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Record W4387168523 · doi:10.11159/ijtan.2023.001

Development and Evaluation of Lipid-Based Formulations for Liver Cancer: A Comparative Study of Solid Lipid Nanoparticles vs. Nanostructured Lipid Carriers

2023· article· en· W4387168523 on OpenAlexvenueno aff
Mina Gayed, Dina M. Gaber, Nabila Borae

Bibliographic record

VenueInternational Journal of Theoretical and Applied Nanotechnology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsSolid lipid nanoparticleNanotechnologyNanoparticleLiver cancerMaterials scienceChemistryCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

To investigate the influence of the physical state and composition of lipid materials on the preparation performance of lipid nanocarriers, two types of carriers were prepared and compared: solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs).To assess the ability of these nanocarriers to promote effective drug delivery of drug to liver for management of liver cancer, Fenretinide (FEN) was employed as a model drug.The FEN encapsulation efficiency in these NLCS was greater than 97% compared to 93% for SLNs.Further loading capacity increased 2 folds in case of NLCs.Furthermore, the unchanged size and size distribution of these nanoparticles for 3 months at room temperature demonstrated their stability in such conditions.However, MTT experiments proved that FEN-SLNs are more effective for delivering FEN to HepG2 cells than NLCs with 3 folds enhancement in cytotoxic activity.Therefore, this study showed that by optimally controlling the lipid physical state and composition, it is possible to fabricate the solid lipid nanoparticles with desired properties.

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 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.323
Teacher spread0.301 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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