Amyloid β-Induced Death in Neurons Involves Glial and Neuronal Hemichannels
Bibliographic record
Abstract
The mechanisms involved in Alzheimer's disease are not completely understood and how glial cells contribute to this neurodegenerative disease remains to be elucidated. Because inflammatory treatments and products released from activated microglia increase glial hemichannel activity, we investigated whether amyloid-β peptide (Aβ) could regulate these channels in glial cells and affect neuronal viability. Microglia, astrocytes, or neuronal cultures as well as acute hippocampal slices made from GFAP-eGFP transgenic mice were treated with the active fragment of Aβ. Hemichannel activity was monitored by single-channel recordings and by time-lapse ethidium uptake, whereas neuronal death was assessed by Fluoro-Jade C staining. We report that low concentrations of Aβ(25-35) increased hemichannel activity in all three cell types and microglia initiate these effects triggered by Aβ. Finally, neuronal damage occurs by activation of neuronal hemichannels induced by ATP and glutamate released from Aβ(25-35)-activated glia. These responses were observed in the presence of external calcium and were differently inhibited by hemichannel blockers, whereas the Aβ(25-35)-induced neuronal damage was importantly reduced in acute slices made from Cx43 knock-out mice. Thus, Aβ leads to a cascade of hemichannel activation in which microglia promote the release of glutamate and ATP through glial (microglia and astrocytes) hemichannels that induces neuronal death by triggering hemichannels in neurons. Consequently, this work opens novel avenues for alternative treatments that target glial cells and neurons to maintain neuronal survival in the presence of Aβ.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".