Tumor-infiltrating nerves functionally alter brain circuits and modulate behavior in a mouse model of head-and-neck cancer
Bibliographic record
Abstract
Cancer patients often experience changes in mental health, prompting an exploration into whether nerves infiltrating tumors contribute to these alterations by impacting brain functions. Using a male mouse model for head and neck cancer, we utilized neuronal tracing techniques and show that tumor-infiltrating nerves indeed connect to distinct brain areas via the ipsilateral trigeminal ganglion. The activation of this neuronal circuitry led to behavioral alterations represented by decreased nest-building, increased latency to eat a cookie, and reduced wheel running. Tumor-infiltrating nociceptor neurons exhibited heightened activity, as indicated by increased calcium mobilization. Correspondingly, the specific brain regions receiving these neural projections showed elevated cFos and delta FosB expression in tumor-bearing mice, alongside markedly intensified calcium responses compared to non-tumor-bearing counterparts. The genetic elimination of nociceptor neurons in tumor-bearing mice led to decreased brain Fos expression and mitigated the behavioral alterations induced by the presence of the tumor. While analgesic treatment successfully restored behaviors involving oral movements to normalcy in tumor-bearing mice, it did not have a similar therapeutic effect on voluntary wheel running. This discrepancy points towards an intricate relationship, where pain is not the exclusive driver of such behavioral shifts. Unraveling the interaction between the tumor, infiltrating nerves, and the brain is pivotal to developing targeted interventions to alleviate the mental health burdens associated with cancer.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".