Enhanced Quantal Release of Excitatory Transmitter in Anterior Cingulate Cortex of Adult Mice with Chronic Pain
Why this work is in the frame
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Bibliographic record
Abstract
The anterior cingulate cortex (ACC) is a forebrain structure that plays important roles in emotion, learning, memory and persistent pain. Our previous studies have demonstrated that the enhancement of excitatory synaptic transmission was induced by peripheral inflammation and nerve injury in ACC synapses. However, little information is available on their presynaptic mechanisms, since the source of the enhanced synaptic transmission could include the enhanced probability of neurotransmitter release at existing release sites and/or increases in the number of available vesicles. The present study aims to perform quantal analysis of excitatory synapses in the ACC with chronic pain to examine the source of these increases. The quantal analysis revealed that both probability of transmitter release and number of available vesicles were increased in a mouse model of peripheral inflammation, whereas only probability of transmitter release but not number of available vesicles was enhanced in a mouse model of neuropathic pain. In addition, we compared the miniature excitatory postsynaptic potentials (mEPSCs) in ACC synapses with those in other pain-related brain areas such as the amygdala and spinal cord. Interestingly, the rate and amplitude of mEPSCs in ACC synapses were significantly lower than those in the amygdala and spinal cord. Our studies provide strong evidences that chronic inflammatory pain increases both probability of transmitter release and number of available vesicles, whereas neuropathic pain increases only probability of transmitter release in the ACC synapses.
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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 | 0.001 | 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) | 0.000 | 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 it