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Record W4407847336 · doi:10.1139/apnm-2024-0458

Carnosine-enriched functional food enhances micro- and macrovascular endothelium-independent vasodilation in competitive athletes—a randomized study

2025· article· en· W4407847336 on OpenAlexvenueno aff
Leon Perić, Ines Drenjančević, Ivana Jukić, Alina Boris, Petar Šušnjara, Nikolina Kolobarić, Zrinka Mihaljević, Zlata Kralik, Gordana Kralik, Manuela Košević, Olivera Galović, Ana Stupin

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2025
Typearticle
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsnot available
Fundersnot available
KeywordsCarnosineVasodilationEndotheliumMedicineBlood pressureInternal medicineMicrocirculationEndocrinologyBrachial arteryCardiology

Abstract

fetched live from OpenAlex

This randomized interventional study aimed to investigate the effect of carnosine-enriched chicken meat consumption on systemic endothelium-dependent and -independent micro- and macrovascular reactivity in thirty-five healthy competitive male athletes. Both forearm skin micro- and macrovascular endothelium-independent vasodilation were increased, and diastolic and mean arterial blood pressure (BP) were decreased in Carnosine group ( n = 19) following the 3-week dietary protocol. Microvascular endothelium-dependent response (post-occlusion reactive hyperemia) was increased in the Carnosine group and significantly associated with decreased mean arterial BP level. Following dietary protocol, Controls ( n = 16) had substantially higher urate (but still normal) levels than the Carnosine group. Carnosine supplementation in the form of functional food enhances endothelium-dependent and vascular smooth muscle-dependent vasodilation in peripheral micro- and microcirculation. Carnosine's effect on vascular endothelium could be attributed to its BP-lowering effect. Results suggest that carnosine has the potential to resist hyperuricemia in healthy individuals. ClinicalTrials.gov (NCT05723939)

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 score0.693
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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