Neurite outgrowth is enhanced by laminin‐mediated down‐regulation of the low affinity neurotrophin receptor, p75NTR
Bibliographic record
Abstract
Laminin (LN), an extracellular matrix component, is a key factor in promoting axonal regeneration, coordinately regulating growth in conjunction with trophic signals provided by the neurotrophins, including nerve growth factor (NGF). This study investigated potential interactions between the LN and NGF-mediated signaling pathways in PC12 cells and primary neurons. Neurite outgrowth stimulated by NGF was enhanced on a LN substrate. Western blot analysis of pertinent signal transduction components revealed both enhanced phosphorylation of early signaling intermediates upon co-stimulation, and a LN-induced down-regulation of p75NTR which could be prevented by the addition of integrin inhibitory arginine-glycine-aspartate (RGD) peptides. This p75NTR down-regulation was associated with a LN-mediated up-regulation of PTEN and resulted in a decrease in Rho activity. Studies using over-expression or siRNA-mediated knock-down of PTEN demonstrate a consistent inverse relationship with p75NTR, and the over-expression of p75NTR impaired neurite outgrowth on a LN substrate, as well as resulting in sustained activation of Rho which is inhibitory to neurite outgrowth. p75NTR is documented for its role in the transduction of inhibitory myelin-derived signals, and our results point to extracellular matrix regulation of p75NTR as a potential mechanism to ameliorate inhibitory signaling leading to optimized neurite outgrowth.
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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".