Differences and Similarities in Spontaneous Activity Between Animal Models of Cancer-Induced Pain and Neuropathic Pain
Bibliographic record
Abstract
Background: Clinical data on cancer-induced pain (CIP) demonstrate widespread changes in sensory function. It is characterized in humans not only by stimulus-invoked pain, but also by spontaneous pain. In our previous studies in an animal model of CIP, we observed changes in intrinsic membrane properties and excitability of dorsal root ganglion (DRG) sensory neurons corresponding to mechanical allodynia and hyperalgesia, of which abnormal activities of Aβ-fiber sensory neurons are consistent in a rat model of peripheral neuropathic pain (NEP). Objective: To investigate whether there are related peripheral neural mechanisms between the CIP and NEP models of spontaneous pain, we compared the electrophysiological properties of DRG sensory neurons at 2-3 weeks after CIP and NEP model induction. Methods: CIP models were induced with metastasis tumour-1 rat breast cancer cells implanted into the distal epiphysis of the femur. NEP models were induced with a polyethylene cuff implanted around the sciatic nerve. Spontaneous pain in animals is measured by spontaneous foot lifting (SFL). After measurement of SFL, the animals were prepared for electrophysiological recordings of spontaneous activity (SA) in DRG neurons in vivo. Results: Our data showed that SFL and SA occurred in both models. The proportion of SFL and SA of C-fiber sensory neurons in CIP was more significantly increased than in NEP models. There was no difference in duration of SFL and the rate of SA between the two models. The duration of SFL is related to the rate of SA in C-fiber in both models. Conclusion: Thus, SFL may result from SA activity in C-fiber neurons in CIP and NEP rats. The differences and similarities in spontaneous pain between CIP and NEP rats is related to the proportion and rate of SA in C-fibers, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".