Cortical Potentiation in Chronic Neuropathic Pain and the Future Treatment
Bibliographic record
Abstract
Pain, or the ability to feel pain and express the unpleasantness caused by peripheral injuries, are functions of the central nervous system. From peripheral sensory nerve terminals to certain cortical regions of the brain, activation of related neural networks underlies the sensory process. Recently, our knowledge of pain has been increasing dramatically, due to the advancement of scientific approaches. We no longer see the brain as a random matrix for pain but, rather, we are able to identify the step-by-step selective signaling proteins, neurons, and networks that preferentially contribute to the process of chronic pain and its related negative emotions, like anxiety and fear. However, there is still lacking the selective and effective drugs and methods for the treatment of chronic pain clinically. While first-line drugs for acute pain and mental diseases are also applied for the clinical management of chronic pain, their prolonged usage always causes serious side effects. In this short review, we will update and summarize the recent progress in this field and mainly focus on the roles of neural networks and synaptic mechanisms in chronic neuropathic pain. Furthermore, potential drug targets (such as plasticity-related signaling molecules, ionic channels, cytokines, and neuropeptides) and methods for the management of chronic neuropathic pain will be discussed as well. We hope this review can provide new, valuable insight into the treatment of chronic neuropathic 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".