previous yields and may be useful in attaining our goal of immobilizing a biomolecule gradient for guided tissue regeneration.
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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) | 0.000 | 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".