Is Matrix Metalloproteinase-9 Associated with Post-Stroke Cognitive Impairment or Dementia?
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinase-9 (MMP-9) is a significant protease required for synaptic plasticity, learning, and memory. Yet, the role of MMP-9 in the occurrence and development of cognitive decline after ischemic stroke is not fully understood. In this study, we used clinical data experiments to further investigate whether MMP-9 and genetic polymorphism are associated with post-stroke cognitive impairment or dementia (PSCID). MATERIALS AND METHODS: A total of 148 patients with PSCID confirmed by the Montreal Cognitive Assessment (MoCA) 3 months after onset (PSCID group) were included in the study. The MMP-9 rs3918242 polymorphisms were analyzed using polymerase chain reaction coupled with restriction fragment length polymorphism, and the serum level of MMP-9 was measured using enzyme-linked immunosorbent assay (ELISA). The same manipulations have been done on 169 ischemic stroke patients without cognitive impairment (NCI group) and 150 normal controls (NC group). RESULTS: < 0.05). Diabetes mellitus, hyperhomocysteinemia, and increased serum MMP-9 levels were the main risk factors of cognitive impairment after ischemic stroke. The serum level of MMP-9 was negatively correlated with the MoCA score, including visual-spatial executive, naming, attention, language, and delayed recall. Genetic polymorphism showed that TC genotype with MMP-9 rs3918242 and CC genotype were associated with a significantly increased risk of PSCID; moreover, the TC genotype significantly increased the risk of cognitive impairment. In the TCCC genotype of MMP-9 rs3918242, diabetes mellitus and hyperhomocysteinemia were associated with the increased risk of PSCID; also, hyperhomocysteinemia could increase the risk of cognitive impairment. CONCLUSIONS: MMP-9 level and MMP-9 rs3918242 polymorphism have an important role in the occurrence and development of post-stroke cognitive impairment or dementia (PSCID).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".