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Record W2040515642 · doi:10.1016/j.juro.2011.02.2242

2014 IFLUENCE OF SEMINAL CATALASE ACTIVITY ON IVF PREGNANCY

2011· article· en· W2040515642 on OpenAlexaboutno aff
Khalid Alrabeeah, Naif Alhathal, Maria San Gabriel, Armand Zini

Bibliographic record

VenueThe Journal of Urology · 2011
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsnot available
Fundersnot available
KeywordsIntracytoplasmic sperm injectionSemenCatalaseGynecologyMedicineAndrologyPregnancyInfertilitySpermMale infertilityDNA fragmentationReproductive medicineOxidative stressBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyInfertility: Physiology, Pathophysiology, Basic Research1 Apr 20112014 IFLUENCE OF SEMINAL CATALASE ACTIVITY ON IVF PREGNANCY Khalid Alrabeeah, Naif Alhathal, Maria San Gabriel San Gabriel, and Armand Zini Khalid AlrabeeahKhalid Alrabeeah Montreal, Canada More articles by this author , Naif AlhathalNaif Alhathal Montreal, Canada More articles by this author , Maria San Gabriel San GabrielMaria San Gabriel San Gabriel Montreal, Canada More articles by this author , and Armand ZiniArmand Zini Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.2242AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Male infertility is associated with seminal oxidative stress (high levels of seminal reactive oxygen species [ROS] and low levels of seminal antioxidants). The aim of this study was to evaluate the relationship between pregnancy outcomes after conventional in vitro fertilization (IVF) or IVF with intracytoplasmic sperm injection (ICSI) and both conventional semen parameters and markers of oxidative stress (sperm DNA damage and seminal catalase activity). METHODS We conducted a prospective study of infertile couples presenting for treatment with IVF and IVF/ICSI. After obtaining informed consent, an aliquot of the semen sample used for assisted reproduction was collected and stored for later assessment of sperm DNA integrity and seminal catalase activity. Conventional semen parameters, sperm DNA fragmentation index (%DFI), high DNA stainability (%HDS) and seminal catalase-like activity were measured and the results correlated with outcomes of IVF and ICSI. RESULTS A total of 70 infertile couples were recruited: 31 underwent IVF and 39 ICSI (with 35% and 41% pregnancy rates, respectively). Catalase-like activity was positively correlated (albeit weakly) with IVF pregnancy rate (r=0.365, P=0.038) but not with ICSI pregnancy rate. As expected, none of the conventional semen parameters were correlated with clinical pregnancy rates after IVF or ICSI. Similarly, markers of sperm DNA integrity (%DFI and %HDS) were not correlated with clinical pregnancy after IVF or ICSI. CONCLUSIONS Seminal catalase activity (a key antioxidant involved in the degradation of hydrogen peroxide) is associated with pregnancy rate after IVF. The data suggest that couples in whom the men have low seminal catalase activity (i.e. inadequate protection from hydrogen peroxide) have a higher risk of post-fertilization failure in IVF cycles. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e805 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Khalid Alrabeeah Montreal, Canada More articles by this author Naif Alhathal Montreal, Canada More articles by this author Maria San Gabriel San Gabriel Montreal, Canada More articles by this author Armand Zini Montreal, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.003

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 head0.028
GPT teacher head0.258
Teacher spread0.229 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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