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Record W3184749152 · doi:10.20463/pan.2021.0009

Consumption of a high-fat-high-sucrose diet partly diminishes mechanical and structural adaptations of cardiac muscle following resistance training

2021· article· en· W3184749152 on OpenAlexaff
Kevin Boldt, Stela Márcia Mattiello, Venus Joumaa, Jeannine Turnbull, Paul W.M. Fedak, Walter Herzog

Bibliographic record

VenuePhysical Activity and Nutrition · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsResistance trainingMedicineCardiac muscleCalciumMuscle fibreInternal medicineCardiologyEndocrinologyAnimal scienceBiologySkeletal muscle

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to investigate the effects of a high-fat high-sucrose (HFHS) diet on previously reported adaptations of cardiac morphological and contractile properties to resistance training. METHODS: Twelve-week-old rats participated in 12-weeks of resistance exercise training and consumed an HFHS diet. Echocardiography and skinned cardiac muscle fiber bundle testing were performed to determine the structural and mechanical adaptations. RESULTS: Compared to chow-fed sedentary animals, both HFHS- and chow-fed resistance-trained animals had thicker left ventricular walls. Isolated trabecular fiber bundles from chow-fed resistance-trained animals had greater force output, shortening velocities, and calcium sensitivities than those of chow-fed sedentary controls. However, trabeculae from the HFHS resistance-trained animals had greater force output but no change in unloaded shortening velocity or calcium sensitivity than those of the chow-fed sedentary group animals. CONCLUSION: Resistance exercise training led to positive structural and mechanical adaptations of the heart, which were partly offset by the HFHS diet.

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 score0.627
Threshold uncertainty score0.352

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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
Published2021
Admission routes1
Has abstractyes

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