Study on the Correlation of Glutamate and Martix Metalloproteinase-9 with Cerebral Infarctione
Bibliographic record
Abstract
Objective: To observe the dynamic changes of glutamate(GLU) and metalloproteinase-9(MMP-9) in patients with cerebral infarction and the relation of Glu、MMP-9 levels to infarction size and intensity of neurological deficit and to investigate clinically the effect of Glu and MMP-9 on the time course in ischemic stroke by detecting Glu and MMP-9 in plasma.Methods: By high-performance liquid chromatography(HLPC),Glu concentration in plasma was determined and by ELISA MMP-9 concentration in plasma was measured in 70 patients with acute cerebral infarction(ACI) and 20 controls on the third day and 14th day after onset.At the same time,Canadian Stroke Scale(CSS) score and cerebral infarction volume(CTV) were also obtained and analyzed.Results: Mean plasma Glu、MMP-9 concentrations in 70 patients significantly elevated compared with controls on the third day,(P0.05).Moreover,Mean plasma Glu、MMP-9 concentrations in patients with CTV≥10 cm3 and CSS 30 were significantly higher than those in the patients with CTV10 cm3 and CSS≤30,(P0.05).On the third day Glu concentrations was significantly positively correlated with MMP-9 in plasma(r=0.499,P0.001).Conclusion: Glu、MMP-9 are involved in the pathologic process of cerebral infarction.The concentrations of Glu and MMP-9 may be taken as an objective guide to judge the degree,size of infarction area and the recent prognosis in acute cerebral infarction.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".