Neurotoxicity and Memory Deficits Induced by Soluble Low-Molecular-Weight Amyloid- 1-42 Oligomers Are Revealed In Vivo by Using a Novel Animal Model
Bibliographic record
Abstract
Neuronal and synaptic degeneration are the best pathological correlates for memory decline in Alzheimer's disease (AD). Although the accumulation of soluble low-molecular-weight amyloid-β (Aβ) oligomers has been suggested to trigger neurodegeneration in AD, animal models overexpressing or infused with Aβ lack neuronal loss at the onset of memory deficits. Using a novel in vivo approach, we found that repeated hippocampal injections of small soluble Aβ(1-42) oligomers in awake, freely moving mice were able to induce marked neuronal loss, tau hyperphosphorylation, and deficits in hippocampus-dependent memory. The neurotoxicity of small Aβ(1-42) species was observed in vivo as well as in vitro in association with increased caspase-3 activity and reduced levels of the NMDA receptor subunit NR2B. We found that the sequestering agent transthyretin is able to bind the toxic Aβ(1-42) species and attenuated the loss of neurons and memory deficits. Our novel mouse model provides evidence that small, soluble Aβ(1-42) oligomers are able to induce extensive neuronal loss in vivo and initiate a cascade of events that mimic the key neuropathological hallmarks of AD.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".