Liposomal antibiotics for the treatment of infectious diseases
Bibliographic record
Abstract
INTRODUCTION: Liposomal delivery systems have been utilized in developing effective therapeutics against cancer and targeting microorganisms in and out of host cells and within biofilm community. The most attractive feature of liposome-based drugs are enhancing therapeutic index of the new or existing drugs while minimizing their adverse effects. AREAS COVERED: This communication provides an overview on several aspects of liposomal antibiotics including the most widely used preparation techniques for encapsulating different agents and the most important characteristic parameters applied for examining shape, size and stability of the spherical vesicles. In addition, the routes of administration, liposome-cell interactions and host parameters affecting the biodistribution of liposomes are highlighted. EXPERT OPINION: Liposomes are safe and suitable for delivery of variety of molecules and drugs in biomedical research and medicine. They are known to improve the therapeutic index of encapsulated agents and reduce drug toxicity. Recent studies on liposomal formulation of chemotherapeutic and bioactive agents and their targeted delivery show liposomal antibiotics potential in the treatment of microbial infections.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".