Liposome-Templated Indocyanine Green J- Aggregates for <i>In Vivo</i> Near Infrared Imaging and Stable Photothermal Heating
Bibliographic record
Abstract
Indocyanine green (ICG) is an FDA-approved near-infrared fluorescent dye that has been used in optical imaging and photothermal therapy. Its rapid in vivo clearance and photo-degradation have limited its application. ICG pharmacokinetics and biodistribution have been improved via liposomal encapsulation, while its photothermal stability has been enhanced by ICG J-aggregate (IJA) formation. In the present work, we report a simple approach to engineer a nano-sized, highly stable IJA liposomal formulation. Our results showed that lipid film hydration and extrusion method led to efficient IJA formation in rigid DSPC liposomes, as supported by molecular dynamics modeling. The engineered DSPC-IJA formulation was nano-sized, and with spectroscopic and photothermal properties comparable to free IJA. Promisingly, DSPC-IJA exhibited high fluorescence, which enabled its in vivo tracking, showing prolonged blood circulation and significantly higher tumor fluorescence signals, compared to free ICG and IJA. Furthermore, DSPC-IJA demonstrated high photo-stability in vivo after multiple cycles of 808 nm laser irradiation. Finally, doxorubicin was loaded into liposomal IJA to utilize the co-delivery capabilities of liposomes. In conclusion, with both liposomes and ICG being clinically approved, our novel liposomal IJA could offer a clinically relevant theranostic platform enabling multimodal imaging and combinatory chemo-and photothermal 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) | 0.001 | 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".