Measurement of Gelatinase B (MMP-9) in the Cerebrospinal Fluid of Patients With Vascular Dementia and Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Vascular causes of dementia are increasing in importance because of the aging of the population. Biological markers to distinguish patients with vascular dementia (VaD) from Alzheimer disease (AD) would be very useful. Because cerebrovascular disease increases expression of brain matrix metalloproteinases (MMPs) and tissue inhibitors to metalloproteinases (TIMPs), we hypothesized that MMPs would be elevated in the cerebrospinal fluid (CSF) of patients with VaD, but not in patients with AD. METHODS: Fifteen patients with VaD were identified, including dementia caused by multiple infarcts and progressive dementia caused by disease of the small cerebral blood vessels. Patients were followed-up for 4 to 10 years to confirm the diagnosis. Thirty patients with AD were also studied. Patients had CSF collected at their initial evaluation. Gelatinase A (MMP-2) and gelatinase B (MMP-9) were quantified by gelatin-substrate zymography, and TIMPs were measured by reverse zymography. Control CSF was obtained from neurologically normal subjects. RESULTS: MMP-9 levels were significantly elevated in the CSF of VaD patients compared either to those with AD (P<0.0001) or to controls. MMP-2, TIMP-1, and TIMP-2 were similar in patient groups and controls. CONCLUSIONS: Patients with multiinfarct and small vessel VaD have elevated levels of MMP-9 in the CSF compared with AD and controls. Although CSF MMP-9 increases in other neurological conditions and is not specific for VaD, it could provide an additional biological marker for the separation of patients with VaD and AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".