Vitexin regulates Epac and NLRP3 and ameliorates chronic cerebral hypoperfusion injury
Bibliographic record
Abstract
Chronic cerebral hypoperfusion (CCH), as a critical factor of chronic cerebrovascular diseases, has greatly influenced the health of patients with vascular dementia. Vitexin, a flavone C-glycoside (apigenin-8-C-β-D-glucopyranoside) that belongs to the flavone subclass of flavonoids, has been shown to possess antioxidant and anti-ischemic properties; however, the putative protective effects of vitexin on the CCH need further investigation. In the current study, the role of vitexin and its underlying mechanism were investigated with permanent bilateral common carotid artery occlusion (2VO) in rats as well as mouse hippocampal neuronal (HT22) cells with oxygen and glucose deprivation/reoxygenation (OGD/R) injury model. The results demonstrated that vitexin improved cognitive dysfunction as well as alleviated pathological neuronal damage in hematoxylin plus eosin (HE) and TUNEL results. The decreased levels of exchange protein directly activated by cAMP 1 (Epac1), Epac2, Ras-associated protein 1 (Rap1), and phospho-extracellular signal-regulated kinase (p-ERK) were reversed by vitexin in rats with CCH. Furthermore, this study indicated that vitexin alleviated CCH-induced inflammation injuries by reducing the expression of NOD-like receptor 3 (NLRP3), caspase-1, interleukin 1β (IL-1β), IL-6, and cleaved caspase-3. In vitro, vitexin increased the expression of Epac1 and Epac2, decreased the activation of the NLRP3-mediated inflammation, and improved cell viability. Taken together, our findings suggest that vitexin can reduce the degree of the progressing pathological damage in the cortex and hippocampus and inhibit further deterioration of cognitive function in rats with CCH. Epac and NLRP3 can be regulated by vitexin in vivo and in vitro, which provides enlightenment for the protection of CCH injury.
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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".