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Record W2093646723 · doi:10.5402/2011/519371

Berberine Improves Glucose Homeostasis in Streptozotocin-Induced Diabetic Rats in Association with Multiple Factors of Insulin Resistance

2011· article· en· W2093646723 on OpenAlexafffund
Yanfeng Chen, Yanwen Wang, Junzeng Zhang, Changhao Sun, Alfonso López

Bibliographic record

VenueISRN Endocrinology · 2011
Typearticle
Languageen
FieldMedicine
TopicBerberine and alkaloids research
Canadian institutionsUniversity of Prince Edward IslandNational Research Council Canada
FundersNational Research Council Canada
KeywordsBerberineInternal medicineEndocrinologyInsulin resistanceStreptozotocinDiabetes mellitusGlucose homeostasisInsulinHomeostasisGlucose tolerance testMedicineChemistryPharmacology

Abstract

fetched live from OpenAlex

The present study was carried out to determine the effect of berberine on glucose homeostasis and several biomarkers associated with insulin sensitivity in male Wistar rats with intraperitoneal injection of streptozotocin (STZ)-induced diabetes. Rats with fasting blood glucose 16.7 mmol/L after 2 weeks of STZ injection were divided into two groups. One group was used as the diabetic control and another treated by gavage feeding with 100 mg/kg/d of berberine in water containing 0.5% carboxymethyl cellulose. A group of rats without receiving STZ was used as the normal control. After 7 weeks, berberine supplementation moderately but significantly lowered fasting blood glucose levels and improved oral glucose tolerance. Berberine lowered plasma free fatty acids and C-reactive protein levels without affecting plasma insulin levels. Diabetic rats treated with berberine showed significantly lower plasma triacylglycerol and cholesterol levels. Furthermore, berberine inhibited dipeptidyl peptidase-4 and protein tyrosine phosphatase-1B activities. In conclusion, berberine showed a dramatic effect of lowering blood cholesterol and triacylglycerols and improved moderately glucose homeostasis in STZ-induced diabetic rats in association with multiple factors related to 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.279
Teacher spread0.246 · 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

Citations57
Published2011
Admission routes2
Has abstractyes

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