Defining a neurotrophic factor concentration gradient to guide neurite outgrowth
Bibliographic record
Abstract
Well-defined neurotrophic factor concentration gradients were prepared using a compartmented diffusion chamber. This model system was employed to study the chemotactic effect of neurotrophic factors to guide neurite outgrowth. The information obtained from this model system may provide insights into the designs of a biomimetic device to enhance nerve regeneration after spinal cord injuries. Specifically, by maintaining the concentrations in both the source and sink compartments constant (and different), a linear NGF concentration profile was achieved within the agarose membrane through which a steady state diffusion was established. The well-defined linear NGF concentration profile enabled the quantification of the minimum concentration gradient required to guide neurite outgrowth using PC12 cells as a model cell line for neurons. Furthermore, multiple concentration gradients of different neurotrophic factors were prepared using similar compartmented diffusion chambers. The synergistic guidance effect of multiple factors observed in this model system suggests that peripheral dorsal root ganglion cell neurite outgrowth can be guided over an extended distance. This result could warrant the incorporation of neurotrophic factor concentration gradient into a nerve regeneration device to guide and augment nerve fiber regeneration. Finally, in an attempt to translate the neurotrophic concentration gradient guidance into an artificial device that may enhance CNS regeneration, efforts were also made immobilize biomolecules in a three-dimensional agarose hydrogel using photoimmobilization. The improved photoimmobilization yield using the photoactive agarose gel approach, as a result of the enhanced interactions between the photoactive moieties and the agarose gel, represented a viable approach to immobilize biomolecules in 3D hydrogel matrices. This approach could also be further advanced to photoimmobilize biomolecule concentration gradients in situ.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".