Immobilized Concentration Gradients of Neurotrophic Factors Guide Neurite Outgrowth of Primary Neurons in Macroporous Scaffolds
Bibliographic record
Abstract
Neurotrophic factors present as concentration gradients are neurotropic cues that direct axonal growth toward their targets. Multiple factors work together in vivo to ensure axons reach the proper targets, likely interacting with one another via intracellular signalling pathways. Nerve growth factor (NGF) and neurotrophin-3 (NT-3) are neurotrophins known to guide axons as well as promote axonal growth following injury to both the spinal cord and peripheral nerve. These molecules interact with neurons through different tyrosine kinase receptors. In this study, the receptors for these growth factors were shown to be co-localized on E10 chick dorsal root ganglion (DRG) cells, providing an opportunity for synergism. Well-defined concentration gradients of NGF and NT-3 were immobilized for the first time in a cell-penetrable, cell-adhesive scaffold of poly(2-hydroxyethylmethacrylate) and poly(L-lysine). An NGF concentration gradient of 310 ng/mL/mm was required to guide chick DRG neurites. A lower concentration gradient of 200 ng/mL/mm of NGF was shown to elicit guidance when an NT-3 concentration gradient of 200 ng/mL/mm was also present, indicating a synergistic response in the DRG neurons. These gradient scaffolds may be useful for guided regeneration following injury to the spinal cord or peripheral nerve and may also elucidate the mechanism for intracellular signaling of neurotrophic factors.
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.
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) | 0.000 | 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 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".