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Record W2169276721 · doi:10.1017/s0967199414000781

Treatment of aromatase (CYP19A1) inhibitor reduces fertility in porcine sperm

2015· article· en· W2169276721 on OpenAlexaff
Jong‐Nam Oh, Jae Yeon Hwang, Kwang‐Hwan Choi, Chang‐Kyu Lee

Bibliographic record

VenueZygote · 2015
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsBiotechnology Research Institute
FundersNational Research Foundation
KeywordsSpermAndrologyHuman fertilizationIn vitro fertilisationPercollExemestaneAromataseBiologySemenSperm motilityOocyteFertilityChemistryIn vitroEmbryoMedicineCell biologyGeneticsPopulation

Abstract

fetched live from OpenAlex

To ascertain whether aromatase (CYP19A1) expression is linked to sperm fertility of pigs, the present study determined the expression of the CYP19A1 gene in porcine sperm and its relationship with fertilization in vitro. First, to investigate its role in fertility, the presence of CYP19A1 of mRNA and protein expression in porcine sperm were confirmed by real-time (RT) or quantitative polymerase chain reaction (q-PCR) and by western blots. The expression levels were determined quantitatively using two sperm groups recovered by a Percoll gradient, which revealed that the sperm group with a low density had a higher penetration rate than that of the high-density group (P < 0.05). However, the expression level of CYP19A1 was not significantly different between the two groups. Secondly, to examine the effect of aromatase activity on fertilization, fresh semen was treated with a steroidal inhibitor, exemestane (50 μM for 0.5 h), followed by the dose- and time-dependent viability test. Our results clearly showed that an exemestane treatment effect (P < 0.05) was found for both the sperm-penetration rate and the oocyte cleavage rate. These results indicated that CYP19A1 could be involved in sperm fertility and its expression in sperm plays an important role in fertilization.

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 score0.040
Threshold uncertainty score0.345

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 head0.065
GPT teacher head0.327
Teacher spread0.262 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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