Combined sperm selection techniques to boost the success of in Vitro Fertilization (IVF) and Intra Cytoplasmic Sperm Injection (ICSI)
Bibliographic record
Abstract
This study aimed to assess the effectiveness of combined methods of Density gradient centrifugation and swim-up (D.G.C/S. U) and Density gradient centrifugation and zeta potential (D.G.C/Z) in semen samples to treat sperm abnormality, decrease DNA fragmentation, and choose one of the best methods for Teratozoospermia patients who have high Sperm DNA Fragmentation (SDF) undergoing assisted reproduction. Method: 101 patients, who all have teratozoospermia with high DNA fragmentation visited the fertility clinic. Semen features were examined using established criteria According to Recent World Health Organization (WHO), DGC /Z and DGC/ SU methods were carried out on semen samples After that, the samples were evaluated by Sperm Chromatin Dispersion (SCD) testing was used by the WHO to detect DNA damage recently. Result:The first combined method used showed statistically significant higher motility with DGC/su(p < 0.001), while the other method showed statistically significant, lower DNA fragmentation and fewer abnormalities was DGC/Z(p < 0.001). We discovered no statistically significant connection between defective sperm morphology and DNA damage. There is no link between sperm motility and DNA damage, indicating that defective sperm are more likely to be normal DNA. Conclusion: According to the results of the current study, the combined use of the DGC/Z and DGC/swim-up procedures improved the motility, morphology, and DNA integrity of semen samples, which may increase the probability of a successful pregnancy.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".