Catechin treatment improves cerebrovascular flow-mediated dilation and learning abilities in atherosclerotic mice
Bibliographic record
Abstract
Severe dyslipidemia and the associated oxidative stress could accelerate the age-related decline in cerebrovascular endothelial function and cerebral blood flow (CBF), leading to neuronal loss and impaired learning abilities. We hypothesized that a chronic treatment with the polyphenol catechin would prevent endothelial dysfunction, maintain CBF responses, and protect learning abilities in atherosclerotic (ATX) mice. We treated ATX (C57Bl/6-LDLR(-/-)hApoB(+/+); 3 mo old) mice with catechin (30 mg · kg(-1) · day(-1)) for 3 mo, and C57Bl/6 [wild type (WT), 3 and 6 mo old] mice were used as controls. ACh- and flow-mediated dilations (FMD) were recorded in pressurized cerebral arteries. Basal CBF and increases in CBF induced by whisker stimulation were measured by optical coherence tomography and Doppler, respectively. Learning capacities were evaluated with the Morris water maze test. Compared with 6-mo-old WT mice, cerebral arteries from 6-mo-old ATX mice displayed a higher myogenic tone, lower responses to ACh and FMD, and were insensitive to NOS inhibition (P < 0.05), suggesting endothelial dysfunction. Basal and increases in CBF were lower in 6-mo-old ATX than WT mice (P < 0.05). A decline in the learning capabilities was also observed in ATX mice (P < 0.05). Catechin 1) reduced cerebral superoxide staining (P < 0.05) in ATX mice, 2) restored endothelial function by reducing myogenic tone, improving ACh- and FMD and restoring the sensitivity to nitric oxide synthase inhibition (P < 0.05), 3) increased the changes in CBF during stimulation but not basal CBF, and 4) prevented the decline in learning abilities (P < 0.05). In conclusion, catechin treatment of ATX mice prevents cerebrovascular dysfunctions and the associated decline in learning capacities.
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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 | 0.000 |
| 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".