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Record W4386018672 · doi:10.1016/j.jff.2023.105635

Supplementation of cyanidin-3-O-β-glucoside-rich haskap (Lonicera caerulea L.) berry extract attenuates hepatic lipid dysregulation in diet-induced obese mice

2023· article· en· W4386018672 on OpenAlexafffund
Dipsikha Biswas, A.B.K.H. De Silva, Angella Mercer, Shreya Sarkar, Petra C. Kienesberger, Morgan G. I. Langille, H.P. Vasantha Rupasinghe, Thomas Pulinilkunnil

Bibliographic record

VenueJournal of Functional Foods · 2023
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDiabetes CanadaFondation de la recherche en santé du Nouveau-Brunswick
KeywordsBerryEndocrinologyInternal medicineObesityChemistryGut floraAnthocyaninBiologyFood scienceBiochemistryMedicineBotany

Abstract

fetched live from OpenAlex

Haskap (Lonicera caerulea L.) berry is enriched in anthocyanins, primarily cyanidin-3-O-β-glucoside (C3G). It remains unknown whether C3G counteracts metabolic alterations of the pathogenesis of obesity. In this study, mice were fed high-fat high-sucrose (HFHS) diet supplemented either with C3G-rich extract (HFHS + CE) or berry powder with low C3G (HFHS + BP). Mice fed HFHS + CE displayed short-term protection against weight gain, independent of food intake. HFHS + CE mice had lower hepatic diacylglycerols and triacylglycerols content and reduced expression of key lipogenic transcription factors. These metabolic changes also translated into improved glucose tolerance and insulin sensitivity. 16S rRNA sequencing revealed altered gut microbiota composition in the HFHS + CE group. In summary, we demonstrate that C3G enrichment in the HFHS diet attenuates short-term weight gain, decreases hepatic lipid content by suppressing key lipogenic gene expression and improves glucose homeostasis during obesity development, supporting the therapeutic utility of C3G as a bioactive phytonutrient to manage obesity-related complications.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.044
GPT teacher head0.312
Teacher spread0.268 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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