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Record W7104017478 · doi:10.5281/zenodo.17528400

Design and assessment of nanostructured lipid carriers for topical application of Cimicoxib

2025· article· en· W7104017478 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsCollège Shawinigan
Fundersnot available
KeywordsPulmonary surfactantZeta potentialDrugSonicationEntrapmentParticle sizeDrug carrierDrug delivery

Abstract

fetched live from OpenAlex

Cimicoxib-loaded Nanostructured Lipid Carriers (NLCs) using the ultrasonication method highlights the effectiveness of this technique in producing stable and efficient drug delivery systems. The optimized lipid and surfactant composition played a vital role in achieving high drug entrapment and uniform carrier formation, consistent with recent findings that emphasize the importance of formulation parameters in NLC development. The in vitro release profile demonstrated an initial burst followed by sustained release, characteristic of NLCs with drug encapsulated within the lipid matrix. The higher release at pH 7.4 compared to pH 5.5 suggests that the drug release is influenced by the environmental pH, which could be leveraged for targeted delivery. These release kinetics fitting the Higuchi model indicate a diffusion-controlled mechanism, as corroborated by similar studies on lipid-based nanocarriers. The statistical analysis confirmed the significance of the observed differences in particle size, zeta potential, entrapment efficiency, and drug release profiles, supporting the reproducibility and reliability of the formulation process.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.798

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)0.0010.000

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 head0.082
GPT teacher head0.406
Teacher spread0.324 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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