Carotid Endarterectomy Ameliorates Cognitive Impairment in Clinical and Experimental Unilateral Carotid Artery Stenosis
Bibliographic record
Abstract
BACKGROUND: Carotid endarterectomy (CEA) is widely used to treat carotid artery stenosis (CAS). However, the effects of CEA on unilateral CAS-induced cognitive impairment and the underlying mechanism remain poorly understood. METHODS AND RESULTS: fluoro-2-deoxy-d-glucose positron emission tomography/magnetic resonance imaging, cognitive assessments, and routine blood tests before and after CEA. Unilateral carotid common artery occlusion and ligation release (reperfusion) surgeries were performed in mice to mimic CAS and CEA. Cognitive function, cerebral blood flow, and white matter damage were evaluated in mice using the Morris water maze test, Doppler flowmetry, laser-speckle imaging, diffusion tensor imaging, Luxol fast blue staining, transmission electron microscopy, and western blot assays post unilateral carotid common artery occlusion and reperfusion. Genomic sequencing of the white matter was performed to explore the potential underlying mechanism. CEA significantly enhanced the Montreal Cognitive Assessment scores in patients with CAS and preoperative cognitive impairment. Moreover, CEA led to notable improvements in cerebral blood flow, energy metabolism, and white matter integrity, while concurrently reducing blood inflammation. In the mouse model, reperfusion surgery alleviated cognitive deficits, increased cerebral blood flow, and alleviated white matter damage following unilateral carotid common artery occlusion. Furthermore, transcriptional surveys have revealed substantial alterations in the upregulation of Nrf2 signaling and metabolic pathways, coupled with the inhibition of neuroinflammation, cellular communication, and immune cell population signaling following reperfusion. CONCLUSIONS: CEA ameliorated CAS-induced cognitive dysfunction by improving the cerebral functional structure. These beneficial effects may be attributed to their antioxidant and anti-inflammatory properties.
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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 | 0.001 |
| 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".