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Record W2342149502 · doi:10.1055/s-0035-1571191

Insulin Cannot Induce Adipogenic Differentiation in Primary Cardiac Cultures

2016· article· en· W2342149502 on OpenAlexaff
Rajendra K. Sharma

Bibliographic record

VenueInternational Journal of Angiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAdipogenesisOil Red OInsulinEndocrinologyInternal medicinePopulationLipid dropletGrowth factorMedicineAdipose tissue

Abstract

fetched live from OpenAlex

Cardiac tissue contains a heterogeneous population of cardiomyocytes and nonmyocyte population especially fibroblasts. Fibroblast differentiation into adipogenic lineage is important for fat accumulation around the heart which is important in cardiac pathology. The differentiation in fibroblast has been observed both spontaneously and due to increased insulin stimulation. The present study aims to observe the effect of insulin in adipogenic differentiation of cardiac cells present in primary murine cardiomyocyte cultures. Oil Red O (ORO) staining has been used for observing the lipid accumulations formed due to adipogenic differentiation in murine cardiomyocyte cultures. The accumulated lipids were quantified by ORO assay and normalized using protein estimation. The lipid accumulation in cardiac cultures did not increase in presence of insulin. However, addition of other growth factors like insulin-like growth factor 1 and epidermal growth factor promoted adipogenic differentiation even in the presence of insulin and other inhibitory molecules such as vitamins. Lipid accumulation also increased in cells grown in media without insulin after an initial exposure to insulin-containing growth media. The current study adds to the existing knowledge that the insulin by itself cannot induce adipogenic induction in the cardiac cultures. The data have significance in the understanding of cardiovascular health especially in diabetic patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.185

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 science0.0000.000
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 head0.008
GPT teacher head0.263
Teacher spread0.254 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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