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Record W2946258994 · doi:10.1038/s42003-019-0439-x

Identifying the murine mammary cell target of metformin exposure

2019· article· en· W2946258994 on OpenAlexafffund
Mona Shehata, Hye‐Yeon Kim, Ravi N. Vellanki, Paul Waterhouse, Mathepan Mahendralingam, Alison E. Casey, Marianne Koritzinsky, Rama Khokha

Bibliographic record

VenueCommunications Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchGovernment of CanadaCanadian Cancer SocietyOntario Institute for Cancer Research
KeywordsMetforminCellCell biologyCancer researchChemistryBiologyEndocrinologyDiabetes mellitusBiochemistry

Abstract

fetched live from OpenAlex

The heterogeneity of breast cancer makes current therapies challenging. Metformin, the anti-diabetic drug, has shown promising anti-cancer activities in epidemiological studies and breast cancer models. Yet, how metformin alters the normal adult breast tissue remains elusive. We demonstrate metformin intake at a clinically relevant dose impacts the hormone receptor positive (HR+) luminal cells in the normal murine mammary gland. Metformin decreases total cell number, progenitor capacity and specifically reduces DNA damage in normal HR+ luminal cells, decreases oxygen consumption rate and increases cell cycle length of luminal cells. HR+ luminal cells demonstrate the lowest levels of mitochondrial respiration and capacity to handle oxidative stress compared to the other fractions, suggesting their intrinsic susceptibility to long-term metformin exposure. Uncovering HR+ luminal cells in the normal mammary gland as the major cell target of metformin exposure could identify patients that would most benefit from repurposing this anti-diabetic drug for cancer prevention/therapy purposes.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.269

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 science0.0010.001
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 head0.018
GPT teacher head0.279
Teacher spread0.261 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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