Abstract P136: What is the Impact of Arterial Stiffness on Brain's Health
Bibliographic record
Abstract
Arterial stiffness is an important risk factor for cognitive decline. However, its specific effects on brain homeostasis are unknown. Hence, the objective of the study is to explore the effects of arterial stiffness on brain's health, especially on oxidative stress, inflammation, cerebrovascular regulation and cognitive functions. Approach and Results: Arterial stiffness was induced by applying calcium chloride to carotid arteries of C57BL6 male mice. The control group received sodium chloride. Cerebral inflammation was assessed by quantifying immunoreactivity to activated glia markers ; Iba-1, CD68 and s100β. Oxidative stress was determined with dihydroethidium. Cerebral blood flow (CBF) was monitored by laser-Doppler flowmetry in anesthetized mice equipped with a cranial window and spatial memory was tested using the Morris water maze. Results show that arterial stiffness activates microglia in the hippocampus, and astrocytes in the hippocampus and the frontal cortex. Superoxide anion production was elevated in the hippocampus of these mice. Arterial stiffness attenuated the CBF increase produced by stimulation of the vibrissae or by topical application of the endothelium-dependent vasodilator acetylcholine. The results indicate learning and spatial memory deficit induced by arterial stiffness in comparison to the sodium chloride group. Conclusions: This study shows that arterial stiffness, induced by carotid calcification, leads to cerebral inflammation and increased oxidative stress mainly in the hippocampus. Arterial stiffness also alters CBF regulation and cognitive functions. This suggests that arterial stiffness has an impact on cerebral homeostasis and should be considered as a therapeutical target for the prevention of cerebral dysfunctions in the aging population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".