Study impact smoking on Sperm Morphology and DNA fragmentation in Iraqi male fertility
Bibliographic record
Abstract
Background: Male infertility is issue a prevalent, involves various factors. Sperm quality, particularly influenced by smoking, is crucial for fertility. DNA fragmentation, indicating damage to DNA strands in sperm cells, is gaining importance in male infertility research, especially concerning abnormal sperm morphology. Aim: To investigate the potential influence of smoking on both sperm morphology and DNA fragmentation in male fertility. Materials and Methods: In this cross-sectional observational study, 83 participants (38 nonsmokers and 45 smokers) with Normozoospermia provided informed consent. Semen samples were collected following at WHO 2021 guidelines. Sperm morphology was evaluated using Kruger Strict Criteria with Hematoxylin stain, and DNA fragmentation index was assessed using Aniline Blue Stain and Sperm Chromatin damage. Results: The significant correlation signs were especially clear in various parameters, including sperm concentration (P=0.000484), sperm morphology (P=0.0001), as well as specific morphological characteristics such as pin & small head (P=0.039), round head (P=0.002), tapered head (P=0.008), irregular neck (P=0.002), short tail (P=0.020), sperm nuclear maturity (P=0.048), and sperm chromatin dispersion (P=0.042). Notably, no significant correlation was found between smoking and non-smoking individuals with normal sperm morphology and undamaged DNA in sperm. Conclusion: Show a links between sperm abnormal morphology and DNA damage in smoking and nonsmoking. Additionally, both smokers and nonsmokers with abnormal sperm morphology according to the Tygerberg criteria exhibited a notable rise in DNA damage index as well as appear important to use Kruger strict criteria to detect sperm morphology in routine semen analysis. Keywords: DNA Fragmentation index, Smokers, Sperm Morphology, Hematoxylin Stain and Kruger Strict Criteria.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".