Brain-Derived Neurotrophic Factor Silences GABA Synapses Onto Hypothalamic Neuroendocrine Cells Through a Postsynaptic Dynamin-Mediated Mechanism
Bibliographic record
Abstract
In the paraventricular nucleus of the hypothalamus (PVN), experimental stress paradigms that suppress gamma-aminobutyric acid (GABA) inputs to parvocellular neuroendocrine cells (PNCs) also increase the expression of brain-derived neurotrophic factor (BDNF). In the adult CNS, BDNF regulates the efficacy of GABAergic transmission, but its contributions to functional changes at inhibitory synapses in the PVN have not been investigated. Analysis of quantal transmission revealed a decrease in the frequency of miniature inhibitory postsynaptic currents (mIPSCs) in response to BDNF with no accompanying changes in their amplitude. These effects were completely blocked by prior inclusion of the TrKB receptor antagonist K252a in the patch pipette. Inclusion of a dynamin inhibitory peptide in the patch pipette also blocked the effects of BDNF, consistent with an all-or-none removal of clusters of postsynaptic GABAA receptors. Finally, to confirm a decrease in the availability of postsynaptic GABAA receptors, we tested the effects of BDNF on focal application of the GABAA agonist muscimol. Postsynaptic responses to muscimol were reduced after BDNF. Collectively, these data indicate that BDNF remodels functional synaptic contacts putatively by reducing the surface expression of postsynaptic GABAA 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 | 0.000 |
| 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".