Enhanced Quantal Release of Excitatory Transmitter in Anterior Cingulate Cortex of Adult Mice with Chronic Pain
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".