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Record W2173356638 · doi:10.1139/v09-170

NMR imaging study of cross-linked high-amylose starch tablets — The effect of drug loading

2010· article· en· W2173356638 on OpenAlexafffundvenue
Y. J. Wang, François Ravenelle, X. X. Zhu

Bibliographic record

VenueCanadian Journal of Chemistry · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversité du Québec à Montréal
KeywordsChemistrySwellingDrugStarchAmyloseDiffusionChromatographyCrude drugChemical engineeringPharmacologyOrganic chemistry

Abstract

fetched live from OpenAlex

NMR imaging techniques were used to study the effect of drug loading in cross-linked high-amylose starch tablets. The tablets contained acetaminophen with loading levels from 10 to 40 wt%. The absolute amount of the drug released increased with a larger amount of drug loading, but the percentages of drug released had only minor differences for the different tablets, probably due to the rapid formation of a gel layer for all the tablets, which slowed down drug release significantly. The release of drugs from the tablets in all cases is dominated by a diffusion mechanism before the disappearance of the dry core of the tablets. Radial and axial swelling and water uptake were found to increase with the amount of drug loading. The diffusion rates of water were comparable at the initial stage for all the tablets with different loadings, but became faster later for the tablets with higher amounts of drug loading as water diffusion may be facilitated by the hydrophilicity of the drug.

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 score0.462
Threshold uncertainty score0.910

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 head0.004
GPT teacher head0.287
Teacher spread0.284 · 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

Citations12
Published2010
Admission routes3
Has abstractyes

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