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Record W2335925323 · doi:10.3109/10717544.2016.1143058

Gastroretentive systems – a proposed strategy to modulate anthocyanin release and absorption for the management of diabetes

2016· article· en· W2335925323 on OpenAlexaff
Giovana B. Celli, Wilhelmina Kalt, Marianne Su‐Ling Brooks

Bibliographic record

VenueDrug Delivery · 2016
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsAgriculture and Agri-Food CanadaDalhousie University
Fundersnot available
KeywordsAbsorption (acoustics)Biochemical engineeringComputer scienceMaterials sciencePharmacologyBiomedical engineeringNanotechnologyMedicineEngineering

Abstract

fetched live from OpenAlex

Several reports have indicated a positive correlation between the consumption of anthocyanins (ACN) and biomarkers relating to the improvement of type 2 diabetes (T2D). However, the results from in vitro studies often do not translate into clinical evidence. Potential causes of these discrepancies are experimental conditions that lack physiological relevancy; extensive degradation of these compounds in vivo due to changes in pH and metabolism; and a short residence time in the absorption window in relation to the absorption rate. Here, gastroretentive systems (GRS) are proposed as a strategy to overcome the limitations in ACN delivery and to reduce the existing bench-to-subject gap. This review summarizes recent literature on the use of ACN for the management and control of T2D, followed by GRS platforms to promote a sustained release of ACN for increased health benefits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2016
Admission routes1
Has abstractyes

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