Arterial stiffness induced by carotid calcification leads to cerebral gliosis mediated by oxidative stress
Bibliographic record
Abstract
BACKGROUND: Arterial stiffness is a risk factor for cognitive decline and dementia. However, its precise effects on the brain remain unexplored. Using a mouse model of carotid stiffness, we investigated its effect on glial activation and oxidative stress. METHODS: Arterial stiffness was induced by the application of calcium chloride to the adventitial region of the right carotid. Superoxide anion production, NADPH activity and levels, as well as glial activation were examined with immunohistochemical and biochemical approaches, 2-week postcalcification. Antioxidant treatment was done with Tempol (1 mmol/l) administered in the drinking water during 2 weeks. RESULTS: The current study revealed that arterial stiffness increases the levels of the microglial markers ionized calcium-binding adapter molecule 1 and cluster of differentiation 68 in hippocampus, and of the astrocyte marker, s100 calcium binding protein β in hippocampus and frontal cortex. The cerebral inflammatory effects of arterial stiffness were specific to the brain and not due to systemic inflammation. Treatment with Tempol prevented the increase in superoxide anion in mice with carotid stiffness and attenuated the activation of microglia and astrocytes in the hippocampus. To determine whether the increased oxidative stress derives from NADPH oxidase, superoxide anion production was assessed by incubating brain tissue in the presence of gp91ds-tat, a selective NADPH oxidase 2 inhibitor. This peptide inhibited superoxide anion production to a greater extent in the brains of mice with carotid calcification compared with controls. CONCLUSION: Carotid calcification leads to cerebral gliosis mediated by oxidative stress. Correcting arterial stiffness could offer a novel paradigm to protect the brain in populations where stiffness is prominent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".