A simple and efficient method for radiolabeling of preformed liposomes.
Bibliographic record
Abstract
A simple and efficient method for radiolabeling preformed liposomes was developed using hepatobiliary imaging agent (99m)Tc-diisopropyl iminodiacetic acid ((99m)Tc-DISIDA). Chloroform extraction of (99m)Tc-DISIDA from aqueous solutions results in 80% radioactivity in the organic phase due to its lipophilic properties. However, with the presence of reduced glutathione (gamma-Glu-Cys-Gly), chloroform extraction results in only 30% of label in the organic phase because the (99m)Tc-DISIDA complex undergoes reduction decomposition to more hydrophilic species by reaction with glutathione. The incorporation efficiency of the (99m)Tc-DISIDA into the liposomes containing reduced glutathione was greater than 90%. The labeled liposomes were stable up to 24 h in saline and 90% FBS after preparation. Biodistribution studies in mice showed that (99m)Tc labeled liposomes accumulated in liver and spleen at 24 h postinjection, unlike (99m)Tc-DISIDA. Compared to hexamethylpropyleneamine oxime (HMPAO), the (99m)Tc-DISIDA compound is much cheaper and has a longer shelf life when used for liposome labeling. The labeling technique described here could be used for monitoring pharmacokinetic and pharmacodynamic changes of liposomes, and tumor or infection imaging when coupled with targeting antibodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".