Prostaglandin E<sub>2</sub> depresses <scp>GABA</scp> release onto parvocellular neuroendocrine neurones in the paraventricular nucleus of the hypothalamus via presynaptic receptors
Bibliographic record
Abstract
Inflammation‐induced activation of the hypothalamic‐pituitary‐adrenal ( HPA ) axis and the ensuing release of anti‐inflammatory glucocorticoids are critical for the fine‐tuning of the inflammatory response. This immune‐induced neuroendocrine response is in large part mediated by prostaglandin E 2 ( PGE 2 ), the central actions of which ultimately translate into the excitation of parvocellular neuroendocrine cells ( PNC s) in the hypothalamic paraventricular nucleus. However, the neuronal mechanisms by which PGE 2 excites PNC s remain incompletely understood. In the present study, we report that PGE 2 potently depresses GABA ergic inhibitory synaptic transmission onto PNC s. Using whole‐cell patch clamp recordings obtained from PNC s in ex vivo hypothalamic slices from rats, we found that bath application of PGE 2 (0.01‐100 μmol L ‐1 ) concentration‐dependently decreased the amplitude of evoked inhibitory postsynaptic currents ( eIPSC s) with maximum effects at 10 μmol L ‐1 . The PGE 2 ‐mediated depression of eIPSC s had a rapid onset and was long‐lasting, and also was accompanied by an increase in paired pulse ratio. In addition, PGE 2 decreased the frequency but not the amplitude of both spontaneous IPSC s and miniature IPSC s. These results collectively indicate that PGE 2 acts at a presynaptic locus to decrease the probability of GABA release. Using pharmacological approaches, we also demonstrated that the EP 3 subtype of the PGE 2 receptor mediated the actions of PGE 2 on GABA synapses. Taken together, our results show that PGE 2 , via actions of presynaptic EP 3 receptors, potently depresses GABA release onto PNC s, providing a plausible mechanism for the disinhibition of HPA axis output during inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".