Pioglitazone and exenatide enhance cognition and downregulate hippocampal beta amyloid oligomer and microglia expression in insulin-resistant rats
Bibliographic record
Abstract
Insulin resistance is known to be a risk factor for cognitive impairment, most likely linked to insulin signaling, microglia overactivation, and beta amyloid (Aβ) deposition in the brain. Exenatide, a long lasting glucagon-like peptide-1 (GLP-1) analogue, enhances insulin signaling and shows neuroprotective properties. Pioglitazone, a peroxisome proliferated-activated receptor-γ (PPAR-γ) agonist, was previously reported to enhance cognition through its effect on Aβ accumulation and clearance. In the present study, insulin resistance was induced in male rats by drinking fructose for 12 weeks. The effect of monotherapy with pioglitazone (10 mg·kg(-1)) and exenatide or their combination on memory dysfunction was determined and some of the probable underlying mechanisms were studied. The current results confirmed that (1) feeding male rats with fructose syrup for 12 weeks resulted in a decline of learning and memory registered in eight-arm radial maze test; (2) treatment with pioglitazone or exenatide enhanced cognition, reduced hippocampal neurodegeneration, and reduced hippocampal microglia expression and beta amyloid oligomer deposition in a manner that is equal to monotherapies. These results may give promise for the use of pioglitazone or exenatide for ameliorating the learning and memory deficits associated with insulin resistance in clinical setting.
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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) | 0.001 | 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 | 0.001 |
| 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".