Male infection: cross-contamination risk management and impact on sperm parameters
Bibliographic record
Abstract
Chronic viral infections are considered a risk factor for male fertility given their ability to infect semen. Several studies have shown that semen parameters and gamete DNA integrity can be compromised by human immunodeficiency virus (HIV), hepatitis B virus (HBV) and hepatitis C virus (HCV) infections. These effects on male fertility are also associated with other types of infection, such as human papillomavirus. To date, European directives for viral screening of couples undergoing assisted reproduction treatment requires evaluation only for the three most common infections: HIV, HBV and HCV. In all these cases it is essential that all operators involved in the laboratory are aware of the risk to themselves and to the patients treated, and also of the risk associated with the possibility of cross-contamination with other patients. In IVF centres, specific procedures must be established and respected in the case of serodiscordant couples, in order to guarantee correct management and treatment of biological samples. We can reduce or minimise the risk of cross-contamination by applying specific laboratory protocols for the safe handling of gametes and embryos. This mini review aims to describe the impact of viral infection on sperm and male fertility and the protocols and recommendations for the management of gametes from serodiscordant couples with infectious diseases. KEY WORDS: Serodiscordant, IVF, cross-contamination, procedures, male infertility, sexual transmission, sperm, viruses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".