Endogenously generated Dutch-type Aβ nonfibrillar aggregates dysregulate presynaptic neurotransmission in the absence of detectable inflammation
Bibliographic record
Abstract
Abstract BACKGROUND APP E693Q (“Dutch”) transgenic mice develop aging‐related learning deficits and accumulate endogenously generated non‐fibrillar aggregates (NFAs) of amyloid beta (Aβ) and amyloid precursor protein α‐carboxy terminal fragments. NFA‐Aβ correlates with synaptic loss and memory deficits more closely than does fibrillar Aβ. METHODS We assessed the physiological, transcriptomic, ultrastructural, histological, and metabolic changes associated with the accumulation of NFA of Dutch Aβ in brains of APP E693Q mice. RESULTS Aging‐related accumulation of NFA‐Aβ in APP E693Q mice was revealed by A11 immunohistochemistry and cyclic D,L‐α‐peptide‐fluorescein‐5‐isothiocyanate microscopy. Presynaptic termini of APP E693Q mice developed physiological abnormalities in post‐tetanic potentiation, synaptic fatigue, synaptic vesicle replenishment, and an aging‐related reduction in mitochondrial complex I activity. Single‐cell RNA sequencing showed that excitatory neurons exhibited an altered transcriptomic profile involving “protein translation” and “oxidative phosphorylation.” DISCUSSION Accumulation of NFA‐Aβ alters neuronal metabolism but does not activate inflammation. Depletion of all forms of Aβ may be required to eliminate Aβ toxicity with anti‐amyloid antibodies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".