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Record W4409944718 · doi:10.1080/02652048.2025.2495290

Recent developments in sustained-release and targeted drug delivery applications of solid lipid nanoparticles

2025· review· en· W4409944718 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Microencapsulation · 2025
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsSolid lipid nanoparticleDrug deliveryNanoparticleDrugNanotechnologyMaterials scienceTargeted drug deliveryDrug carrierPharmacologyMedicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.279
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it