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Record W4307320914 · doi:10.1139/cjas-2021-0036

Expression analysis of <i>POSTN</i> gene in ovine follicles

2022· article· en· W4307320914 on OpenAlexvenueno aff
Jiapeng Lin, Chunjie Liu, Liqin Wang, Ying Chen, Xiaolin Li, Yangsheng Wu, Juncheng Huang

Bibliographic record

VenueCanadian Journal of Animal Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRedox biology and oxidative stress
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsFollicleBiologyInternal medicineFolliculogenesisOvarian follicleSpleenEndocrinologyOvaryAndrologyCell biologyEmbryoImmunologyEmbryogenesisMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the potential expression regularity of POSTN in ovarian tissue. In this study, the uterus, ovarian follicles, heart, liver, spleen, lung, kidney, and muscle tissues of Merino sheep (10) were collected and detected by Western blot, real-time quantitative polymerase chain reaction, immunohistofluorescence, and fluorescence staining, respectively. The expression of POSTN in various organizations was analyzed. The results showed that the POSTN in Merino sheep had close homology with Bos mutus and Bos taurus. The expression of POSTN was detected in the uterus, follicle, heart, liver, spleen, lung, kidney, and muscle tissue, among which the expression of POSTN was highest in ovarian tissue. In addition, the expression of POSTN gradually increased with the increase of follicle diameter, among which POSTN was highly expressed in the granulosa cells (GCs) of follicles. Meanwhile, POSTN were distributed throughout the nucleus and cytoplasm of GCs, suggesting that POSTN may be involved in the regulation of follicle development.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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