Rapid selection of sperm with high DNA integrity
Bibliographic record
Abstract
Sperm selection is essential to assisted reproductive technology (ART), influencing treatment outcomes and the health of offspring. The fundamental challenge of sperm selection is dictated by biology: a heterogeneous population of ~10(8) sperm per milliliter with a short lifetime in vitro. However, conventional sperm selection approaches result in less than 50% improvement in DNA integrity. Here, a clinically applicable microfluidic device is presented that selects sperm based on the progressive motility in 500 parallel microchannels. The result is a one-step procedure for semen purification and high DNA integrity sperm selection from 1 mL of raw semen in under 20 minutes. Experiments with bull sperm indicate more than 89% improvement in selected sperm vitality. Clinical tests with human sperm show more than 80% improvement in human DNA integrity, significantly outperforming the best current practices. These results demonstrate the presence of a sub-population of sperm with nearly intact chromatin and DNA integrity, and a simple clinically-applicable lab-on-a-chip method to select this population.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".