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Record W3196574919 · doi:10.3390/nu13093144

The Effects of Aerobic-Resistance Training and Broccoli Supplementation on Plasma Dectin-1 and Insulin Resistance in Males with Type 2 Diabetes

2021· article· en· W3196574919 on OpenAlexaff
Ayoub Saeidi, Mohammad Soltani, Ali Daraei, Hanieh Nohbaradar, Marjan Mosalman Haghighi, Nikoo Khosravi, Kelly E. Johnson, Ismail Laher, Anthony C. Hackney, Trisha A. VanDusseldorp, Hassane Zouhal

Bibliographic record

VenueNutrients · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsInsulin resistancePlaceboMedicineType 2 diabetesDiabetes mellitusInternal medicineResistance trainingInsulinEndocrinology

Abstract

fetched live from OpenAlex

Background: This study aimed to evaluate the effects of a combination of aerobic-resistance training (CARET) and broccoli supplementation on dectin-1 levels and insulin resistance in men with type 2 diabetes mellitus (T2D). Methods: Forty-four males with T2D were randomly allocated to four groups (n = 11 each group): CARET + broccoli supplement (TS), CARET + placebo (TP), control + broccoli supplement (S), and control + placebo (CP). CARET was performed three days per week for 12 weeks. TS and S groups received 10 g of broccoli supplement per day for 12 weeks. All variables were assessed at baseline and 12 weeks. Results: Plasma dectin-1 levels were decreased in TS and TP groups compared with the CP group (p < 0.05). Cardiometabolic risk factors showed significant reductions in TP and TS groups compared to S and CP groups (p < 0.05). Conclusion: The combination of CARET and broccoli supplementation produced the largest improvements in insulin resistance and dectin-1 and other complications of T2D.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations33
Published2021
Admission routes1
Has abstractyes

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