Pleiotrophin Signals Through ALK Receptor to Enhance the Growth of Neurons in the Presence of Inhibitory Chondroitin Sulfate Proteoglycans
Bibliographic record
Abstract
Chondroitin sulfate proteoglycans (CSPGs), one of the major extracellular matrix components of the glial scar that surrounds central nervous system (CNS) injuries, are known to inhibit the regeneration of neurons. This study investigated whether pleiotrophin (PTN), a growth factor upregulated during early CNS development, can overcome the inhibition mediated by CSPGs and promote the neurite outgrowth of neurons in vitro. The data showed that a CSPG matrix inhibited the outgrowth of neurites in primary cortical neuron cultures compared to a control matrix. PTN elicited a dose-dependent increase in the neurite outgrowth even in the presence of the growth inhibitory CSPG matrix, with optimal growth at 15 ng mL −1 of PTN (114.8% of neuronal outgrowth relative to laminin control). The growth-promoting effect of PTN was blocked by inhibition of the receptor anaplastic lymphoma kinase (ALK) by alectinib in a dose-dependent manner. Neurite outgrowth in the presence of this CSPG matrix was induced by activation of the protein kinase B (AKT) pathway, a key downstream mediator of ALK activation. This study identified PTN as a dose-dependent regulator of neurite outgrowth in primary cortical neurons cultured in the presence of a CSPG matrix and identified ALK activation as a key driver of PTN-induced growth.
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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".