Ketamine Increases the Function of γ-Aminobutyric Acid Type A Receptors in Hippocampal and Cortical Neurons
Bibliographic record
Abstract
Introducción:The "dissociative" general anesthetic ketamine is a well-known NMDA receptor antagonist 1 .However, whether ketamine, at clinically relevant concentrations, increases the activity of inhibitory GABA A receptors in different brain regions remains controversial 2,3,4 .Here, we studied the effects of ketamine on synaptic and extrasynaptic GABA A receptors in hippocampal neurons.Ketamine modulation of extrasynaptic GABA A receptors in cortical neurons was also examined.Objetivo General: To determine whether ketamine potentiates the function of extrasynaptic and synaptic GABA A receptors in the hippocampus and cortex.Material y Métodos: Whole-cell currents were recorded from primary cultures of hippocampal and cortical neurons of mice.Current evoked by exogenous GABA, miniature inhibitory postsynaptic currents, and currents directly activated by ketamine were studied.Data are represented as mean ± S.E.M. together with the 95% confidence interval of the mean (CI).Student's t test (paired or unpaired), one-way analysis of variance (ANOVA), two-way ANOVA were used where appropriate.Cumulative distributions of the amplitude and frequency of mIPSCs were compared using the Kolmogorov-Smirnov test.Pearson correlation coefficient was used to measure the strength of concentration-dependent effects.Statistical significance was set at p < 0.05.No statistical power calculation was conducted prior to the study.The sample size was based on our previous experience with this experimental design.
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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) | 0.004 | 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".