Immunogenic exosome-encapsulated black phosphorus nanoparticles as an effective anticancer photo-nanovaccine
Bibliographic record
Abstract
Tumor vaccines are a promising form of cancer immunotherapy, but difficulties such as neo-antigen identification, activation of immune cells, and tumor infiltration prevent their clinical breakthrough. Interestingly, nanotechnology-based photothermal therapy (PTT) has great potential to overcome these barriers. Previous studies have shown that serum exosomes (hEX) from hyperthermia-treated tumor-bearing mice displayed an array of patient-specific tumor-associated antigens (TAAs), and strong immunoregulatory abilities in promoting dendritic cell (DC) differentiation and maturation. Here, we developed a tumor vaccine (hEX@BP) by encapsulating black phosphorus quantum dots (BPQDs) with exosomes (hEX) against a murine subcutaneous lung cancer model. In comparison with BPQDs alone (BP), hEX@BP demonstrated better long-term PTT performance, greater elevation of tumor temperature and tumor targeting efficacy in vivo. Vaccination with hEX@BP in combination with PTT further demonstrated an outstanding therapeutic efficacy against established lung cancer, and promoted the infiltration of T lymphocytes into the tumor tissue. Our findings demonstrated that hEX@BP might be an innovative cancer photo-nanovaccine that offers effective immuno-PTT against cancers.
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) | 0.000 | 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 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".