Synthesis and Evaluation of a Stable Bacteriochlorophyll-Analog and Its Incorporation into High-Density Lipoprotein Nanoparticles for Tumor Imaging
Why this work is in the frame
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Bibliographic record
Abstract
The syntheses of novel near-infrared (NIR) dyes with excellent optical properties in biological tissues have driven the continued improvement of fluorescence imaging of deeply seated tumors. Bacteriochlorophyll a (Bchl), a dye synthesized by the phototrophic bacteria, R. sphaeroids, is particularly suited for deep tissue imaging due to its high absorbance coefficient and good fluorescence quantum yield in the NIR spectrum. However, obstacles that impede the development of this fluorophore are its poor stability and lack of tumor specificity. These issues ultimately limit its utility for tumor detection. Herein we describe a robust synthesis of a novel Bchl analog, bacteriochlorin e(6) bisoleate (BchlBOA), which is chemically stable, has excellent photophysical properties (ex, 752 nm; em, 762 nm) and is tailored for the incorporation into a tumor targetable high-density lipoprotein (HDL)-like nanoparticle (NP). Incorporating BchlBOA into HDL (HDL-BchlBOA) yielded 12 nm sized particles, corresponding well with the diameter of native HDL. Functional cell uptake studies showed that HDL-BchlBOA was taken up by cells expressing the HDL receptor, scavenger receptor B type I (SR-BI), and was inhibited by 25-fold excess native HDL. Furthermore, the NP was successfully detected in KB cancer cells both in vitro and in tumor xenografts. Taken together, these results demonstrate that we successfully synthesized and formulated a stable analog of Bchl that is capable of being incorporated within HDL-like NPs for tumor-targeted imaging.
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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 | 0.000 | 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 it