Hippocampal GABAergic Neurons are Susceptible to Amyloid-β Toxicity in vitro and are Decreased in Number in the Alzheimer's Disease TgCRND8 Mouse Model
Bibliographic record
Abstract
The relevance of γ-amino-butyric acid (GABA)-ergic dysfunctions in the pathology of Alzheimer's disease (AD) remains a matter of debate. In the present study, we characterized the toxicity of amyloid-β (Aβ) on hippocampal GABAergic neurons both in vivo and in vitro. In the TgCRND8 mouse model of AD, we found a significant decrease in the number of hippocampal neurons immunoreactive for glutamate decarboxylase 67 (GAD67), the enzyme synthesizing GABA. This decrease, which was specific for hippocampal CA1-3 fields, was observed at 6 months of age, long after the overproduction of soluble Aβ42 (between 2 and 4 months) and accumulation of insoluble Aβ into amyloid plaques (between 4 and 6 months). In vitro, neurotoxicity was observed in primary hippocampal cultures 72 h following the addition of Aβ42 solutions containing a mixture of soluble oligomers. Taken together, our results suggest that when cultured and exposed to Aβ in vitro, GABAergic neurons are susceptible to Aβ42 neurotoxicity. However, in TgCRND8 mice, the number of GABAergic neurons is not altered up to 6 months, in spite of the massive Aβ load. Combined with the previously reported increased sensitivity to seizures observed in younger (1.5-2 month-old) TgCRND8 mice, it is likely that Aβ toxicity leads to GABAergic neuron dysfunction prior to their losses at a later stage.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".