Hybrid Quantum Dot−Fatty Ester Stealth Nanoparticles: Toward Clinically Relevant <i>in Vivo</i> Optical Imaging of Deep Tissue
Bibliographic record
Abstract
Despite broad applications of quantum dots (QDs) in vitro, severe toxicity and dominant liver uptake have limited their clinical application. QDs that excite and emit in the ultraviolet and visible regions have limited in vivo applicability due to significant optical interference exerted by biological fluids and tissues. Hence we devised a new biocompatible hybrid fluorophore composed of near-infrared-emitting PbSe quantum dots encapsulated in solid fatty ester nanoparticles (QD-FEN) for in vivo imaging. The quantum yield and tissue penetration depth of the QD-FEN were characterized, and their biological fate was examined in a breast tumor-bearing animal model. It was found for the first time that chemical modification of the headgroup of QD-encapsulating organic fatty acids was a must as these groups quenched the photoluminescence of PbSe nanocrystals. The use of fatty esters enhanced aqueous quantum yields of PbSe QDs up to ∼45%, which was 50% higher than that of water-soluble PbSe nanocrystals in an aqueous medium. As a result, a greater than previously reported tissue penetration depth of fluorescence was recorded at 710 nm/840 nm excitation/emission wavelengths. The QD-FEN had much lower short-term cytotoxicity compared to nonencapsulated water-soluble QDs. More importantly, reduced liver uptake, increased tumor retention, lack of toxic response, and nearly complete clearance of QD-FEN from the tested animals was demonstrated. With a combination of near-infrared spectral properties, enhanced optical properties,and significantly improved biosafety profile, this novel hybrid nanoparticulate fluorophore system demonstrably provides real-time, deep-tissue fluorescent imaging of live animals, laying a foundation for further development toward clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".