Glycol-Chitosan-Based Technetium-99m-Loaded Multifunctional Nanomicelles: Synthesis, Evaluation, and In Vivo Biodistribution
Bibliographic record
Abstract
We hereby propose the use of stable, biocompatible, and uniformly sized polymeric micelles as high-radiotracer-payload carriers at region-of-interest with negligible background activity due to no or low offsite radiolysis. We modified glycol chitosan (GC) polymer with varying levels of palmitoylation (P) and quaternization (Q). Quaternary ammonium palmitoyl glycol chitosan (GCPQ) with a Q:P ratio of 9:35 (Q9P35GC) offers >99% biocompatibility at 10 mg mL−1. Q9P35GC micelles exhibit >99% 99mTechnetium (99mTc) radiolabeling via the stannous chloride reduction method without heat. The 99mTc-Q9P35GC micelles (65 ± 3 nm) exhibit >98% 6 h serum stability at 37 °C and 7 day of radiochemical stability at 25 °C. HepG2 cells show a higher uptake of FITC-Q9P35GC than Q13P15GC and Q20P15GC. The in vivo 24 h organ cumulated activity (MBq h) order follows: liver (234.4) > kidneys (60.95) > GIT (0.73) > spleen (88.84). The liver to organ ratio remains higher than 2.4, rendering a better contrast in the liver. The radiotracer uptake decreases significantly in fibrotic vs. normal liver, whereas a blocking study with excess Q9P35GC significantly decreases the radiotracer uptake in a healthy vs. fibrotic liver. FITC-Q9P35GC shows in vivo hepato-specific uptake. Radiotracer liver uptake profile follows reversible binding kinetics with data fitting to two-tissue compartmental (2T), and graphical Ichise multilinear analysis (MA2) with lower AIC and higher R2 values, respectively. The study concludes that 99mTc-Q9P35GC can be a robust radiotracer for noninvasive hepatocyte function assessment and diagnosis of liver fibrosis. Furthermore, its multifunctional properties enable it to be a promising platform for nanotheranostic applications.
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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".