Photoacoustic imaging of tumor targeting with riboflavin-functionalized theranostic nanocarriers
Bibliographic record
Abstract
Abstract: Photoacoustic imaging is an emerging method in the molecular imaging field, providing high spatiotemporal resolution and sufficient imaging depths for many clinical applications. Therefore, the aim of this study was to use photoacoustic imaging as a tool to evaluate a riboflavin (RF)-based targeted nanoplatform. RF is internalized by the cells through a specific pathway, and its derivatives were recently shown as promising tumor-targeting vectors for the drug delivery systems. Here, the RF amphiphile synthesized from a PEGylated phospholipid was successfully inserted into a long-circulating liposome formulation labeled with the clinically approved photoacoustic contrast agent – indocyanine green (ICG). The obtained liposomes had a diameter of 124 nm (polydispersity index =0.17) and had a negative zeta potential of –26 mV. Studies in biological phantoms indicated a stable and concentration-dependent photoacoustic signal (Vevo ® LAZR) of the ICG-containing RF-functionalized liposomes. In A431 cells, a high uptake of RF-functionalized liposomes was found and could be blocked competitively. First, studies in mice revealed ~3 times higher photoacoustic signal in subcutaneous A431 tumor xenografts ( P <0.05) after injection of RF-functionalized liposomes compared to control particles. In this context, the application of a spectral unmixing protocol confirmed the initial quantitative data and improved the localization of liposomes in the tumor. In conclusion, the synthesized RF amphiphile leads to efficient liposomal tumor targeting and can be favorably detected by photoacoustic imaging with a perspective of theranostic applications. Keywords: photoacoustic imaging, long-circulating liposomes, active targeting, riboflavin, indocyanine green
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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) | 0.000 | 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 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".