Abstract 1990: Cjc-1134-pc, a long-acting glp-1r agonist, effects in modulating pro-inflammatory cytokines in the tumor environment
Bibliographic record
Abstract
Abstract The incidence rates of The incidence rates of diabetes and cancer are growing globally. It is reported that type 2 diabetes (T2D) is associated with increased risk of cancers, which suggests a synergy. There is a strong association among inflammation and cancer on diabetes, which is reflected by the high cytokine prevalence in the microenvironment. Previously our studies showed that IL-2 and IFN-γ were downregulated but IL-6 and TNFα were upregulated in diabetic mice. The levels of IL-2, IFN-γ, IL-6 and TNFα returned to baseline levels similar to that of non-diabetic mice after the treatment of CJC-1134-PC. In order to explore the benefits of CJC-1134-PC beside its glucose lowering effects, the level of pro-inflammatory cytokines has been measured and compared between vehicle and CJC-1134-PC treated BALB/c mice bearing CT26 colon tumors. All mice bearing tumors showed high IL-6 and TNF-r compared with the normal mice. Interestingly, after CJC-1134-PC treatment, IL-6 and TNFα levels decreased significantly (P < 0.05), which makes the cytokine profile close to the reported levels in normal mice. Overall, the data suggests that CJC-1134-PC plays a role in modulating inflammation, which could benefit cancer treatment in addition to achieving glycemic control. Citation Format: Xiping Liu, Sean Hong, Shijuan Wu, Zhiwen Yao, Qiang Xie. Cjc-1134-pc, a long-acting glp-1r agonist, effects in modulating pro-inflammatory cytokines in the tumor environment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1990.
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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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".