Resistance to trophic neurite outgrowth of sensory neurons exposed to insulin
Bibliographic record
Abstract
Insulin offers trophic support through receptors expressed widely on peripheral neurons. In this work, we studied whether peripheral sensory neurons demonstrate resistance to its trophic properties, a property relevant during type 2 diabetes mellitus or following supraphysiological therapy. Insulin receptors were not only localized to neuronal membranes and cytoplasm but also had a unique, previously unrecognized localization to neuronal nuclei. We confirmed that nanomolar doses increased neurite outgrowth of adult sensory neurons, but in response to micromolar doses of insulin, even following a brief 2-h exposure, survival and outgrowth of neurites were blunted. Neurons exposed to picomolar insulin concentrations in their media for 5 days had resistance to the impact of later nanomolar doses of insulin. Using a stripe assay seeded with insulin, neurites were more likely to reject higher doses of insulin. Insulin down-regulated mRNAs of the insulin receptor β subunit and up-regulated levels of GSK-3β, both potential mechanisms of insulin resistance, while down-regulating the protein expression of pAkt and pGSK-3β. Overall, these studies identify neuronal nuclear targeting of insulin and evidence for insulin-induced resistance to its trophic properties. The findings have implications for the understanding of the actions of insulin in the treatment of diabetes and neurological disorders.
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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".