Paper-Based Quantification of Male Fertility Potential
Bibliographic record
Abstract
BACKGROUND: More than 70 million couples worldwide are affected by infertility, with male-factor infertility accounting for about half of the cases. Semen analysis is critical for determining male fertility potential, but conventional testing is costly and complex. Here, we demonstrate a paper-based microfluidic approach to quantify male fertility potential, simultaneously measuring 3 critical semen parameters in 10 min: live and motile sperm concentrations and sperm motility. METHODS: The device measures the colorimetric change of yellow tetrazolium dye to purple formazan by the diaphorase flavoprotein enzyme present in metabolically active human sperm to quantify live and motile sperm concentration. Sperm motility was determined as the ratio of motile to live sperm. We assessed the performance of the device by use of clinical semen samples, in parallel with standard clinical approaches. RESULTS: Detection limits of 8.46 and 15.18 million/mL were achieved for live and motile sperm concentrations, respectively. The live and motile sperm concentrations and motility values from our device correlated with those of the standard clinical approaches (R(2) ≥ 0.84). In all cases, our device provided 100% agreement in terms of clinical outcome. The device was also robust and could tolerate conditions of high absolute humidity (22.8 g/m(3)) up to 16 weeks when packaged with desiccant. CONCLUSIONS: Our device outperforms existing commercial paper-based assays by quantitatively measuring live and motile sperm concentrations and motility, in only 10 min. This approach is applicable to current clinical practices as well as self-diagnostic applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".