Characterization of insulin signaling in a Caenorhabditis elegans Alzheimer’s disease model
Bibliographic record
Abstract
Insulin is a key hormone in the regulation of blood glucose levels in many animals. Dysfunctional insulin signaling is one of the leading mechanisms of interest for the pathogenesis of diabetes and Alzheimer’s disease (AD). Despite the claim of a link between diabetes and AD, the results in the literature have been incongruent. Some studies have claimed that insulin resistance is a major implication of AD, while others have reported that the risk is increased by excess insulin in the blood. Using the microscopic worm, Caenorhabditis elegans, we have developed a model to study the link between insulin signaling and AD pathologies, including changes in neuronal morphology and abnormal behaviour. Furthermore, we have examined the effects of hyperactive insulin signaling on the expression of the two characteristic proteins of AD, amyloid-beta and tau. By genetically modifying the key players of the insulin-signaling pathway and the 40 insulin-like peptides in C. elegans, we may be able to identify novel targets for the prevention or treatment of AD. In this research, we tested whether the 40 insulin-like peptides are inhibitors or activators of the insulin-signaling pathway, based on the aforementioned phenotypes associated with AD. Alternatively, some of these 40 insulin-like peptides may work through the non-canonical insulin-signaling pathway, with the potential of pointing us to novel genetic interactors. Our work has shown that of the 40 insulin-like peptides, some exacerbated AD-related pathologies, while others reduced them. Understanding the function of the components of the insulin-signaling pathway and the 40 insulin-like peptides in C. elegans may clarify the link between defective insulin signaling and AD and present novel treatment strategies for the disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".