A role for astrocyte‐derived amyloid β peptides in the degeneration of neurons in an animal model of temporal lobe epilepsy
Bibliographic record
Abstract
Kainic acid, an analogue of the excitatory neurotransmitter glutamate, can trigger seizures and neurotoxicity in the hippocampus and other limbic structures in a manner that mirrors the neuropathology of human temporal lobe epilepsy (TLE). However, the underlying mechanisms associated with the neurotoxicity remain unclear. Since amyloid-β (Aβ) peptides, which are critical in the development of Alzheimer's disease, can mediate toxicity by activating glutamatergic NMDA receptors, it is likely that the enhanced glutamatergic transmission that renders hippocampal neurons vulnerable to kainic acid treatment may involve Aβ peptides. Thus, we seek to establish what role Aβ plays in kainic acid-induced toxicity using in vivo and in vitro paradigms. Our results show that systemic injection of kainic acid to adult rats triggers seizures, gliosis and loss of hippocampal neurons, along with increased levels/processing of amyloid precursor protein (APP), resulting in the enhanced production of Aβ-related peptides. The changes in APP levels/processing were evident primarily in activated astrocytes, implying a role for astrocytic Aβ in kainic acid-induced toxicity. Accordingly, we showed that treating rat primary cultured astrocytes with kainic acid can lead to increased Aβ production/secretion without any compromise in cell viability. Additionally, we revealed that kainic acid reduces neuronal viability more in neuronal/astrocyte co-cultures than in pure neuronal culture, and this is attenuated by precluding Aβ production. Collectively, these results indicate that increased production/secretion of Aβ-related peptides from activated astrocytes can contribute to neurotoxicity in kainic acid-treated rats. Since kainic acid administration can lead to neuropathological changes resembling TLE, it is likely that APP/Aβ peptides derived from astrocytes may have a role in TLE pathogenesis.
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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 | 0.001 |
| 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 | 0.000 |
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".