Ojeok-san ameliorates visceral and somatic nociception in a mouse model of colitis induced colorectal cancer
Bibliographic record
Abstract
Cancer patients can develop visceral, somatic, and neuropathic pain, largely due to the malignancy itself and its treatments. Often cancer patients and survivors turn to the use of complementary and alternative medicine (CAM) to alleviate pain and fatigue. Thus, it is necessary to investigate how CAM therapies work as novel analgesics to treat cancer pain. Ojeok-san (OJS) is an herbal formula consisting of seventeen herbs. This herbal formula has been shown to possess anti-inflammatory, immunoregulatory, and analgesic properties. In this study, we examined the potential beneficial effects and mechanism of action of OJS in a preclinical model of colitis-associated colorectal cancer. Male and female C57BL/6J mice were exposed to the carcinogen, azoxymethane (AOM, 10 mg/kg) and a chemical inflammatory driver, dextran sulfate sodium (DSS1-2%), to promote tumorigenesis in the colorectum. OJS was given orally (500, 1000, and 2000 mg/kg) to determine its influence on disease activity, tumor burden, nociception, sedation, Erk signaling, and behavioral and metabolic outcomes. In addition, in vitro studies were performed to assess CT-26 cell viability, dorsal root ganglia (DRG) activation, and bone-marrow-derived macrophage (BMDM) inflammatory response to lipopolysaccharide stimulation after OJS treatment. We found that administration of 2000 mg/kg of OJS was able to mitigate mechanical somatic and visceral nociception via Erk signaling without affecting symptom score and polyp number. Moreover, we discovered that OJS has sedative properties and elicits prolonged total sleeping time in AOM/DSS mice. Our in vitro experiments showed that OJS has the capacity to reduce TNFα gene expression in LPS-stimulated BMDM, but no changes were observed in DRG spike number and CT-26 cell proliferation. Taken together, these data suggest that OJS ameliorates nociception in mice and warrants further examination as a potential CAM therapy to promote analgesia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".