Nano-delivery of Silibinin Potentiate the Induction of Immunogenic Cell Death (ICD) in Melanoma Cells
Bibliographic record
Abstract
BACKGROUND: Induction of immunogenic cell death (ICD) in tumors can enhance antitumor immunity and modulate immunosuppression in the tumor microenvironment (TME). OBJECTIVE: In the current study, we investigated the effect of silibinin, a natural compound with anticancer activity, and its polymer-based nanoformulations on the induction of apoptosis and ICD in cancer cells. METHODS: Free and nanoparticulate silibinin were evaluated for their growth-inhibitory effects using an MTT assay. Annexin V/PI staining was used to analyze apoptosis. Calreticulin (CRT) expression was measured by flow cytometry. Western blotting was conducted to examine the levels of elf2α, which plays a role in the ICD pathway. The HSP90 and ATP levels were determined using specific detection kits. RESULTS: Compared to the free drug, silibinin-loaded nanocarriers significantly increased the induction of apoptosis and ICD in B16F10 cells. ICD induction was characterized by significantly increased levels of ICD biomarkers, including CRT, HSP90, and ATP. We also observed an increased expression of p-elf-2α/ elf-2α in B16F10 cells treated with silibinin-loaded micelles compared to cells that received free silibinin. CONCLUSION: Our findings showed that the encapsulation of silibinin in polymeric nanocarriers can potentiate the effects of this drug on the induction of apoptosis and ICD in B16F10 melanoma cells.
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) | 0.000 | 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".