Behavioural Effects of Using Sulfasalazine to Inhibit Glutamate Released by Cancer Cells: A Novel target for Cancer-Induced Depression
Bibliographic record
Abstract
Abstract Despite the lack of robust evidence of effectiveness, current treatment options for cancer-induced depression (CID) are limited to those developed for non-cancer related depression. Here, anhedonia-like and coping behaviours were assessed in female BALB/c mice inoculated with 4T1 mammary carcinoma cells. The behavioural effects of orally administered sulfasalazine (SSZ), a system xc− inhibitor, were compared with fluoxetine (FLX). FLX and SSZ prevented the development of anhedonia-like behaviour on the sucrose preference test (SPT) and passive coping behaviour on the forced swim test (FST). The SSZ metabolites 5-aminosalicylic acid (5-ASA) and sulfapyridine (SP) exerted an effect on the SPT but not on the FST. Although 5-ASA is a known anti-inflammatory agent, neither treatment with SSZ nor 5-ASA/SP prevented tumour-induced increases in serum levels of interleukin-1β (IL-1β) and IL-6, which are indicated in depressive disorders. Thus, the observed antidepressant-like effect of SSZ may primarily be attributable to the intact form of the drug, which inhibits system xc−. This study represents the first attempt at targeting cancer cells as a therapeutic strategy for CID, rather than targeting downstream effects of tumour burden on the central nervous system. In doing so, we have also begun to characterize the molecular pathways of CID.
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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 | 0.001 |
| 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".