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Record W2046142166 · doi:10.1155/2010/149678

Effect of Moderate-Intensity Exercise on Plasma C-Reactive Protein and Aortic Endothelial Function in Type 2 Diabetic Mice

2010· article· en· W2046142166 on OpenAlexafffund
Nada A. Sallam, Majid Khazaei, Ismail Laher

Bibliographic record

VenueMediators of Inflammation · 2010
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of British Columbia
FundersHeart and Stroke Foundation of Canada
KeywordsInternal medicineMedicineMyographInflammationC-reactive proteinEndocrinologyType 2 diabetesEndothelial dysfunctionTumor necrosis factor alphaEndotheliumDiabetes mellitus

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the effects of moderate-intensity exercise on plasma levels of C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α) as markers of low-grade inflammation and endothelial function in diabetic (db/db) mice. Control and db/db mice were divided into sedentary and exercised groups. Aortic endothelial function was evaluated after two- and six-week exercises using a wire myograph. Plasma CRP levels were measured at baseline, and after two and six weeks of exercise. Baseline plasma CRP levels were significantly higher in db/db mice compared to control (P < .05). After two weeks of exercise, aortic endothelial function was significantly improved without affecting body weight or plasma CRP levels. Six weeks of exercise not only improved endothelial function, but also significantly reduced body weight and plasma CRP levels in db/db mice. Thus short-term exercise has beneficial effect on endothelial function without reducing low-grade inflammation while more prolonged exercise periods are required to reduce inflammatory markers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.227
Teacher spread0.223 · 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 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

Citations24
Published2010
Admission routes2
Has abstractyes

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