Microfluidics: The future of sperm selection in assisted reproduction
Bibliographic record
Abstract
BACKGROUND: Obtaining functional sperm cells is the first step to treat infertility. With the ever-increasing trend in male infertility, clinicians require access to effective solutions that are able to single out the most viable spermatozoa, which would max out the chance for a successful pregnancy. The new generation techniques for sperm selection involve microfluidics, which offers laminar flow and low Reynolds number within the platforms can provide unprecedented opportunities for sperm selection. Previous studies showed that microfluidic platforms can provide a novel approach to this challenge and since then researchers across the globe have attacked this problem from multiple angles. OBJECTIVE: In this review, we seek to provide a much-needed bridge between the technical and medical aspects of microfluidic sperm selection. Here, we provide an up-to-date list on microfluidic sperm selection procedures and its application in assisted reproductive technology laboratories. SEARCH METHOD: A literature search was performed in Web of Science, PubMed, and Scopus to select papers reporting microfluidic sperm selection using the keywords: microfluidic sperm selection, self-motility, non-motile sperm selection, boundary following, rheotaxis, chemotaxis, and thermotaxis. Papers published before March 31, 2023 were selected. OUTCOMES: Our results show that most studies have used motility-based properties for sperm selection. However, microfluidic platforms are ripe for making use of other properties such as chemotaxis and especially rheotaxis. We have identified that low throughput is one of the major hurdles to current microfluidic sperm selection chips, which can be solved via parallelization. CONCLUSION: Future work needs to be performed on numerical simulation of the microfluidics chip prior to fabrication as well as relevant clinical assessment after the selection procedure. This would require a close collaboration and understanding among engineers, biologists, and medical professionals. It is interesting that in spite of two decades of microfluidics sperm selection, numerical simulation and clinical studies are lagging behind. It is expected that microfluidic sperm selection platforms will play a major role in the development of fully integrated start-to-finish assisted reproductive technology systems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".