Laminin and growth factor receptor activation stimulates differential growth responses in subpopulations of adult DRG neurons
Bibliographic record
Abstract
Neurons in the adult rat dorsal root ganglion (DRG) can be classified into at least three separate subpopulations based on morphologic and phenotypic differences. In this study we have focused on the growth response of these specific subpopulations in vitro with respect to laminin (LN) and growth factor receptor activation. Using a cell selection approach we show that LN-induced neurite growth occurs in the absence of added trophic factors only in heavy-chain neurofilament-positive and calcitonin gene-related peptide-positive DRG neurons [nerve growth factor (NGF)-responsive population]. In contrast, LN alone is not sufficient to stimulate significant neurite growth from lectin Griffonia simplicifolia IB4-positive neurons (IB4+ve), although it is still required to elicit a growth response from these cells in the presence of glial-derived neurotrophic factor (GDNF, e.g. neurite growth occurred only when cells were plated on LN in the presence of GDNF). By using chemical inhibitors we demonstrate that only the phosphatidylinositol 3 kinase (PI 3-K)/Akt pathway is required for neurite growth from the NGF-responsive cell population. However, both the PI 3-K/Akt and MEK/mitogen-activated protein kinase signaling pathways are required for neurite growth from the IB4+ve cell population. Thus, we have identified specific signaling events and environmental requirements associated with neurite growth for different subpopulations of adult DRG neurons, pointing to potential therapeutic targets while identifying an inability for any one treatment alone to repair peripheral nerve damage.
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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".