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Record W2799643954 · doi:10.1139/cjas-2017-0130

Regulatory role of <i>LEF-1</i> in the proliferation of Arbas White Cashmere goat dermal papilla cells

2018· article· en· W2799643954 on OpenAlexvenueno aff
Fei Hao, Wei Yan, Xiaodong Guo, Bing Zhu, Dongjun Liu

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsCyclin D1Wnt signaling pathwayCashmere goatDermal papillaeHair follicleCell growthCateninBiologyCell cycleSignal transductionMolecular biologyCell biologyChemistryCellGeneGenetics

Abstract

fetched live from OpenAlex

Cashmere, which has high economic value, is made from the secondary hair follicles of cashmere goat skin. Dermal papilla cells (DPCs) are considered the center for regulation of hair growth, which is closely related to hair follicle growth. We constructed LEF-1 overexpression and interference experimental groups of goat DPCs to investigate LEF-1 regulation of DPCs proliferation by Wnt signaling, and provide a theoretical basis for improving cashmere yield. In primary DPCs, LEF-1, β-catenin, C-myc, and cyclin D1 expression in the LEF-1 overexpression group was 9.25-, 1.27-, 1.74-, and 1.63-fold, respectively, that of the control. LEF-1, β-catenin, C-myc, and cyclin D1 expression in the LEF-1 interference group was 0.20-, 0.75-, 0.38-, and 0.39-fold, respectively, that of the control. In secondary DPCs, LEF-1, β-catenin, C-myc, and cyclin D1 expression in the LEF-1 overexpression group was 10.53-, 1.48-, 1.64-, and 1.39-fold, respectively, that of the control. LEF-1, β-catenin, C-myc, and cyclin D1 expression in the LEF-1 interference group was 0.21-, 0.71-, 0.40-, and 0.36-fold, respectively, that of the control. Primary and secondary DPCs proliferation rates changed with LEF-1 expression. Therefore, the LEF-1 regulation pattern of cell proliferation through Wnt signaling is similar in both DPCs.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.229
Teacher spread0.221 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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