Immunomodulatory and anticancer effects of intra-tumoral co-delivery of synthetic lipid A adjuvant and STAT3 inhibitor, JSI-124
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The efficiency of cancer immunotherapy strategies is hampered by the existence of an intra-tumoral immunosuppressive environment involving tolerogenic dendritic cells (DCs) and regulatory T (T(reg)) cells. Hyperactivation of STAT3 in tumor is implicated in the generation of this immunosuppressive environment. The purpose of this study was to test whether simultaneous inhibition of STAT3 in tumor and TLR4 ligand-induced activation of DCs can modulate tumor-induced immunosuppression. For this purpose, the effects of a TLR4 ligand, 7-acyl lipid A, delivered by poly(lactic-co-glycolic acid) nanoparticles (PLGA-NPs) to DCs on the activity of DCs and T(reg) cells was evaluated in vitro. In addition the immunomodulatory and anticancer effects of 7-acyl lipid A PLGA-NPs in combination with a STAT3 inhibitory agent, JSI-124, in a B16 mouse melanoma model was explored, in vivo. PLGA-NP delivery of 7-acyl lipid A to DCs reduced the suppressive effects of T(reg) cells on T cells in vitro. Besides, daily Intra-tumoral co-administration of 7-acyl lipid A PLGA-NPs and JSI-124 in C57BL/6 mice bearing B16-F10 tumor for 8 days resulted in a significant increase in the percentage of tumor infiltrated T cells as compared with control group that received PBS and monotherapy groups. The average tumor volume in the tumor-bearing mice that received JSI-124 plus 7-acyl lipid A PLGA-NPs combination therapy was found to be significantly lower than that in PBS and monotherapy groups. Our findings show a potential for the combination of STAT3 inhibition in tumor and TLR4 induced DC activation in increasing the efficacy of cancer immunotherapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it