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Record W2889661542 · doi:10.7482/0003-9438-57-029

A second look at leptin and adiponectin actions on the growth of primary porcine myoblasts under serum-free conditions

2014· article· en· W2889661542 on OpenAlexaff
Katja Will, Judith Kuzinski, Marie‐France Palin, Jan‐Peter Hildebrandt, Charlotte Rehfeldt

Bibliographic record

VenueArchives animal breeding/Archiv für Tierzucht · 2014
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsAgriculture and Agri-Food Canada
FundersLeibniz-Gemeinschaft
KeywordsAdipokineAdiponectinLeptinInternal medicineEndocrinologyAdipose tissueMAPK/ERK pathwayMyocyteChemistryBiologyCell biologyKinaseMedicineInsulin resistanceInsulin

Abstract

fetched live from OpenAlex

Abstract. Cross-talk between adipose tissue and skeletal muscle may be mediated in part by adipokines. This study was conducted to elucidate further aspects of a possible role of recombinant adiponectin and leptin in the in vitro growth of primary porcine skeletal muscle cells cultured in energetically balanced, growth factor-supplemented, serum-free medium (GF-SFM). Therefore, the effects of these adipokines on cell number (DNA content), DNA synthesis rate, cell death and on key intracellular signalling molecules were investigated. Short-term adiponectin and leptin treatment decreased DNA synthesis, measured as [3H]-thymidine incorporation, as early as after 4-h exposure (P<0.01), without alterations in DNA content. Both adipokines attenuated the rate of cell death in terms of lactate dehydrogenase (LDH) activity in the culture medium after 48-h treatment (P<0.05). The specific activation of p44/42 MAP kinase (MAPK) was reduced (P<0.05) after 15-min incubation with either adipokine. In conclusion, the early decreases in DNA synthesis of primary porcine myoblasts cultured in GF-SFM in response to adiponectin or leptin are related to p44/42 MAPK signalling and adipokine treatment does not impair cell viability.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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.

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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

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