The Nerve Growth Factor Metabolic Pathway Dysregulation as Cause of Alzheimer’s Cholinergic Atrophy
Bibliographic record
Abstract
The cause of the loss of basal forebrain cholinergic neurons (BFCNs) and their terminal synapses in the cerebral cortex and hippocampus in Alzheimer's disease (AD) has provoked a decades-long controversy. The cholinergic phenotype of this neuronal system, involved in numerous cognitive mechanisms, is tightly dependent on the target-derived nerve growth factor (NGF). Consequently, the loss of BFCNs cholinergic phenotype in AD was initially suspected to be due to an NGF trophic failure. However, in AD there is a normal NGF synthesis and abundance of the NGF precursor (proNGF), therefore the NGF trophic failure hypothesis for the atrophy of BCNs was abandoned. In this review, we discuss the history of NGF-dependency of BFCNs and the atrophy of these neurons in Alzheimer's disease (AD). Further to it, we propose that trophic factor failure explains the BFCNs atrophy in AD. We discuss evidence of the occurrence of a brain NGF metabolic pathway, the dysregulation of which, in AD explains the severe deficiency of NGF trophic support for the maintenance of BFCNs cholinergic phenotype. Finally, we revise recent evidence that the NGF metabolic dysregulation in AD pathology starts at preclinical stages. We also propose that the alteration of NGF metabolism-related markers in body fluids might assist in the AD preclinical diagnosis.
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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 | 0.001 |
| 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 | 0.001 |
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".