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Record W1979496891 · doi:10.1002/biof.5

Sarcopenia of aging: Underlying cellular mechanisms and protection by calorie restriction

2009· review· en· W1979496891 on OpenAlexaff
Emanuele Marzetti, Hazel A. Lees, Stephanie E. Wohlgemuth, Christiaan Leeuwenburgh

Bibliographic record

VenueBioFactors · 2009
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsInstitute of Aging
FundersInstitute on Aging, College of Medicine, University of FloridaNational Institute on AgingUniversity of Florida
KeywordsSarcopeniaMitochondrial biogenesisCalorie restrictionAutophagyBiogenesisMitochondrionOxidative stressBiologyAgeingCell biologyMalnutritionBioinformaticsApoptosisEndocrinologyMedicineInternal medicineBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

Sarcopenia, the loss of muscle mass and function, is a common feature of aging and impacts on individual health and quality of life. Several cellular mechanisms have been involved in the pathogenesis of this syndrome, including mitochondrial dysfunction, altered apoptotic and autophagic signaling, and, more recently, trace metal dyshomeostasis. Calorie restriction (CR) without malnutrition has been shown to ameliorate the age-related loss of muscle mass in a variety a species. Mechanisms of protection span from preservation of mitochondrial functional and structural integrity to mitochondrial biogenesis, reduction of oxidative stress, and favorable modulation of apoptotic and autophagic signaling pathways. Importantly, preliminary evidence indicates that moderate CR may promote muscle mitochondrial biogenesis in middle-aged human subjects. Further research is warranted to investigate whether CR may represent a safe and efficient strategy to delay the onset and mitigate the progression of sarcopenia in older adults.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.175
GPT teacher head0.388
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations180
Published2009
Admission routes1
Has abstractyes

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