MétaCan
Menu
Back to cohort
Record W4390270443 · doi:10.1111/andr.13578

Microfluidics: The future of sperm selection in assisted reproduction

2023· review· en· W4390270443 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAndrology · 2023
Typereview
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsSpermMicrofluidicsSelection (genetic algorithm)BiologyComputer scienceNanotechnologyArtificial intelligenceMaterials scienceGenetics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.318
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it