Plasma macrophage migration inhibitory factor and matrix metalloproteinase-9 levels, and their related factors in Alzheimer’s disease
Bibliographic record
Abstract
Previous studies have shown that the levels of macrophage migration inhibitory factor (MIF) and matrix metalloproteinase-9 (MMP-9) in its downstream signaling pathway are related to the occurrence and development of Alzheimer’s disease (AD); some studies have suggested that plasma levels of MIF and MMP-9 could be used as potential biomarkers for AD. This study aimed to explore the changes in MIF and MMP-9 levels in the plasma of patients with AD and whether they were correlated with other clinical indicators and cognitive function. Altogether, 43 patients with AD and 40 healthy controls (HCs) were enrolled in this study. Socio-demographic information of the subjects was collected, and their cognitive function was assessed using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Further, the biochemical indicators and plasma MIF and MMP-9 levels were detected. Our study found that plasma MIF levels were not significantly altered in patients with AD compared to HCs, while MMP-9 levels were significantly increased, and prolactin levels had significant effects on MMP-9 levels. In addition, plasma levels of MIF and MMP-9 in patients with AD had no significant correlation with cognitive function. In summary, plasma levels of MIF and MMP-9 in AD patients may be influenced by multiple factors and could vary significantly across different disease stages. Further studies are needed to elucidate their roles and underlying mechanisms in AD.
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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 | 0.000 |
| 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) | 0.001 | 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".