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Mitochondria and the aging heart

2011· article· en· W150257603 on OpenAlexafffund
Ketul R. Chaudhary, El-Sikhry Haitham, Seubert John M

Bibliographic record

VenueJournal of Geriatric Cardiology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche MédicaleHeart and Stroke Foundation of Canada
KeywordsMedicineDiseaseHeart diseaseGerontologyAffect (linguistics)MitochondrionLife spanVulnerability (computing)AgeingPhysiologyBioinformaticsNeurosciencePathologyInternal medicineBiologyCell biologyPsychology

Abstract

fetched live from OpenAlex

The average human life span has markedly increased in modern society largely attributed to advances in medical and therapeutic sciences that have successfully reduced important health risks. However, advanced age results in numerous alterations to cellular and subcellular components that can impact the overall health and function of an individual. Not surprisingly, advanced age is a major risk factor for the development of heart disease in which elderly populations observe increased morbidity and mortality. Even healthy individuals that appear to have normal heart function under resting conditions, actually have an increased susceptibility and vulnerability to stress. This is confounded by the impact that stress and disease can have over time to both the heart and vessels. Although, there is a rapidly growing body of literature investigating the effects of aging on the heart and how age-related alterations affect cardiac function, the biology of aging and underlying mechanisms remain unclear. In this review, we summarize effects of aging on the heart and discuss potential theories of cellular aging with special emphasis on mitochondrial dysfunction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.236

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 head0.012
GPT teacher head0.224
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 designNot applicable
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

Citations59
Published2011
Admission routes2
Has abstractyes

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