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Record W2039732615 · doi:10.1021/jf100135h

Curcumin Nanoparticles Improve the Physicochemical Properties of Curcumin and Effectively Enhance Its Antioxidant and Antihepatoma Activities

2010· article· en· W2039732615 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 Agricultural and Food Chemistry · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCurcumin's Biomedical Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCurcuminAntioxidantNanoparticleChemistryNanotechnologyBiochemistryMaterials science

Abstract

fetched live from OpenAlex

Curcumin (CUR), a natural polyphenol isolated from tumeric ( Curcuma longa ), has been documented to possess antioxidant and anticancer activities. Unfortunately, the compound has poor aqueous solubility, which results in poor bioavailability following high doses by oral administration. To improve the solubility of CUR, we developed a novel curcumin nanoparticle system (CURN) and investigated its physicochemical properties as well as its enhanced dissolution mechanism. Our results indicated that CURN improved the physicochemical properties of CUR, including a reduction in particle size and the formation of an amorphous state with hydrogen bonding, both of which increased the drug release of the compound. Moreover, in vitro studies indicated that CURN significantly enhanced the antioxidant and antihepatoma activities of CUR (P < 0.05). Consequently, we suggest that CURN can be used to reduce the dosage of CUR and improve its bioavailability and merits further investigation for therapeutic 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)0.000
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 score0.016
Threshold uncertainty score0.300

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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