Therapeutic Effects of ADU-S100 as STING Agonist and CpG ODN1826 as TLR9 Agonist in CT-26 Model of Colon Carcinoma
Bibliographic record
Abstract
Cancer immunotherapy emerged as a novel therapeutic approach to destroy tumor cells, and it has grown toward clinical transition following successful fundamental research and clinical trials. Immunotherapy by efficacious adjuvants is critical for increasing protective immune responses against infectious diseases and cancers. STING and TLR9 agonists are interesting candidates for novel immunotherapies of cancers. In this study, the antitumoral effects of ADU-S100, as a potent STING agonist, and CpG ODN1826, as a TLR9 agonist, in single and combined forms in CT-26 colon adenocarcinoma model were evaluated. This model was induced in female BALB/c mice which were divided into five groups treated with PBS, ADU-S100 (20 and 40 µg), CpG ODN (40 µg), and ADU-S100 (20 µg)+CpG ODN (20 µg). The tumor volumes and weights of mice were measured every other day. On the 30th day, the tumor, spleen, and liver tissues of mice were isolated for histopathological assessment. Hematological analysis was performed on heart blood. Intratumoral injection of agonists induced significant tumor suppression in all treatment groups with profound effect in the combination group that received half concentration of single form. Moreover, the histopathological analysis of tumor tissues showed the presence of apoptotic and inflammatory cells and increased the number of lymphocytes in the blood samples of the treatment groups indicating the effective role of these agonists in clearing the tumor. Therefore, a such synergy of adjuvants may have an effective role in cancer immunotherapy and offer new perspectives on the combination of agonists that trigger innate immune sensors during malignancy.
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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".