Abstract 1990: Cjc-1134-pc, a long-acting glp-1r agonist, effects in modulating pro-inflammatory cytokines in the tumor environment
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".