Age-dependent impact of streptozotocin on metabolic endpoints and Alzheimer's disease pathologies in 3xTg-AD mice
Bibliographic record
Abstract
Alzheimer's disease (AD) is a multifactorial neurodegenerative disease with a complex origin, thought to involve a combination of genetic, biological and environmental factors. Insulin dysfunction has emerged as a potential factor contributing to AD pathogenesis, particularly in individuals with diabetes, and among those with insulin deficiency or undergoing insulin therapy. The intraperitoneal administration of streptozotocin (STZ) is widely used in rodent models to explore the impact of insulin deficiency on AD pathology, although prior research predominantly focused on young animals, with no comparative analysis across different age groups. Our study aimed to fill this gap by analyzing the impact of insulin dysfunction in 7 and 23 months 3xTg-AD mice, that exhibit both amyloid and tau pathologies. Our objective was to elucidate the age-specific consequences of insulin deficiency on AD pathology. STZ administration led to insulin deficiency in the younger mice, resulting in an increase in cortical amyloid-β (Aβ) and tau aggregation, while tau phosphorylation was not significantly affected. Conversely, older mice displayed an unexpected resilience to the peripheral metabolic impact of STZ, while exhibiting an increase in both tau phosphorylation and aggregation without significantly affecting amyloid pathology. These changes were paralleled with alterations in signaling pathways involving tau kinases and phosphatases. Several markers of blood-brain barrier (BBB) integrity declined with age in 3xTg-AD mice, which might have facilitated a direct neurotoxic effect of STZ in older mice. Overall, our research confirms the influence of insulin signaling dysfunction on AD pathology, but also advises careful interpretation of data related to STZ-induced effects in older animals.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".