Engineering a facile and versatile nanoplatform to facilitate the delivery of multiple agents for targeted breast cancer chemo-immunotherapy
Bibliographic record
Abstract
There is growing evidence showing that single administration of immunotherapeutic agents has limited efficacy in a number of cancer patients mainly due to tumor heterogeneity and immunosuppressive tumor microenvironment. In this study, a novel nanoparticle-based strategy was applied to achieve efficient tumor-targeted therapy by combining chemotherapeutic agents, i.e., doxorubicin (Dox) and melittin (Mel), with an immune checkpoint inhibitor (PD-L1 DsiRNA). The proposed nanoparticle was prepared by the formation of a complex between Mel and PD-L1 DsiRNA (Dicer-substrate short-interfering RNA), followed by the loading of Dox. The surface of the resultant particles (DoxMel/PD-L1 DsiRNA) was then modified with hyaluronic acid (HA) to increase their stability and distribution. In addition, HA can also act as a tumor-targeting agent through binding to its receptor CD44 on the surface of cancer cells. We demonstrated that the surface engineering of DoxMel/PD-L1 DsiRNA with HA significantly enhances its specificity towards breast cancer cells. Moreover, we observed a noticeable reduction in PD-L1 expression together with a synergistic effect of Dox and Mel on killing cancer cells and inducing immunogenic cell death, leading to significantly diminished tumor growth in 4T1-breast tumor bearing Balb/c mice, improved survival rate and extensive infiltration of immune cells including cytotoxic T cells into the tumor microenvironment. Safety analysis revealed that there is no significant toxicity associated with the developed nanoparticle. All in all, the proposed targeted combination treatment strategy can be considered as a useful method to reduce cancer-associated mortality.
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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 | 0.000 |
| 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".