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Effects of Long Term Exercise on Age‐related changes in Cardiac Fibrosis and Cell Death Profiles

2013· article· en· W593203097 on OpenAlexaff
Tiffany Akins, Gregory P. Barton, M.M. Hoffman, Judd M. Aiken, Gary M. Diffee

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of Alberta
FundersNational Institutes of Health
KeywordsMedicineEndurance trainingFibrosisInternal medicineEndocrinologyNecrosisTreadmillCardiac function curveCardiologyHeart failure

Abstract

fetched live from OpenAlex

Aging results in changes to cardiac structure and function but the mechanisms underlying these effects are not well understood. Exercise training appears to have some positive effects on age related functional changes, but the effects of exercise on age‐related structural changes have not been well explored. We characterized cardiac remodeling in response to aging and exercise in male FBN rats, starting with rats age 24 mo and continuing until age 36 mo. Rats were divided into 3 groups; high intensity (H), and moderate intensity (M) treadmill exercise, and sedentary (S). M and H animals trained at 13 m/min for 30 min/day, with the H group at 5% incline. At the end of training, hearts were excised, embedded and sectioned. Sections were stained with H&E and Masson's Trichrome. Cardiac sections were analyzed via immunohistochemistry for autophagy (using an anti‐Becilin antibody) and necrosis (using an anti‐C5b9 antibody). Fibrosis deposition significantly increased with age, but was decreased by exercise training. The number of cells positive for C5b9 and Becilin were significantly higher in aged hearts, but this number was decreased with exercise training. These results show that aging increases fibrosis deposition and markers for autophagy and necrosis. Prolonged endurance exercise training into advanced ages appears to minimize a number of these age‐related changes. Supported by: NIH AG030423

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 score0.430
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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 routes1
Has abstractyes

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