Selection of high-quality sperm with thousands of parallel channels
Bibliographic record
Abstract
Sperm selection is essential for successful fertilization and embryo development. Current clinical sperm selection methods are labor-intensive and lack the selectivity required to isolate high-quality sperm. Microfluidic sperm selection approaches have shown promise but present a trade-off between the quality and quantity of selected sperm - clinicians demand both. The structure of the female reproductive tract helps to isolate a sufficient quantity of high-quality sperm for fertilization with densely folded epithelium that provides a multitude of longitudinally oriented pathways that guide sperm toward the fertilization site. Here, a three-dimensionally structured sperm selection device is presented that levers this highly parallelized in vivo mechanism for in vitro sperm selection. The device is inserted in a test tube atop 1 mL of raw semen and provides 6500 channels that isolate ∼100 000 high-DNA-integrity sperm for assisted reproduction. In side-by-side clinical testing, the developed approach outperforms the best current clinical methods by improving the DNA integrity of the selected sperm subpopulation up to 95%. Also, the device streamlines clinical workflow, reducing the time required for sperm preparation 3-fold. This single-tube, single-step sperm preparation approach promises to improve both the economics and outcomes of assisted reproduction practices, especially in cases with significant male-factors.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".