Microglial VEGF Receptor Response Is an Integral Chemotactic Component in Alzheimer's Disease Pathology
Bibliographic record
Abstract
We hypothesize that microglial chemotactic responses to amyloid-beta peptide (Abeta(1-42)) serve as an early and integral component of inflammatory response in Alzheimer's disease (AD) brain. This study reports a receptor for vascular endothelial growth factor (VEGF), termed VEGF-1 (Flt-1), subserves microglial chemotactic responses induced by Abeta(1-42) stimulation, in vivo and in vitro. Expression of Flt-1 was significantly increased in tissue obtained from AD patients [compared with tissue from nondemented (ND) individuals], in Abeta(1-42)-injected rat hippocampus, and in peptide-stimulated human microglia. Single and double immunohistochemical staining demonstrated marked immunoreactivity, for both Flt-1 and its ligand VEGF, in association with microglia and Abeta deposits in AD, but not ND, brain tissue. Functionally, treatment with anti-Flt-1 antibody was highly effective in inhibiting microglial mobility and chemotactic responses measured in vitro using a transwell migration assay. In vivo, transplanted enhanced green fluorescent protein (EGFP)-labeled microglia exhibited Flt-1-dependent chemotaxis induced by peptide injection with anti-Flt-1 effective in blocking migration of cells. Importantly, anti-Flt-1 reduction of microglial mobility was neuroprotective in peptide-injected hippocampus and associated with a significant increase in numbers of viable hippocampal neurons. The results of this study suggest critical functional roles for Flt-1 in mediating microglial chemotactic inflammatory responses which contribute to pathological conditions in AD brain.
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 | 0.000 |
| 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) | 0.001 | 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".