Effects of Endovascular Stent-Assisted Angioplasty on Cellular Metabolism in the Hippocampus of Elderly Patients with Symptomatic Vertebrobasilar Artery Stenosis
Bibliographic record
Abstract
BACKGROUND Cerebral artery stenosis is closely related to cognitive function, and angioplasty can improve the cognitive function of elderly patients with vertebrobasilar artery stenosis. The specific mechanism, however, is not clear. This study explored the effect of angioplasty on cellular metabolism in the hippocampus of elderly patients with symptomatic vertebrobasilar artery stenosis. MATERIAL AND METHODS Eighteen elderly patients with symptomatic vertebrobasilar artery stenosis who underwent endovascular stent-assisted angioplasty (ESAA) in our department were studied. The changes in cellular metabolism (NAA / Cr, CHO / Cr, NAA / CHO) in bilateral hippocampal areas were detected by MRS before and at 6 months and 12 months after the ESAA. The Montreal Cognitive Assessment Scale (MoCA), Hamilton Depression Self-assessment Scale (HAMD), and Hamilton Anxiety Self-assessment Scale (HAMA) were also used to evaluate the cognition, depression, and anxiety of patients at different time points of the study, and analyzed the correlation between the changes of cellular metabolism in the hippocampus and the scores of MoCA, HAMD, and HAMA. RESULTS The levels of NAA/Cr in left/right hippocampal areas were significantly higher at 6 and 12 months after the ESAA than before (1.01±0.17/1.22±0.26 vs. 1.10±0.20/1.05±0.26 vs. 0.82±0.10/0.84±0.11, respectively) (P<0.01). MoCA scores were positively correlated with the levels of NAA/Cr in the left/right hippocampal areas (P<0.05 and P<0.01, respectively). CONCLUSIONS ESAA can improve cognitive function of patients by changing the cellular metabolism of the hippocampus in elderly patients with symptomatic vertebrobasilar artery stenosis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".