MétaCan
Menu
Back to cohort
Record W2045888813 · doi:10.1210/en.2010-0018

Myocyte Androgen Receptors Increase Metabolic Rate and Improve Body Composition by Reducing Fat Mass

2010· article· en· W2045888813 on OpenAlexafffund
Shannon M. Fernando, Pengcheng Rao, Lee Niel, Diptendu Chatterjee, Marijana Stagljar, D. Ashley Monks

Bibliographic record

VenueEndocrinology · 2010
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Neurological Disorders and StrokeUniversity of Toronto
KeywordsEndocrinologyInternal medicineAndrogen receptorLean body massAndrogenTestosterone (patch)Genetically modified mouseSkeletal muscleMyocyteBiologyTransgeneMuscle hypertrophyReceptorChemistryHormoneMedicineBody weightBiochemistryGene

Abstract

fetched live from OpenAlex

Testosterone and other androgens are thought to increase lean body mass and reduce fat body mass in men by activating the androgen receptor. However, the clinical potential of androgens for improving body composition is hampered by our limited understanding of the tissues and cells that promote such changes. Here we show that selective overexpression of androgen receptor in muscle cells (myocytes) of transgenic male rats both increases lean mass percentage and reduces fat mass. Similar changes in body composition are observed in human skeletal actin promoter driving expression of androgen receptor (HSA-AR) transgenic mice and result from acute testosterone treatment of transgenic female HSA-AR rats. These shifts in body composition in HSA-AR transgenic male rats are associated with hypertrophy of type IIb myofibers and decreased size of adipocytes. Metabolic analyses of transgenic males show higher activity of mitochondrial enzymes in skeletal muscle and increased O(2) consumption by the rats. These results indicate that androgen signaling in myocytes not only increases muscle mass but also reduces fat body mass, likely via increases in oxidative metabolism.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.503

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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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

Citations69
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueEndocrinologySame topicHormonal and reproductive studiesFrench-language works237,207