Chemically-bound nerve growth factor for neural tissue engineering applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it