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Record W2741415354 · doi:10.1158/1538-7445.am2017-2234

Abstract 2234: Efatutazone reduces mammosphere formation in <i>Brca1</i>WT/fl11/Cre/p53+/- and <i>Brca1</i>fl11/fl11/Cre/p53+/- mice

2017· article· en· W2741415354 on OpenAlexaboutno aff
Sahar J. Alothman, Chao Shan, Weisheng Wang, Priscilla A. Furth

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
Fundersnot available
KeywordsProgenitor cellMammary glandBiologyAndrologyStem cellFat padMolecular biologyEndocrinologyInternal medicineAdipose tissueMedicineCancerCell biologyBreast cancer

Abstract

fetched live from OpenAlex

Abstract Introduction: Efatutazone, a PPAR gamma agonist, may affect tumor growth through the induction of terminal cell differentiation. Thus, we hypothesize that efatutazone could affect progenitor cell number. Here, we evaluate progenitor cell number by measuring mammospheres, which is considered one way to measure potential progenitor cells. Methods: Two month old Brca1WT/fl11/Cre/p53+/- (n= 14) and Brca1fl11/fl11/Cre/p53+/- (n= 10) C57Bl/6 mice were randomly placed on either control or treated with efatutazone through the diet (30-mg/kg concentration, F3028, rodent diet, grain-based, 1/2-in pellets; Bio-Serv, Frenchtown, NJ) with necropsy at four months and isolation of primary mammary epithelial cells from thoracic glands for studies of mammosphere formation using SCIVAX 96-well low adhesion nanoculture plates (Organogenix, Inc., Japan) using EpiCult-B Mouse Media (Stem Cell Technology, Inc., Vancouver, Canada) with 0,1,5, and 10% fetal bovine serum (FBS) added. Thoracic mammary gland tissue was frozen at -20°C followed by isolation of RNA and evaluation of PPAR gamma pathway gene expression by real-time RT-PCR using TaqMan® Array Mouse Lipid Regulated Genes (Thermo Fisher Scientific, Inc., Waltham MA). One inguinal gland was fixed for mammary gland whole mount and the other inguinal gland was formalin-fixed and paraffin-embedded for histology. Results: SCIVAX nanoculture plates showed reproducible increases in mammosphere numbers with increasing FBS concentrations (p <0.05 one-way, Kruskal-Wallis). A significant increase in sphere number was seen in Brca1fl11/fl11/Cre/p53+/- compared to Brca1WT/fl11/Cre/p53+/- mice (p<0.05 one-way, Kruskall Wallis). Efatutazone treatment significantly decreased sphere numbers in Brca1WT/fl11/Cre/p53+/- and in Brca1fl11/fl11/Cre/p53+/- mice (p<0.05 one-way, Kruskall Wallis). Expression of PPAR gamma pathway genes were increased at the RNA level with statistically significant increases in Acadvl, Tnf, Alox5 (p<0.05) and Il1B, Srebf2, Hmgcs1, Hmgcr (p<0.01) genes (BootsRatio, http://rht.iconcologia.net/stats/br/several.html) in mice on efatutazone as compared to control diet. Discussion: SCIVAX nanoculture plates can be used to quantitatively evaluate and compare mammosphere numbers between different genotypes and treatment groups. The higher numbers of mammospheres found with loss of both Brca1 copies as compared to one copy is consistent with previously published literature. The decrease in mammosphere numbers with efatutazone treatment could be secondary to its differentiating impact on mammary epithelial cells. Conclusion: While efatutazone statistically significantly reduced mammosphere numbers, the absolute reduction was less the 25%. Citation Format: Sahar J. Alothman, Shan Chao, Weisheng Wang, Priscilla A. Furth. Efatutazone reduces mammosphere formation in Brca1WT/fl11/Cre/p53+/- and Brca1fl11/fl11/Cre/p53+/- mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2234. doi:10.1158/1538-7445.AM2017-2234

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.068
GPT teacher head0.397
Teacher spread0.328 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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