Bone cancer-induced pain is associated with glutamate signalling in peripheral sensory neurons
Bibliographic record
Abstract
We previously identified that several cancer cell lines known to induce nociception in mouse models release glutamate in vitro. Although the mechanisms of glutamatergic signalling have been characterized primarily in the central nervous system, its importance in the peripheral nervous system has been recognized in various pathologies, including cancer pain. We therefore investigated the effect of glutamate on intracellular electrophysiological characteristics of peripheral sensory neurons in an immunocompetent rat model of cancer-induced pain based on surgical implantation of mammary rat metastasis tumour-1 cells into the distal epiphysis of the right femur. Behavioural evidence of nociception was detected using von Frey tactile assessment. Activity of sensory neurons was measured by intracellular electrophysiological recordings in vivo. Glutamate receptor expression at the mRNA level in relevant dorsal root ganglia was determined by reverse transcription polymerase chain reaction using rat-specific primers. Nociceptive and non-nociceptive mechanoreceptor neurons exhibiting changes in neural firing patterns associated with increased nociception due to the presence of a bone tumour rapidly responded to sulphasalazine injection, an agent that pharmacologically blocks non-vesicular glutamate release by inhibiting the activity of the system x C − antiporter. In addition, both types of mechanoreceptor neurons demonstrated excitation in response to intramuscular glutamate injection near the femoral head, which corresponds to the location of cancer cell injection to induce the bone cancer-induced pain model. Therefore, glutamatergic signalling contributes to cancer pain and may be a factor in peripheral sensitization and induced tactile hypersensitivity associated with bone cancer-induced 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) | 0.000 | 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".