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Record W2028895269 · doi:10.1155/2012/847167

Liuwei Dihuang Lowers Body Weight and Improves Insulin and Leptin Sensitivity in Obese Rats

2011· article· en· W2028895269 on OpenAlexafffund
Benjamin Perry, Junzeng Zhang, Changhao Sun, Tarek M. Saleh, Yanwen Wang

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2011
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsNational Research Council CanadaUniversity of Prince Edward Island
FundersCanadian Institutes of Health ResearchHealth CanadaAtlantic Veterinary College
KeywordsNEFAInternal medicineEndocrinologyLeptinMedicineObesityOverweightTriglycerideDoseInsulinAdverse effectInsulin resistanceCholesterol

Abstract

fetched live from OpenAlex

The present study was aimed at investigating the efficacy and mechanism(s) of action of a Chinese herbal formulation, Liuwei Dihuang (LWDH), as a prospective natural weight-lowering product. Following a 2-week acclimation period, 48 obesity-prone (OP-CD) rats were divided into 4 groups (n = 12 each). One group served as a positive control for obesity (OP), while the other 3 were challenged twice daily by oral gavage with total daily dosages of 500, 1500, or 3500 mg/kg BW LWDH, respectively, for 10 weeks. One group (n = 12) of obesity-resistant (OR-CD) rats served as the normal control group. All rats were fed the same AIN-93G diet modified to contain 60% energy from fat. The highest LWDH dose significantly reduced body weight during the last 4 weeks of treatment. Food intake was reduced beginning in week 2. The high LWDH dose lowered serum triglyceride (TG) and nonesterified fatty acid (NEFA) levels and body fat. Both the high and medium doses also lowered serum leptin and insulin levels. Liver function testing revealed no adverse side effects under the current experimental conditions. The results of the present study suggest that LWDH has potential as a preventive or therapeutic natural product against overweight and obesity.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.092
GPT teacher head0.306
Teacher spread0.213 · 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

Citations22
Published2011
Admission routes2
Has abstractyes

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Same venueEvidence-based Complementary and Alternative MedicineSame topicRegulation of Appetite and ObesityFrench-language works237,207