Influence of Inflammatory Pain and Dopamine on Synaptic Transmission in the Mouse ACC
Bibliographic record
Abstract
Dopamine (DA) inhibits excitatory synaptic transmission in the anterior cingulate cortex (ACC), a brain region involved in the sensory and affective processing of pain. However, the DA modulation of inhibitory synaptic transmission in the ACC and its alteration of the excitatory/inhibitory (E/I) balance remains relatively understudied. Using patch-clamp recordings, we demonstrate that neither DA applied directly to the tissue slice nor complete Freund's adjuvant (CFA) injected into the hind paw significantly impacted excitatory currents (eEPSCs) in the ACC, when recorded without pharmacological isolation. However, individual neurons exhibited varied responses to DA, with some showing inhibition, potentiation, or no response. The degree of eEPSC inhibition by DA was higher in naïve slices compared to that in the CFA condition. The baseline inhibitory currents (eIPSCs) were greater in the CFA-treated slices, and DA specifically inhibited eIPSCs in the CFA-treated, but not naïve group. DA and CFA treatment did not alter the balance between excitatory and inhibitory currents. Spontaneous synaptic activity revealed that DA reduced the frequency of the excitatory currents in CFA-treated mice and decreased the amplitude of the inhibitory currents, specifically in CFA-treated mice. However, the overall synaptic drive remained similar between the naïve and CFA-treated mice. Additionally, GABAergic currents were pharmacologically isolated and found to be robustly inhibited by DA through postsynaptic D2 receptors and G-protein activity. Overall, the study suggests that CFA-induced inflammation and DA do not significantly affect the balance between excitatory and inhibitory currents in ACC neurons, but activity-dependent changes may be observed in the DA modulation of presynaptic glutamate release in the presence of inflammation.
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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 | 0.001 | 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".