Dihydromyricetin improves type 2 diabetes-induced cognitive impairment via suppressing oxidative stress and enhancing brain-derived neurotrophic factor-mediated neuroprotection in mice
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) leads to cognitive impairment (CI), but there have been no effective pharmacotherapies or drugs for cognitive dysfunction in T2DM. Dihydromyricetin (DHM) is a natural flavonoid compound extracted from the leaves of Ampelopsis grossedentata and has various pharmacological effects including anti-oxidant and anti-diabetes. Thus, we investigated the effects of DHM on CI in T2DM mouse model and its possible mechanism. To induce T2DM, mice were fed with high-sugar and high-fat diet for 8 weeks, followed by a low dose streptozotocin (STZ) administration. After the successful induction of T2DM mouse model, mice were treated respectively with equal volume of saline (T2DM group), 125 mg/kg/d DHM (L-DHM group), or 250 mg/kg/d DHM (H-DHM group). After 16 weeks of DHM administration, the body weight (BW), fasting blood glucose, blood lipids, intraperitoneal glucose tolerance (IPGT), and cognitive function were determined. Then, alterations in the expressions of oxidative stress markers and brain-derived neurotrophic factor (BDNF) in the hippocampus were investigated. Our findings demonstrated that DHM could significantly ameliorate CI and reverse aberrant glucose and lipid metabolism in T2DM mice, likely through the suppression of oxidative stress and enhancement of BDNF-mediated neuroprotection. In conclusion, our results suggest that DHM is a promising candidate for the treatment of T2DM-induced cognitive dysfunction.
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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 | 0.000 |
| 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".