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Record W7161807366 · doi:10.82308/3213

Human sperm DNA damage: Impact of «in vitro» oxidative stress and «in vivo» vitamins on conventional and advanced sperm function parameters

2019· dissertation· en· W7161807366 on OpenAlexaboutno aff
Mohammed Alharbi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsnot available
Fundersnot available
KeywordsSpermOxidative stressDNA fragmentationDNA damageSperm motilityReactive oxygen speciesAntioxidantMale infertilitySemen

Abstract

fetched live from OpenAlex

Introduction: Reactive oxygen species (ROS) and oxidative stress (OS) play a major role in sperm DNA damage which may adversely affect fertilisation and embryo development. ROS induces sperm DNA damage and impairs sperm motility via multiple mechanisms (lipid peroxidation process, initiation of apoptosis, and DNA oxidation). Oral antioxidants may have a favorable effect in decreasing ROS and protecting the sperm against DNA damage. However, a physiologic balance between oxidation and reduction is vital for sperm function. Material & methods: For in vitro OS study, we performed a prospective study of 10 fertile donors at McGill University Health Center in Montreal. Semen samples were collected and sperm parameters (progressive motility and viability) were obtained. For the in vivo vitamin study, we conducted a prospective study of 24 men presenting with idiopathic infertility at the OVO fertility clinic in Montreal between January 2016 and January 2018. We included 6 healthy fertile sperm donors as a control. All patients received 6 months of oral antioxidant treatment. We assessed sperm parameters (concentration, progressive motility) and aniline blue (AB) staining. For both studies, we also evaluated DNA fragmentation index (DFI), high DNA stainability (HDS), iodoacetamide fluorescein (IAF), at baseline and at increasing doses of hydrogen peroxide (H2O2) of 100, 250, and 500 µM H2O2 for in vitro study, and before and 6 months after antioxidant treatment for in vivo study. Results: In the in vitro study, the mean viability, mean % progressive motility, and mean % DFI showed a statistically significant differences among the groups (P < 0.05). H2O2 induced a statistically significant increase in %DFI as the dose of H2O2 increased (P < 0.05). % positive IAF fluorescence increased with increasing dose of H2O2 but without statistical significance (P > 0.05). In term of the effect of H2O2 on % HDS, there was an improvement with increasing H2O2 concentrations, but it was not of statistical significance (P > 0.05). In the in vivo study, the fertile donor group had a higher mean sperm concentration and mean % progressive motility than the group of infertile men (P < 0.05). The mean % DFI, % positive IAF fluorescence, % positive AB staining of infertile group were significantly higher than that of the fertile control group (P < 0.05). Conversely, the mean % HDS in the infertile group was not significantly different than the control group. There was an improvement in sperm parameters after antioxidant treatment, but the difference was not statistically significant (P > 0.05). Unexpectedly, the chromatin integrity measures (% HDS, % positive IAF fluorescence, % positive AB) worsened after 6 months of antioxidant treatment, but without statistical significance (P > 0.05). There was a trend toward improvement in % DFI after antioxidants supplementation with borderline statistical significance (mean: 23.4 vs. 19.1; P = 0.06). Conclusions: In vitro oxidative stress (OS) resulted in a significant increase in DFI with impaired motility at higher concentration of H2O2 with no impact on chromatin compaction. In our study, 6 months of antioxidant supplementation had no significant impact on sperm parameters, DFI, and chromatin integrity measures

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)0.001
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 score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0040.000

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 head0.012
GPT teacher head0.295
Teacher spread0.283 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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