Lifestyle Factors and Laboratory Sperm Processing Techniques Are Correlated With Sperm Dna Fragmentation Index, Oxidative Stress Adducts, and High Dna Stainability
Bibliographic record
Abstract
Abstract Purpose To determine correlation between lifestyle risk factors and sperm quality.Methods Patients (n = 133) who consented for the study completed a lifestyle questionnaire. An aliquot of sperm was frozen at three different timepoints. Preparation methods for 30 semen analysis were compared: ZyMōt Sperm Separation Device (DxNow), Isolate gradient (Irvine), SpermGrad gradient (Vitrolife), and each gradient was followed by swim-up (SU), Isolate + SU and Spermgrad + SU. All samples were analyzed using the Sperm DNA Fragmentation Assay (acridine orange/flow cytometry SDFA™). Analysis included DNA fragmentation index (DFI), oxidative stress adducts (OSA) and high DNA stainability (HDS). Statistical analysis was performed using JMP (SAS 2018) and P < 0.05 was considered statistically significant.Results The neat DFI was not correlated with age, morphology, or oligospermia (< 20 million/mL). Men that consumed alcohol daily trended towards a higher DFI than those that drank multiple times per week and significantly higher than those who never drink (p = 0.0608 and p = 0.0290, respectively), but interestingly not those who drank rarely. DFI was also positively correlated with OSA and HDS in the neat and processed sample (INSEM). The DFI of the INSEM sperm sample was positively correlated with age, poor morphology, and oligospermia (p = 0.0208, p < 0.0001, p = 0.0006, respectively). There was no correlation with BMI or smoking status for neat or processed sperm health. The separation device effectively improved the DFI, OSA, and HDS compared to other methodsConclusion Lifestyle factors and preparation method is correlated with sperm quality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".