Neurite growth promotion by nerve growth factor and insulin‐like growth factor‐1 in cultured adult sensory neurons: Role of phosphoinositide 3‐kinase and mitogen activated protein kinase
Bibliographic record
Abstract
Although neurons of the PNS no longer require neurotrophins such as Nerve Growth Factor (NGF) for their survival, such factors are involved in regulating axonal sprouting and regeneration after injury. In addition to the neurotrophin receptors, sensory neurons are reported to express IGF-1, EGF and FGF receptors. To investigate the influence of growth factors in addition to NGF, we examined the effects of IGF-1 EGF and FGF on neurite growth from adult rat dorsal root ganglion sensory neurons in both dissociated cultures and in compartmented cultures. As expected, NGF elicited robust neuritic growth in both the dissociated and compartmented cultures. The growth response to IGF-1 was similar, although there was minimal neurite growth in response to EGF or FGF. In addition, IGF-1 (but neither FGF nor EGF), when applied to cell bodies in compartmented cultures, potentiated the distal neurite growth into NGF-containing side compartments. This potentiation was not seen when these factors were provided along with NGF in the side compartments of compartmented cultures, or in the dissociated cultures. To determine the contribution of signaling intermediates downstream of receptor activation, we used inhibitors of the potential effectors and Western blotting. The PI 3-kinase inhibitor, LY294002 attenuated neurite growth evoked by NGF, IGF and EGF in dissociated cultures, although the MAP kinase kinase (MEK) inhibitor PD098059 diminished the growth in only IGF. Immunoprecipitation and Western blotting results demonstrated differential activation of MAPK, PI 3-kinase, PLCgamma1 and SNT by the different factors. Activation of PI 3-kinase and SNT by both NGF and IGF-1 correlated with their effects on neurite growth. These results support the hypothesis that the PI 3-kinase pathway plays an important role in neuritogenesis.
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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 | 0.001 |
| 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".