Bibliographic record
Abstract
Liposomal nanoparticles (LNs) encapsulating therapeutic agents, or liposomal nanomedicines, represent an advanced class of drug delivery systems, with several formulations presently on the market and many more in clinical trials. Over the past 20 years, a variety of techniques have been developed for encapsulating both conventional drugs (such as anticancer drugs and antibiotics) and the new genetic drugs (plasmid DNA containing therapeutic genes, antisense oligonucleotides and small interfering RNA) within LNs. If the LNs possess certain properties, they tend to accumulate at sites of disease, such as tumours, where the endothelial layer is 'leaky' and allows extravasation of particles with small diameters. These properties include a diameter centred on 100 nm, a high drug-to-lipid ratio, excellent retention of the encapsulated drug, and a long (> 6 h) circulation lifetime. These properties permit the LNs to protect their contents during circulation, prevent contact with healthy tissues, and accumulate at sites of disease. The authors discuss recent advances in this field involving conventional anticancer drugs, as well as applications involving gene delivery, stimulation of the immune system and silencing of unwanted gene expression. Liposomal nanomedicines have the potential to offer new treatments in such areas as cancer therapy, vaccine development and cholesterol management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".