Chemically-bound nerve growth factor for neural tissue engineering applications
Bibliographic record
Abstract
In order to promote regeneration after spinal cord injury, growth factors have been applied in vivo to rescue ailing neurons and provide a path finding signal for regenerating neurites. We previously demonstrated that soluble growth factor concentration gradients can guide axons over long distances, but this model is inherently limited to in vitro applications. To translate the use of growth factor gradients to an implantible device for in vivo studies, we developed a photochemical method to bind nerve growth factor (NGF) to microporous poly(2-hydroxyethylmethacrylate) (PHEMA) gels and tested bioactivity in vitro. A cell adhesive photoreactive poly(allylamine) (PAA) was synthesized and characterized. This photoreactive PAA was applied to the surface of the PHEMA gels to provide both a cell adhesive layer and a photoreactive handle for further NGF immobilization. Using a direct ELISA technique, the amount of NGF immobilized on the surface of PHEMA after UV exposure was determined to be 5.65 +/- 0.82 ng/cm2 or 3.4% of the originally applied NGF. A cell-based assay was performed to determine the bioactivity of the immobilized NGF. Using pheochromocytoma (PC-12) cells, 30 +/- 7% of the cell population responded to bound NGF, a response statistically similar to that of cells cultured on collagen in the presence of 40 ng/ml soluble NGF of 39 +/- 12%. These results demonstrate that PHEMA with photochemically bound NGF is bioactive. This photochemical technique may be useful to spatially control the amount of NGF bound to PHEMA using light and thus build a stable concentration gradient.
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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 | 0.001 | 0.000 |
| 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".