<p>Activation of hippocampal microglia in a murine model of cancer-induced pain</p>
Bibliographic record
Abstract
INTRODUCTION: Pain is a common and debilitating comorbidity of metastatic breast cancer. The hippocampus has been implicated in nociceptive processing, particularly relating to the subjective aspect of pain. Here, a syngeneic mouse model was used to characterize the effects of peripheral tumors on hippocampal microglial activation in relation to cancer-induced pain (CIP). MATERIALS AND METHODS: Mice were systemically treated with the colony-stimulating factor 1 receptor inhibitor Pexidartinib prior to intrafemoral (IF) or subcutaneous 4T1 carcinoma cell inoculation. Spontaneous and evoked nociceptive responses were quantitated throughout tumor development, and contralateral hippocampi were collected via endpoint microdissection for RNA analysis. Additionally, IF tumor-bearing animals were sacrificed on days 5, 10, 15, and 20 post 4T1 cell inoculation, and brain sections were immunofluorescently stained for Iba1, a marker of activated microglia. RESULTS: Ablation of these neuroimmune cells with the CSF1R inhibitor Pexidartinib delayed the onset and severity of cancer-induced nociceptive behaviors in IF tumor-bearing animals, adding to the body of literature that demonstrates microglial contribution to the development and maintenance of CIP. Furthermore, in untreated IF tumor-bearing mice, nociceptive behaviors appeared to progress in parallel with microglial activation in hippocampal regions. Immunofluorescent Iba1+ microglia increased in the dentate gyrus and cornu ammonis 1 hippocampal regions in IF tumor-bearing animals over time, which was confirmed at the mRNA level using relevant microglial markers. CONCLUSION: This is the first experimental evidence to demonstrate the effects of peripheral tumor-induced nociception on hippocampal microglial activation. The increase in hippocampal microglia observed in the present study may reflect the emotional and cognitive deficits reported by patients with CIP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".