Effects of Ethanol on Synaptic Plasticity and NMDA Currents in the Juvenile Rat Dentate Gyrus
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We examined how acute ethanol (EtOH) exposure affects long term depression (LTD) in the dentate gyrus (DG) of the hippocampus in juvenile rats. EtOH is thought to directly modulate n-methyl-D-aspartate receptor (NMDAr) currents, which are believed important for LTD induction. LTD in turn is believed to play an important developmental role in the hippocampus by facilitating synaptic pruning. METHODS: m) were obtained at post-natal day (PND) 14, 21, or 28. Field EPSPs (excitatory post-synaptic potential) or whole-cell EPSCs (excitatory post-synaptic conductance) were recorded from the DG (dentate gyrus) in response to medial perforant path activation. Low-frequency stimulation (LFS; 900 pulses; 120 s pulse) was used to induce LTD. RESULTS: Whole-cell recordings indicated that EtOH exposure at 50mM did not significantly impact ensemble NMDAr EPSCs in slices obtained from animals in the PND14 or 21 groups, but it reliably produced a modest inhibition in the PND28 group. Increasing the concentration to 100 mM resulted in a modest inhibition of NMDAr EPSCs in all three groups. LTD induction and maintenance was equivalent in magnitude in all three age groups in control conditions, however, and surprisingly, NMDA antagonist AP5 only reliably blocked LTD in the PND21 and 28 age groups. The application of 50 mM EtOH attenuated LTD in all three age groups, however increasing the concentration to 100 mM did not reliably inhibit LTD. CONCLUSIONS: These results indicate that the effect of EtOH on NMDAr-EPSCs recorded from DGCs is both age and concentration dependent in juveniles. Low concentrations of EtOH can attenuate, but did not block LTD in the DG. The effects of EtOH on LTD do not align well with it's effects on NNMDA receptors.
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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".