Liposomes for Intra-Articular Analgesic Drug Delivery in Orthopedics: State-of-Art and Future Perspectives. Insights from a Systematic Mini-Review of the Literature
Bibliographic record
Abstract
Background and objectives: Liposomal structures are artificial vesicles composed of one or several lamellae of phospholipids which surround an inner aqueous core. Given the amphoteric nature of phospholipids, liposomes are promising systems for drug delivery. The present review provides an updated synthesis of the main techniques for the production of liposomes for orthopedic applications, focusing on the drawbacks of the conventional methods and on the advantages of high pressure techniques. Materials and Methods: Articles published in any language were systematically retrieved from two major electronic scholarly databases (PubMed/MEDLINE and Scopus) up to March 2020. Nine articles were retained based on the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines. Results: Liposome vesicles decrease the rate of inflammatory reactions after local injections, and significantly enhance the clinical effectiveness of anti-inflammatory agents providing controlled drug release, reducing toxic side effects. Conclusions: This review presents an update on the improvement in musculoskeletal ailments using liposome treatment.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".