Chemotherapy-evoked neuropathic pain: Abnormal spontaneous discharge in A-fiber and C-fiber primary afferent neurons and its suppression by acetyl-l-carnitine
Bibliographic record
Abstract
Cancer patients treated with antimitotic drugs in the taxane and vinca alkaloid classes sometimes develop a chronic painful peripheral neuropathy whose cause is not understood. In animal models of painful peripheral neuropathy due to nerve trauma or diabetes there is obvious axonal degeneration accompanied by an abnormal incidence of spontaneous discharge in A-fiber and C-fiber nociceptors. But animals with paclitaxel- and vincristine-evoked neuropathic pain do not have axonal degeneration at the level of the peripheral nerve. However, recent data show that they do have a partial degeneration of the primary afferent neurons' terminal arbors in the epidermis. It is not clear as to whether this relatively minor degeneration is accompanied by abnormal spontaneous discharge. We surveyed primary afferent axonal activity in the sural nerve of rats with the paclitaxel- and vincristine-evoked pain syndromes at the time of peak pain severity. Compared to vehicle-injected controls, we find a significant increase in spontaneously discharging A-fibers and C-fibers. Moreover, we show that prophylactic treatment with acetyl-l-carnitine (ALC), which blocks the development of the paclitaxel-evoked pain, causes a significant decrease (ca. 50%) in the incidence of A-fibers and C-fibers with spontaneous discharge. These results suggest that abnormal spontaneous afferent discharge is likely to be a factor in the pathogenesis of chemotherapy-evoked painful peripheral neuropathy, and that the therapeutic effects of ALC may be due to the suppression of this discharge.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".