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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".