“Two birds with one stone” strategy for the lung cancer therapy with bioinspired AIE aggregates
Bibliographic record
Abstract
Abstract Aggregation-induced emission luminogens (AIEgens) have emerged as novel phototherapeutic agents with high photostability and excellent performance to induce photodynamic and/or photothermal effects. In this study, a zwitterion-type NIR AIEgens C 41 H 37 N 2 O 3 S 2 (named BITT) with biomimetic modification was utilized for lung cancer therapy. The tumor-associated macrophage (TAM)-specific peptide (CRV) was engineered into the lung cancer cell-derived exosomes. The CRV-engineered exosome membranes (CRV-EM) were obtained to camouflage the BITT nanoparticles (CEB), which targeted both lung cancer cells and TAMs through homotypic targeting and TAM-specific peptide, respectively. The camouflage with CRV-EM ameliorated the surface function of BITT nanoparticles, which facilitated the cellular uptake in both cell lines and induced significant cell death in the presence of laser irradiations in vitro and in vivo. CEB showed improved circulation lifetime and accumulations in the tumor tissues in vivo, which induced efficient photodynamic and photothermal therapy. In addition, CEB induced the tumor microenvironment remodeling as indicated by the increase of CD8 + and CD4 + T cells, as well as a decrease of M2 TAM and Myeloid-derived suppressor cells (MDSCs). Our work developed a novel style of bioinspired AIE aggregates, which could eliminate both lung cancer cells and TAMs, and remodel the tumor environments to achieve an efficient lung cancer therapy. To the best of our knowledge, we are the first to use this style of bioinspired AIE aggregates for photo-mediated immunotherapy in lung cancer therapy.
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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) | 0.000 | 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".