Bibliographic record
Abstract
Neurons and synapses in the central nervous systems are very dynamic and plastic, and can undergo changes throughout life. Studies of molecular and cellular mechanisms of such changes not only provide important insight into how we learn and store new knowledge in our brains, but also reveal the mechanisms of pathological changes occurring following an injury. Here, we propose that while neuronal mechanisms underlying physiological functions such as learning and memory may share some common signalling molecules with abnormal or injury-related changes in the brain, distinct synaptic mechanisms are involved in pathological pain as compared with that of cognitive learning and memory. Using genetically altered mice and classic physiological approaches, we showed that N-methyl-D-aspartate (NMDA) receptor-dependent, calcium-calmodulin-activated adenylyl cyclases (AC1 and AC8) in the anterior cingulate cortex (ACC) play important roles in the induction and expression of persistent inflammatory and neuropathic pain. In contrast, acute pain was not significantly affected. Calcium-calmodulin-dependent protein kinase IV, which is widely expressed in central areas related to pain and memory, primarily contributes to injury-related fearful memory and emotional responses. Our studies suggest distinct signalling pathways are responsible for physiological responses to the injury, including behavioural, emotional and memory.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".