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Record W3085288157 · doi:10.1039/d0fo01912g

Two polyphenol-rich Brazilian fruit extracts protect from diet-induced obesity and hepatic steatosis in mice

2020· article· en· W3085288157 on OpenAlexafffund
Cíntia Reis Ballard, Elisvânia Freitas dos Santos, Marie-Julie Dubois, Geneviève Pilon, Cínthia Baú Betim Cazarin, Mário Roberto Maróstica, André Marette

Bibliographic record

VenueFood & Function · 2020
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchConselho Nacional de Desenvolvimento Científico e TecnológicoCYTED Ciencia y Tecnología para el DesarrolloCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSteatosisPolyphenolObesityLiver steatosisFood scienceFatty liverBiologyTraditional medicineMedicineBiochemistryInternal medicineEndocrinologyAntioxidant

Abstract

fetched live from OpenAlex

Consumption of polyphenol-rich food is associated with better metabolic health. Tucum-do-Pantanal (Bactris setosa Mart) and taruma-do-cerrado (Vitex cymosa Bertero ex Spreng) are underexploited native Brazilian fruits with an important source of phytochemicals. In this study, we assessed the effects of 100 mg kg-1 tucum (TPE) and taruma (TCE) extracts on diet-induced obesity (DIO) C57BL/6J mice. After 8 weeks of daily treatment, TPE and TCE were found to significantly prevented the diet-induced body weight gain and fully protected against hepatic steatosis associated with a tendency to stimulate hepatic AMPK phosphorylation. TPE reduced visceral obesity and improved glucose metabolism as revealed by an improvement of the insulin tolerance test, a reduction in the insulin fasting level, and a decreased glucose-induced hyperinsulinemia during an oral glucose tolerance test. TPE and TCE showed promising effects on the treatment of obesity and NAFLD, furthermore, TPE on insulin resistance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.718

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 head0.031
GPT teacher head0.255
Teacher spread0.224 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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