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Record W2155163916 · doi:10.1111/hdi.12001

Serum coenzyme <scp>Q</scp>10 levels are associated with coronary flow reserve in hemodialysis patients

2012· article· en· W2155163916 on OpenAlexvenueno aff
Beyza Macunluoğlu, Yüksel Kaya, Aydın Atakan, Elif Arı, Çiğdem Kaspar, Halit Demir, Hamit Hakan, Ebru Aşıcıoğlu, Alla Eldeen Kedrah

Bibliographic record

VenueHemodialysis International · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCoenzyme Q10 studies and effects
Canadian institutionsnot available
Fundersnot available
KeywordsCoenzyme Q10MedicineCoronary flow reserveHemodialysisInternal medicineGastroenterologyCardiologyDoppler echocardiographySerum concentrationEndocrinologyUrologyCoronary artery diseaseDiastoleBlood pressure

Abstract

fetched live from OpenAlex

Accelerated atherosclerosis is the major cause of mortality in patients on chronic hemodialysis (HD). The aim of this study was to evaluate the relation between coenzyme Q10 (CoQ10) levels and coronary flow reserve (CFR) in HD patients as an indicator of atherosclerosis. Seventy-one chronic HD patients and 65 age- and sex-matched healthy individuals were included in the study. Plasma CoQ10 levels were performed by high-performance liquid chromatography measurements. CFR was assessed by transthoracic Doppler echocardiography. Serum CoQ10 levels (1.36 ± 0.43 vs. 2.53 ± 0.55, P < 0.001) and CFR values (1.73 ± 0.11 vs. 2.32 ± 0.28, P < 0.001) were significantly lower in HD patients compared with controls. There was a significant positive correlation between CFR and serum levels of CoQ10 (r = 0.669, P < 0.001). A linear regression analysis showed that serum levels of CoQ10 were still significantly and positively correlated with CFR (regression coefficient = 0.235, P < 0.001). Our data have demonstrated that HD patients exhibit decreased plasma CoQ10 levels and CFR values. The study also showed for the first time that serum CoQ10 levels independently predict CFR in HD patients.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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.

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

Citations10
Published2012
Admission routes1
Has abstractyes

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