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Dietary supplement combination reduces inflammation and improves vigor in stressed subjects

2010· article· en· W2266506365 on OpenAlexaff
Shawn Talbott, Julie Talbott, Mark Vosti, Jeannie Anderson

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsPlaceboEstroneMedicineMoodInternal medicineTestosterone (patch)EstriolInflammationHormoneEndocrinologyPhysiology

Abstract

fetched live from OpenAlex

Objective The purpose of this study was to evaluate the combined effects of two commercially available dietary supplements on measures of Metabolic Balance, Inflammation, and Psychological Mood State. The supplements are (1) a whole Mangosteen fruit/rind juice (Xango ™ ), and (2) an herbal blend containing Citrus sinensis , Eurycoma longifolia , Camelia sinensis , and L‐theanine (Eleviv ™ ). Previous studies have shown benefits on measures of inflammation (Xango) and hormone balance and mood (Eleviv). Methods We recruited 30 moderately stressed subjects (22 women/8 men) for this 4‐week placebo‐controlled, double‐blind study. Each participant was randomly assigned to consume 6oz/d of the Juice/Placebo (3oz AM/3oz PM) and 2 capsules/d of the Herbal/Placebo (AM). Results There were no significant differences on measures of female reproductive hormones (Progesterone, Estradiol, Estriol, Estrone, 2‐hydroxyestrone, 16‐alpha‐hydroxysterone, and Estrone Sulfate). Significant differences between groups were found for measures of Inflammation (−59% C‐Reactive Protein); Metabolic Balance (+421% ratio between Free Androgen Index/24h Cortisol exposure), Vigor (+27%), and Tension (−34%). Conclusion These data provide clear evidence that consuming the supplements together for 4 weeks provides a range of metabolic benefits. This study was conducted by SupplementWatch and funded by Xango, LLC.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.252

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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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