Integrin activation and neurotrophin signaling cooperate to enhance neurite outgrowth in sensory neurons
Bibliographic record
Abstract
Neurite growth is influenced by many factors, including the availability of trophic support as well as the extracellular environment. In this study, we have investigated whether attachment to a permissive culture substrate such as laminin is sufficient to promote neurite outgrowth from dorsal root ganglion neurons in the absence of added nerve growth factor (NGF) and whether this attachment can enhance the response of these neurons to NGF. Adult dorsal root ganglia neurons plated on surfaces coated with a thin film of laminin exhibited increased neurite outgrowth. This effect was integrin-dependent as it was attenuated by treatment with RGD (arginine-glycine-aspartate) peptides and by a beta1-integrin blocking antibody. The addition of NGF resulted in a significant increase in the integrin-dependent outgrowth. We have correlated this increase in growth with increased expression of integrin subunits and activation of known downstream signaling intermediates such as focal adhesion kinase, Src, and Akt. We have also examined pathway cooperation through the use of an Src-specific inhibitor, PP2, and a beta1-integrin blocking antibody, beta1i, by observing downstream signaling intermediates in both integrin and growth factor signaling pathways. These results are among the first to detail the importance of interactions between neurotrophin- and integrin-activated signaling in adult primary neurons.
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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".