Interactions between beta-amyloid and central cholinergic neurons: implications for Alzheimer's disease.
Bibliographic record
Abstract
Alzheimer's disease is an age-related neurodegenerative disorder that is characterized by a progressive loss of memory and deterioration of higher cognitive functions. The brain of an individual with Alzheimer's disease exhibits extracellular plaques of aggregated beta-amyloid protein (Abeta), intracellular neurofibrillary tangles that contain hyperphosphorylated tau protein and a profound loss of basal forebrain cholinergic neurons that innervate the hippocampus and the neocortex. Abeta accumulation may trigger or contribute to the process of neurodegeneration. However, the mechanisms whereby Abeta induces basal forebrain cholinergic cell loss and cognitive impairment remain obscure. Physiologically relevant concentrations of Abeta-related peptides have acute, negative effects on multiple aspects of acetylcholine (ACh) synthesis and release, without inducing toxicity. These data suggest a neuromodulatory influence of the peptides on central cholinergic functions. Long-term exposure to micromolar Abeta induces cholinergic cell toxicity, possibly via hyperphosphorylation of tau protein. Conversely, activation of selected cholinergic receptors has been shown to alter the processing of the amyloid precursor protein as well as phosphorylation of tau protein. A direct interaction between Abeta and nicotinic ACh receptors has also been demonstrated. This review addresses the role of Abeta-related peptides in regulating the function and survival of central cholinergic neurons and the relevance of these effects to cholinergic deficits in Alzheimer's disease. Understanding the functional interrelations between Abeta peptides, cholinergic neurons and tau phosphorylation will unravel the biologic events that precede neurodegeneration and may lead to the development of more effective pharmacotherapies for Alzheimer's disease.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.002 |
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".