Amyloid-β Inhibits PDGFβ Receptor Activation and Prevents PDGF-BBInduced Neuroprotection
Bibliographic record
Abstract
BACKGROUND: PDGFβ receptors and their ligand, PDGF-BB, are upregulated in vivo after neuronal insults such as ischemia. When applied exogenously, PDGF-BB is neuroprotective against excitotoxicity and HIV proteins. OBJECTIVE: Given this growth factor's neuroprotective ability, we sought to determine if PDGF-BB would be neuroprotective against amyloid-β (1-42), one of the pathological agents associated with Alzheimer's disease (AD). METHODS AND RESULTS: In both primary hippocampal neurons and the human-derived neuroblastoma cell line, SH-SY5Y, amyloid-β treatment for 24 h decreased surviving cell number in a concentrationdependent manner. Pretreatment with PDGF-BB failed to provide any neuroprotection against amyloid-β in primary neurons and only very limited protective effects in SH-SY5Y cells. In addition to its neuroprotective action, PDGF promotes cell growth and division in several systems, and the application of PDGFBB alone to serum-starved SH-SY5Y cells resulted in an increase in cell number. Amyloid-β attenuated the mitogenic effects of PDGF-BB, inhibited PDGF-BB-induced PDGFβ receptor phosphorylation, and attenuated the ability of PDGF-BB to protect neurons against NMDA-induced excitotoxicity. Despite the ability of amyloid-β to inhibit PDGFβ receptor activation, immunoprecipitation experiments failed to detect a physical interaction between amyloid-β and PDGF-BB or the PDGFβ receptor. However, G protein-coupled receptor transactivation of the PDGFβ receptor (an exclusively intracellular signaling pathway) remained unaffected by the presence of amyloid-β. CONCLUSIONS: As the PDGF system is upregulated upon neuronal damage, the ability of amyloid-β to inhibit this endogenous neuroprotective system should be further investigated in the context of AD pathophysiology.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".