Recent developments in sustained-release and targeted drug delivery applications of solid lipid nanoparticles
Bibliographic record
Abstract
Solid Lipid Nanoparticles (SLNs) are versatile nano-carriers for wide range of applications. The advantages of SLNs include ease of preparation, low toxicity, high active compound bioavailability, flexibility of incorporating hydrophilic and lipophilic drugs, and feasibility of large-scale production. This review provides an overview on the preparation methods of the SLNs, the micro and nanostructure characteristics of the SLNs, and the different factors influencing sustained release and targeted drug delivery. The applications in agriculture and environment, cosmetics, wound healing, malarial treatment, gene therapy and nano-vaccines, and cancer therapy, are elaborated. The mechanisms such as passive, active, and co-delivery are discussed. The issues, challenges and the way forward with ionisable SLNs for delivery of gene and vaccines, RAS-targeted therapy, and bioactive compounds, are highlighted. In combination with multiple compounds and the potential for integration with nature/bio-based solutions, SLNs are proven to be effective, and practical for diverse applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".