Evidence map›Paper›PMID 41048632›Full record

ReviewInternational journal of pharmaceutics: X2025

Advanced microfluidic techniques for the preparation of solid lipid nanoparticles: Innovations and biomedical applications.

Mohammad Javad Javid-Naderi, Seyed Ali Mousavi Shaegh

Abstract readReview
In one paragraph

Review in International journal of pharmaceutics: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mohammad Javad Javid-NaderiLaboratory of Microfluidics and Medical Microsystems, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Seyed Ali Mousavi ShaeghLaboratory of Microfluidics and Medical Microsystems, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Solid lipid nanoparticles (SLNs) represent a promising category of nanocarriers used in medicine and cosmetics, offering enhanced drug protection, controlled release, and targeted delivery for both hydrophilic and lipophilic compounds. Conventional preparation methods, such as high-pressure homogenization and solvent emulsification-evaporation, face several challenges, including increased polydispersity, scaling limitations, and the presence of hazardous residual solvents. Microfluidic technology has emerged as a novel approach for preparing SLNs, addressing issues such as variable particle sizes and residual solvents by facilitating enhanced control over particle dimensions, morphology, and encapsulation efficiency. Microfluidics enables rapid and uniform mixing through micro-scale fluid dynamics, resulting in the production of homogeneous nanoparticles with adjustable characteristics. The review examines key parameters in microfluidic SLN preparation and categorizes various microfluidic chip designs and mixing techniques in detail, illustrating their unique advantages in controlling nanoparticle properties. Furthermore, this article provides a comprehensive overview of microfluidic SLN preparation, emphasizing its advantages over conventional methods, and explores the transformative potential of SLNs for advancing drug delivery systems, cosmetic formulations, and diagnostics. The integration of artificial intelligence (AI) and machine learning to optimize synthesis conditions and enhance reproducibility and scalability for industrial translation are also discussed.

Indexed as

Artificial intelligenceCosmeceutical applicationsDrug delivery systemsIndustrial scale-upMicrofluidicSolid lipid nanoparticles

Identifiers

PMID41048632
PMCPMC12494845

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.