Immobilized concentration gradients of nerve growth factor guide neurite outgrowth
Bibliographic record
Abstract
Axons are guided to their targets by a combination of haptotactic and chemotactic cues. We previously demonstrated that soluble neurotrophic factor concentration gradients guide axons in a model system. In an attempt to translate this model system to a device for implantation, our goal was to immobilize a stable neurotrophic concentration gradient for axonal (or neurite) guidance. Nerve growth factor (NGF) was immobilized within poly(2-hydroxyethylmethacrylate) [p(HEMA)] microporous gels using a gradient maker. The NGF was stably immobilized, with only approximately 0.05% of the amount originally incorporated into the gel released over an 8-day period. Immobilized NGF was bioactive: the percent of PC12 cells extending neurites on NGF-immobilized p(HEMA) gels was 16 +/- 2%, which was statistically the same as those exposed to soluble NGF (22 +/- 6%). We were able to predict and reproducibly create stable NGF concentration gradients in the gel. At an NGF concentration gradient of 357 ng/mL/mm, PC12 cell neurites were guided up the gradient. The facile, flexible, and reproducible nature of this method allowed us to translate soluble growth factor gradient models to stable growth factor gradient devices that may ultimately enhance axonal guidance and regeneration in vivo.
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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".