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Endurance exercise training attenuates fibrosis and collagen cross‐linking in myocardium of aged F344BNF1 rats

2013· article· en· W844948329 on OpenAlexafffund
Kathryn Jean Wright, Andrew C. Betik, Melissa M. Thomas, Russell T. Hepple, Darrell D. Belke

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsMcMaster UniversityMcGill University
FundersCanadian Institutes of Health Research
KeywordsMMP1TIMP1PentosidineFibrosisSenescenceGlycationMatrix metalloproteinaseEndocrinologyInternal medicineCollagenaseMedicineGene expressionEndurance trainingChemistryBiochemistryGeneEnzymeDiabetes mellitus

Abstract

fetched live from OpenAlex

The objective of the study was to determine if endurance exercise training (ET) could attenuate the age‐related increase in fibrosis and collagen cross‐linking in the myocardium of the heart, which is thought to be a cause of the age‐related decline in diastolic function. Fisher 344 × Brown Norway F1 rats underwent ET from late middle age into senescence. Genes for collagen synthesis and degradation were assessed by polymerase chain reaction. Matrix metalloproteinase (MMP) activity was assessed using EnzChek Gelatinase/Collagenase Assay kit, and collagen cross‐linking was determined indirectly by immunohistochemical staining for advanced glycation end‐products. Tissue inhibitor of matrix metalloproteinase‐1 (TIMP1) gene expression, TIMP and MMP1 protein expression, and MMP activity increased with age but with no ET effect. ET attenuated the age‐related increase in fibrosis and collagen cross‐linking, while increasing survival rate. This could have attenuated the age‐related decline in myocardial compliance and in turn have helped attenuate the decrease in diastolic function generally seen with aging. This work was supported by an operating grant from CIHR (MOP 57808).

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)0.001
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 score0.746
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.265
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

Citations0
Published2013
Admission routes2
Has abstractyes

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