Blockage of immune checkpoint molecules increases T‐cell priming potential of dendritic cell vaccine
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
Dendritic cell (DC) -based cancer immunotherapy is one of the most important anti-cancer immunotherapies, and has been associated with variable efficiencies in different cancer types. It is well-known that tumor microenvironment plays a key role in the efficacy of various immunotherapies such as DC vaccine. Accordingly, the expression of programmed death ligand 1 (PD-L1) on DCs, which interacts with PD-1 on T cells, leads to inhibition of anti-tumor responses following presentation of tumor antigens by DCs to T cells. Therefore, we hypothesized that down-regulation of PD-L1 in DCs in association with silencing of PD-1 on T cells may lead to the enhancement of T-cell priming by DCs to have efficient anti-tumor T-cell responses. In this study, we silenced the expression of PD-L1 in DCs and programmed cell death protein 1 (PD-1) in T cells by small interfering RNA (siRNA) -loaded chitosan-dextran sulfate nanoparticles (NPs) and evaluated the DC phenotypic and functional characteristics and T-cell functions following tumor antigen recognition on DCs, ex vivo. Our results showed that synthesized NPs had good physicochemical characteristics (size 77·5 nm and zeta potential of 14·3) that were associated with efficient cellular uptake and target gene silencing. Moreover, PD-L1 silencing was associated with stimulatory characteristics of DCs. On the other hand, presentation of tumor antigens by PD-L1-negative DCs to PD-1-silenced T cells led to induction of potent T-cell responses. Our findings imply that PD-L1-silenced DCs can be considered as a potent immunotherapeutic approach in combination with PD-1-siRNA loaded NPs, however; further in vivo investigation is required in animal models.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it