Pyridinium Rotor Strategy toward a Robust Photothermal Agent for STING Activation and Multimodal Image-Guided Immunotherapy for Triple-Negative Breast Cancer
Bibliographic record
Abstract
The immunosuppressive tumor microenvironment in triple-negative breast cancer could hinder the response to thorough immunotherapy and diminish the antitumor efficacy. Although the STING pathway emerges as a promising target to remedy defects, uncertain drug delivery might lead to off-target inflammatory reactions. Here, we manifest a novel phototheranostic agent with an aggregation-induced emission property that guided the pharmacological activation of a STING agonist for photothermal immunotherapy to create an immunologically "hot" tumor. A pyridinium rotor strategy is proposed to develop a positively charged TBTP-Bz, which is stably coincorporated with a STING agonist MSA-2 into thermal-responsive exosome-liposome hybrid nanoparticles for tumor-targeting delivery. TBTP-Bz exhibits aggregation-enhanced NIR-II emission and a photoacoustic signal, accomplishing real-time tumor tracking. Its photothermal stimulation induces immunogenic cancer cell death and promotes the precise release of MSA-2, thus boosting STING activation and STING-mediated type I interferon production. Significantly, single-dose photoimmunotherapy effectively suppresses abscopal tumor growth and provokes an immune memory effect to inhibit postsurgical recurrent and rechallenged tumors. This demonstrates promising clinical potential for poorly immunogenic breast cancer.
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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".