Inhibitory and excitatory synaptic neuroadaptations in the diazepam tolerant brain
Bibliographic record
Abstract
Benzodiazepine (BZ) drugs treat seizures, anxiety, insomnia, and alcohol withdrawal by potentiating γ2 subunit containing GABA type A receptors (GABAARs). BZ clinical use is hampered by tolerance and withdrawal symptoms including heightened seizure susceptibility, panic, and sleep disturbances. Here, we investigated inhibitory GABAergic and excitatory glutamatergic plasticity in mice tolerant to benzodiazepine sedation. Repeated diazepam (DZP) treatment diminished sedative effects and decreased DZP potentiation of GABAAR synaptic currents without impacting overall synaptic inhibition. While DZP did not alter γ2-GABAAR subunit composition, there was a redistribution of extrasynaptic GABAARs to synapses, resulting in higher levels of synaptic BZ-insensitive α4-containing GABAARs and a concomitant reduction in tonic inhibition. Conversely, excitatory glutamatergic synaptic transmission was increased, and NMDAR subunits were upregulated at synaptic and total protein levels. Quantitative proteomics further revealed cortex neuroadaptations of key pro-excitatory mediators and synaptic plasticity pathways highlighted by Ca2+/calmodulin-dependent protein kinase II (CAMKII), MAPK, and PKC signaling. Thus, reduced inhibitory GABAergic tone and elevated glutamatergic neurotransmission contribute to disrupted excitation/inhibition balance and reduced BZ therapeutic power with benzodiazepine tolerance.
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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 | 0.000 |
| 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".