Insulin exerts neuroprotection by counteracting the decrease in cell‐surface GABA<sub>A</sub> receptors following oxygen–glucose deprivation in cultured cortical neurons
Bibliographic record
Abstract
A loss of balance between excitatory and inhibitory signaling leads to excitoxicity, and contributes to ischemic cell death. Reduced synaptic inhibition as a result of dysfunction of the ionotropic GABAA receptor has been suggested as one of the major causes for this imbalance, although the underlying mechanisms remain poorly understood. In the present study, we investigated whether oxygen-glucose deprivation (OGD), an ischemia-like challenge, alters cell-surface expression of GABAA receptors in cultured hippocampal neurons, and thereby leads to excitotoxic cell death. Using cell culture ELISA as a cell surface receptor assay, we found that OGD produced a marked decrease in cell surface GABAA receptors, without altering the total amount of receptors. Furthermore, the reduction could be prevented by inhibition of receptor endocytosis with hypertonic sucrose treatment. Notably, insulin significantly limited OGD-induced changes in cell-surface GABAA receptors. In parallel, insulin protected cultured neurons against both glutamate toxicity and OGD, as assayed by mitochondrial reduction of Alamar Blue. Importantly, insulin-mediated neuroprotection was eliminated when bicuculline, a GABAA receptor antagonist, was co-applied with insulin during OGD. Together, our results strongly suggest that ischemia-like insults decrease cell surface GABAA receptors in neurons via accelerated internalization, and that insulin provides neuroprotection by counteracting this reduction.
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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) | 0.000 | 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 | 0.001 |
| 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".