Neuronal and microglial mechanisms for neuropathic pain in the spinal dorsal horn and anterior cingulate cortex
Bibliographic record
Abstract
Neuropathic pain is a debilitating chronic pain condition occurring after damage in the nervous system and is refractory to the currently available treatments. Major challenges include elucidating its mechanisms and developing new medications to treat it. Nerve injury-induced pain hypersensitivity involves aberrant excitability in spinal dorsal horn (SDH) neurons as a consequence of dysfunction of inhibitory interneurons and of hyperactivity of glial cells, especially microglia, the immune cells of the central nervous system. Evidence of this is found using animal models to investigate the molecular and cellular mechanisms of neuropathic pain. The pathologically altered somatosensory signals in the SDH then convey to the brain regions, including the anterior cingulate cortex (ACC). In these regions, nerve injury produces pre- and postsynaptic long-term plasticity, which contributes to negative emotions and anxiety associated with chronic pain conditions. Furthermore, recent evidence also indicates that the descending projection pathways from the ACC directly and indirectly to the SDH (the top-down corticospinal network) regulate nociceptive sensory transmission in the SDH. Thus, understanding a possible connection between the SDH and ACC, including a neuron-microglia interaction, may provide us with insights into the mechanisms used to amplify pain signals related to neuropathic pain and clues to aid the development of new therapeutic agents for the management of chronic pain. This article is part of the special article series "Pain".
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".