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Record W2921972886 · doi:10.1080/21623945.2019.1583037

Harnessing adipogenesis to prevent obesity

2019· review· en· W2921972886 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdipocyte · 2019
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsAdipogenesisAdipocyteAdipose tissueWhite adipose tissueBiologyEndocrinologyInternal medicineCell biologyMedicine

Abstract

fetched live from OpenAlex

Obesity and associated metabolic complications, including diabetes, cardiovascular and hepatic diseases, and certain types of cancers, create a major socioeconomic burden. Obesity is characterized by excessive expansion of white adipose tissue resulting from increased adipocyte size, and enhanced adipocyte precursor cells proliferation and differentiation into mature adipocytes, a process well-defined as adipogenesis. Efforts to develop therapeutically potent strategies to circumvent obesity are impacted by our limited understanding of molecular mechanisms regulating adipogenesis. In this review, we discuss recently discovered molecular mechanisms restraining adipogenesis. In this perspective, the discoveries of white adipose tissue endogenous adipogenesis-regulatory cells (Aregs) that negatively regulate adipocyte differentiation, platelet-derived growth factor receptor isoform α (PDGFRα) activation and downstream signaling that hinder adipocyte precursors differentiation, and a group of obesity-associated non-coding RNAs (ncRNAs) that regulate adipogenesis open up promising therapeutic avenues to prevent and/or treat obesity.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.009

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 head0.071
GPT teacher head0.363
Teacher spread0.293 · 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