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Record W1995754879 · doi:10.1155/2007/93578

PPARs, RXRs, and Stem Cells

2007· article· en· W1995754879 on OpenAlexaboutno aff
Z. Elizabeth Floyd, Jeffrey M. Gimble

Bibliographic record

VenuePPAR Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineComputer scienceInformation retrieval

Abstract

fetched live from OpenAlex

Welcome to this special issue of PPAR Research: PPARs, RXRs, and Stem Cells. Within the past decade, there has been a burgeoning interest regarding the mechanisms regulating stem cell regeneration and differentiation in embryonic and adult tissues. Recent studies have identified stem or progenitor cells within most, if not all, somatic tissues. Cell biologists have explored a number of transcriptional regulatory pathways in the context of stem cell self-renewal and lineage commitment. While there has been a wealth of attention given to the Wnt pathway, Oct4, nanog, and STAT transcription factors in this context, the role of PPARs and related nuclear hormone receptors in regulating stem cells remains relatively unexplored. The current issue of PPAR Research has called for manuscripts that will spotlight the PPAR-Stem Cell relationship. We are fortunate to have received a mixture of excellent primary research manuscripts and comprehensive review articles from experts in the field. Mullen, Gu, and Cooney (Houston, Tex) have explored the role of nuclear hormone receptors in murine embryonic stem cell differentiation and function. Purton (Boston, Mass) has comprehensively reviewed the literature concerning the role of retinoid receptors in hematopoietic stem cells. Casteilla, Cousin, and Carmona (Toulouse, Fla) evaluate the classical role of PPARγ as an adipogenic regulator in adipose tissue-derived stromal/stem cells (ASCs). Three investigators use bone marrow-derived mesenchymal stem cell (MSC) models. Isales et al. (Augusta, Ga) provide novel findings relating to the role of mystatin and GILZ on adipogenesis in response to PPAR ligands. Shockley et al. (Bar Harbor, Me & Little Rock, Ark) report the transcriptomic response of MSCs to PPARγ agonists. Duque, Rivas, and Akter (Montreal, Canada) describe a role for farnesylation in modulating MSC adipogenesis. Finally, Cimini et al. (L’Aquila, IT) provide novel insights into the effect of PPARγ during neural stem cell (NSC) astroglial differentiation. We hope that this issue will stimulate other investigators to pursue novel avenues related to the converging themes of PPARs, nuclear hormone receptors, and stem cell biology. The outcomes of such investigations will have far reaching implications regarding fundamental questions relating to normal development, tumor biology, and tissue engineering and regenerative medicine. Z. Elizabeth Floyd Jeffrey M. Gimble

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0070.006
Open science0.0020.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0550.025

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 head0.049
GPT teacher head0.358
Teacher spread0.310 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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
Published2007
Admission routes1
Has abstractyes

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