Neuronal receptors as targets for the action of amyloid-beta protein (Aβ) in the brain
Bibliographic record
Abstract
Accumulation of neurotoxic soluble amyloid-beta protein (Aβ) oligomers in the brains of patients with Alzheimer disease (AD) and their role in AD pathogenesis have emerged as topics of considerable interest in recent years. Soluble Aβ oligomers impair synaptic and neuronal function, leading to neurodegeneration that is clinically manifested by memory and cognitive dysfunction. The precise mechanisms whereby Aβ oligomers cause neurotoxicity remain unknown. Emerging insights into the mechanistic link between neuronal receptors and soluble Aβ oligomers highlight the potential role of these receptors in Aβ-mediated neurotoxicity in AD. The current review focuses on studies describing interactions between soluble Aβ oligomers and neuronal receptors, and their role in AD pathogenesis. Furthermore, these studies provide insight into potential therapies for AD using compounds directed at putative target receptors for the action of Aβ in the central nervous system. We focus on interactions of Aβ with subtypes of acetylcholine and glutamatergic receptors. Additionally, neuronal receptors such as insulin, amylin and receptor for advanced glycation end products could be potential targets for soluble Aβ-oligomer-mediated neurotoxicity. Aβ interactions with other receptors such as the p75 neurotrophin receptors, which are highly expressed on cholinergic basal forebrain neurons lost in AD, are also highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".