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Record W4411749145 · doi:10.1093/humrep/deaf097.361

P-052 Redefining Male Infertility: ORP as a Key Marker for Sperm Quality

2025· article· en· W4411749145 on OpenAlexaff
Artak Tadevosyan, Mélanie Chow-Shi-Yée, Jessica Carrière, Marie-Éve Stébenne, Isaac Jacques Kadoch

Bibliographic record

VenueHuman Reproduction · 2025
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSperm qualityInfertilitySpermMale infertilityAndrologyGynecologyKey (lock)BiologyMedicineGeneticsPregnancy

Abstract

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Abstract Study question Can ORP serve as a reliable biomarker for male infertility assessment and should it be integrated into routine testing? Summary answer Since ORP does not correlate with DNA fragmentation, both parameters should be routinely assessed in male infertility evaluations for a comprehensive understanding of semen quality. What is known already Oxidative stress plays a crucial role in male infertility by impairing sperm function and increasing DNA fragmentation. ORP is an emerging marker to evaluate oxidative stress levels in semen. Elevated ORP has been associated with poor semen quality, including reduced motility and concentration. However, its relationship with SDF remains unclear, and studies assessing its predictive value for male infertility are limited. Understanding the association between ORP, sperm concentration, and SDF may help refine diagnostic protocols and improve patient management. Study design, size, duration This retrospective cohort study analyzed semen samples from 900 men undergoing fertility evaluation over two years. Participants/materials, setting, methods This study included men (mean age: 38 years) undergoing fertility evaluation. Semen samples were collected and analyzed following WHO guidelines to ensure standardized assessment of oxidative stress and sperm quality. Sperm DNA fragmentation (SDF) was measured using the TUNEL assay on the BD FACSLyric flow cytometer, while oxidation-reduction potential (ORP) was assessed with the MiOXSYS analyzer. Semen parameters, including concentration, total sperm count, pH, and abstinence duration, were also recorded. Main results and the role of chance Semen analysis of 900 men undergoing fertility evaluation showed a mean sperm concentration of 57.2 × 106 sperm/mL and a total sperm count of 147.9 × 106 sperm/sample. The mean semen pH was 8.2, and the average abstinence period was 2.9 days. ORP levels averaged 1.7 mV/106 sperm/mL, with values >1.34 mV/106 sperm/mL indicating high oxidative stress. The mean SDF was 19.4%, categorized as low (≤16.9%), moderate (16.9–30%), or high (>30%). The distribution of ORP and SDF categories among patients was: low ORP & low SDF (38.4%), low ORP & moderate SDF (22.6%), low ORP & high SDF (11.8%), high ORP & low SDF (13.8%), high ORP & moderate SDF (7.3%), and high ORP & high SDF (6.1%). A weak, non-significant correlation was found between ORP and SDF (Spearman r = 0.046, 95% CI: -0.02 to 0.11, P = 0.46). However, a significant negative correlation was observed between ORP and sperm concentration (Spearman r = -0.569, 95% CI: -0.61 to -0.52, P < 0.001). Limitations, reasons for caution Potential methodological variations in ORP and SDF measurement techniques may impact results. Standardization across different assay platforms and laboratory conditions is necessary to ensure consistency and reproducibility. Wider implications of the findings ORP is a valuable complementary marker for semen quality, helping identify infertile patients with normal DNA fragmentation. Since oxidative stress affects fertility independently of DFI, evaluating both markers in male infertility assessments could improve diagnostic accuracy and guide personalized treatment strategies, ultimately enhancing reproductive outcomes for affected individuals. Trial registration number No

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)0.002
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 score0.207
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.056
GPT teacher head0.358
Teacher spread0.302 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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