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Record W4394920352 · doi:10.14814/phy2.16003

Sex differences in cachexia and branched‐chain amino acid metabolism following chemotherapy in mice

2024· article· en· W4394920352 on OpenAlexafffund
Stephen Mora, G. S. Mann, Olasunkanmi A. J. Adegoke

Bibliographic record

VenuePhysiological Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsCachexiaInternal medicineEndocrinologyAnabolismChemotherapyMetabolismMedicineSkeletal muscleBiologyCancer

Abstract

fetched live from OpenAlex

Abstract Chemotherapy is a major contributor to cachexia, but studies often investigate male animals. Here, we investigated whether sex modifies the effects of chemotherapy on cachexia and BCAA metabolism. Ten‐week‐old CD2F1 male and female mice were treated with the chemotherapy drug cocktail folfiri (50 mg/kg 5‐fluorouracil, 90 mg/kg leucovorin, and 24 mg/kg CPT11) (drug) or vehicle twice a week for 6 weeks. Insulin tolerance tests were conducted and BCAA levels and metabolism were measured in plasma and tissues. Drug treatment reduced body and skeletal muscle weights and anabolic signaling in both sexes, with females showing worsened outcomes ( p < 0.05 for all). Drug treatment increased plasma BCAA only in males, but BCAA concentrations in the skeletal muscle of both sexes were decreased; this decrease was more profound in males ( p = 0.0097). In addition, muscle expression of the BCAA transporter LAT1 was reduced; this reduction was more severe in females ( p = 0.0264). In both sexes, the (inhibitory) phosphorylation of BCKD‐E1α ser293 was increased along with decreased BCKD activity. In the liver, drug treatment increased BCAA concentrations and LAT1 expression, but BCKD activity was suppressed in both sexes ( p < 0.05 for all). Our results demonstrate that altered BCAA metabolism may contribute to chemotherapy‐induced cachexia in a sex‐dependent manner.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.393

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 head0.051
GPT teacher head0.341
Teacher spread0.290 · 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 designObservational
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

Citations11
Published2024
Admission routes2
Has abstractyes

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