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Record W4250585585 · doi:10.1016/j.juro.2018.02.2297

MP72-13 EFFECT OF A KETOGENIC DIET ON THE CLEAR CELL RENAL CELL CARCINOMA CELL GROWTH

2018· article· en· W4250585585 on OpenAlexaff
Maxime Benoit, E. Fortier, Magalie Barth, Vincent Procaccio, Jennifer Bourreau, Daniel Henrion, Pierre Bigot

Bibliographic record

VenueThe Journal of Urology · 2018
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsProstate Cancer Canada
Fundersnot available
KeywordsKetogenic dietRenal cell carcinomaMedicineInternal medicineEndocrinologyCellCell growthKetone bodiesCell cultureMetabolismBiochemistryBiologyGeneticsPsychiatry

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyKidney Cancer: Basic Research & Pathophysiology II1 Apr 2018MP72-13 EFFECT OF A KETOGENIC DIET ON THE CLEAR CELL RENAL CELL CARCINOMA CELL GROWTH Maxime Benoit, Edouard Fortier, Magalie Barth, Vincent Procaccio, Jennifer Bourreau, Daniel Henrion, and Pierre Bigot Maxime BenoitMaxime Benoit More articles by this author , Edouard FortierEdouard Fortier More articles by this author , Magalie BarthMagalie Barth More articles by this author , Vincent ProcaccioVincent Procaccio More articles by this author , Jennifer BourreauJennifer Bourreau More articles by this author , Daniel HenrionDaniel Henrion More articles by this author , and Pierre BigotPierre Bigot More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2297AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Clear cell renal cell carcinoma (ccRCC) is characterized by a metabolic feature : an energy production by aerobic glycolysis at the expense of oxydative phosphorylation [1]. Ketogneic diet (KD), which consists of high fat and low carbohydrate intakes, could bring required energy substrates to healthy cells while depriving tumor cells of glucose. METHODS We study the effect of ketogenic diet on a ccRCC cell line : ACHN. Fifteen CD-1 nude mice received a sub-cutaneous xenograft of ACHN cells, and were then split into 3 feeding groups and fed either a standard diet (SD group, n=5), a 2:1 ketogneic diet (KD 2:1 group, n=5) or a 4:1 ketogenic diet (KD 4:1 group, n=5) ad libitum. Tumor growth, blood glucose and blood ketone levels were measured weekly for 8 weeks. RESULTS Ketosis was quickly reached. The mean blood ketone level was 0,86 mmol/L in SD group, 1,07 mmol/L in KD 2:1 group and 1,21 mmol/L in KD 4:1 group (p<0,01) (Fig.1). The mean 8-week tumor growth was 351% in SD group, 65% in KD 2:1 group and 66% in KD 4:1 group (p=0,01) (Fig.2). There was no difference in weight increase between the 3 groups. CONCLUSIONS This study showed that ketogenic diet can slow ccRCC tumor growth in a mouse model, without any difference of result between 2:1 or 4:1 KD. These outcomes have to be verified in other cell lines, and signalling pathways need to be understood by transcriptomic and metabolomic analyses, before starting clinical trials. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e957 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Maxime Benoit More articles by this author Edouard Fortier More articles by this author Magalie Barth More articles by this author Vincent Procaccio More articles by this author Jennifer Bourreau More articles by this author Daniel Henrion More articles by this author Pierre Bigot More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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