Oral Presentations—Abstracts
Bibliographic record
Abstract
Targeted Delivery of Antisense Oligodeoxynucleotides In Vivo by Means of Coated Cationic LipoplexesEarlier we reported on the massive uptake of liposomes surface-modified with negatively charged aconitylated albumin (Aco-HSA) by liver endothelial cells (EC) in vivo. In the present work we apply this principle for in vivo delivery of antisense oligodeoxynucleotides (ODN) to these cells by means of coated cationic lipoplexes (CCL) (). CCL were prepared by complexing ODN with the cationic lipid DOTAP and subsequent coating of the complex by neutral lipids including a lipid-anchored poly(ethylene glycol). Aco-HSA was covalently coupled.The Aco-HSA-CCLs were 160 nm in size, contained 1.03 ± 0.35 nmol ODN and 54 ± 18 µg Aco-HSA per µ mol total lipid. The Aco-HSA-CCLs were rapidly eliminated from plasma, 60% of the injected dose being recovered in the liver after 30 m. Within the liver, the EC accounted for two thirds of total liver uptake. Non-targeted CCLs were eliminated very slowly: after 30 m >90% of the particles was in the blood. Currently, we compare the encapsulation efficiency, stability and targetability of the CCL with stabilized antisense lipid particles (SALP) (), while also the biological activity of these carriers is addressed. In conclusion our results demonstrate that antisense ODN can be targeted very efficiently to EC in vivo, employing plasma-stable CCL, surface modified with negatively charged albumin.ReferencesStuart DD, Allen TM. BBA 2000; 1463:219–229.Semple S. et al. BBA 2001; 1510: 152–166.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.721 | 0.523 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".