Glial fibrillar acidic protein in the cerebrospinal fluid of Alzheimer's disease, dementia with Lewy bodies, and frontotemporal lobar degeneration
Bibliographic record
Abstract
Biomarkers in the cerebrospinal fluid (CSF) are currently regarded as indispensable indicators for accurate differential diagnosis of neurodegenerative disorders. Although high levels of astrocyte-secreted glial fibrillar acidic protein (GFAP) in the CSF of patients with Alzheimer's disease (AD) have been reported, the levels of GFAP in the CSF have not been fully investigated in other neurological disorders that cause dementia, such as dementia with Lewy bodies (DLB) and frontotemporal lobar degeneration (FTLD). In this study, we determined the levels of GFAP in the CSF of healthy control subjects and AD, DLB, and FTLD patients to address two questions: (i) Do the levels of GFAP differ among these disorders? and (ii) Can GFAP be used as a biomarker for the differential diagnosis of these neurodegenerative disorders? The levels of GFAP in AD, DLB, and FTLD patients were significantly higher than those in the healthy control subjects. Although the levels of GFAP were not significantly different between AD and DLB patients, a higher level of GFAP was observed in FTLD patients than in AD and DLB patients. It is concluded that representative neurological disorders causing dementia were associated with higher levels of GFAP in the CSF. We propose the following mechanism concerning the amount of glial fibrillar acidic protein (GFAP) in the cerebrospinal fluid (CSF) in Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). The increase in the release of GFAP into CSF is considered to reflect the sum of degeneration of astrocytes and astrocytosis. The sum of degeneration and astrocytosis or the GFAP release could be in the order of FTLD > DLB > AD > normal condition.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".