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Record W3000057583 · doi:10.1139/gen-2019-0132

Characterization of HTATIP2 and its role during hair follicle cycles in Angora rabbit

2020· article· en· W3000057583 on OpenAlexvenueno aff
Bohao Zhao, Yang Chen, Shuaishuai Hu, Naisu Yang, Ming Liu, Jiali Li, Zhiyuan Bao, Xinsheng Wu

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyHair follicleCell biologyCell cycleGeneCell growthGene knockdownHair cycleSignal transductionMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Hair follicle (HF) growth and cycling is a complex biological process that occurs in most mammals. As HF growth and cycling directly impacts rabbit wool yield, it is important to better understand the potential regulation pattern of HF development. Our previous study demonstrated that HTATIP2 may participate in regulating rabbit HF cycles, but the molecular mechanism of HTATIP2 remained unclear. In this study, the coding sequence of the HTATIP2 gene in Angora rabbit was cloned. The length of the coding region sequence was 840 bp, which could code 279 amino acids, and exhibited high homology in different mammals. Bioinformatics analyses indicated that the HTATIP2 protein is stable, hydrophilic, located around the cytoplasm, and has a putative signal peptide. Moreover, we verified that HTATIP2 is highly expressed during catagen and telogen of the HF cycle. The overexpression vector was constructed and siRNAs were designed. Overexpression and knockdown of HTATIP2 appeared to regulate JAK-STAT pathway genes, such as BCL2, CCND1, c-Myc, and STAT2. It is therefore likely that HTATIP2 promotes cell apoptosis and inhibits cell proliferation. Our results indicate that HTATIP2 is highly expressed during catagen and telogen and may play an important role in JAK-STAT signaling. This study provides a theoretical foundation for investigating HTATIP2 in biological processes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.214

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 designBench or experimental
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

Citations6
Published2020
Admission routes1
Has abstractyes

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