Ethanol Inhibition of Glycine-Activated Responses in Neurons of Ventral Tegmental Area of Neonatal Rats
Bibliographic record
Abstract
The brain is particularly sensitive to alcohol during the period of its rapid growth. To better understand the mechanism(s) involved, we studied ethanol effects on glycine-activated responses of ventral tegmental area (VTA) neurons isolated from the newborn rat, using whole cell and gramicidin perforated patch-clamp techniques. Previously we reported that 0.1-40 mM ethanol enhances glycine-induced responses of 35% of VTA neurons. We now direct our attention to the inhibitory effects of ethanol observed in 45% (312 of 694) of neonatal VTA neurons. Under current-clamp conditions, 1 mM ethanol had no effect on the membrane potential of these cells, but it decreased glycine-induced membrane depolarization and the frequency of spontaneous action potentials. Under voltage-clamp conditions, 0.1-10 mM ethanol did not elicit a current but depressed the glycine-induced currents. The ethanol-induced inhibition of glycine current was independent of membrane potential (between -60 and +60 mV). Likewise, ethanol did not alter the reversal potential of the glycine-activated currents. Ethanol-mediated inhibition of glycine current depended on the glycine concentration. While ethanol strongly depressed currents activated by 30 microM glycine, it had no appreciable effect on maximal currents activated by 1 mM glycine. In the presence of ethanol (1 mM), the EC(50) for glycine increased from 32 +/- 5 to 60 +/- 3 microM. Thus ethanol may decrease the agonist affinity of glycine receptors. A kinetic analysis indicated that ethanol shortens the time constant of glycine current deactivation but has no effect on activation. In conclusion, by altering VTA neuronal function, ethanol-induced changes in glycine receptors may contribute to neurobehavioral manifestations of the fetal alcohol syndrome.
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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 | 0.000 |
| 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".