Understanding the formulation parameters for engineering indocyanine green J-aggregate lipid nanocapsules and solid lipid nanoparticles as promising photothermal agents
Bibliographic record
Abstract
Indocyanine green J-aggregate (IJA) is a promising photothermal (PTT) agent that has recently been utilised in preclinical studies for cancer diagnostics and treatment. The unique properties, such as the red-shift absorption band and longer wavelengths, are behind IJA's superior thermal stability compared to its monomeric ICG. Loading IJA into nanoparticles (NPs) has proven advantageous in enhancing its in vivo targeting of various cancer models. However, the loading of IJA into more complex lipids, such as lipid nanocapsules (LNCs) and solid lipid nanoparticles (SLNs), has not been reported. The present work focuses on investigations of the effect of formulation parameters on pre-formed IJA (p-IJA) stability and the formation of p-IJA-loaded LNCs and SLNs, thus enhancing their theranostic applications. We investigated the effect of the lipid shell of LNCs and the lipid core of SLN on p-IJA stability. Our findings demonstrated the significant role of lipophilic surfactants (Span 85) and a high-melting-point lipid core (sodium stearate) in enhancing the p-IJA ratio and heating capacity following loading into SLNs. More importantly, p-IJA-SLN enhanced the optical stability of p-IJA in a range of biological media, such as serum proteins, blood, and collagen. Furthermore, lyophilised p-IJA-SLNs were successfully obtained after long-term storage. Overall, p-IJA-loaded lipid NPs could provide a promising platform for various applications, including photoacoustic imaging, PTT, photodynamic therapy (PDT), and combination therapy with chemotherapeutics.
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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 | 0.001 | 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 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".