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Record W4381682242 · doi:10.1093/humrep/dead093.438

P-073 Comparison of human preimplantation embryo development after sperm selection for ICSI using the microfluidic ZyMōt device versus density gradient centrifugation

2023· article· en· W4381682242 on OpenAlexaff
H Balakaier, Iryna Kuznyetsova, A Chaudary, Clifford Librach

Bibliographic record

VenueHuman Reproduction · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoCReATe Fertility Centre
Fundersnot available
KeywordsBlastocystAndrologySpermBiologySemenCryopreservationIntracytoplasmic sperm injectionAssisted reproductive technologySperm motilityEmbryoGynecologyEmbryogenesisIn vitro fertilisationPregnancyMedicineGeneticsInfertility

Abstract

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Abstract Study question Does microfluidic sperm selection using ZyMōt device has a significant effect on blastocyst development, ploidy, and improves pregnancy outcomes when compared with density-gradient centrifugation. Summary answer The study demonstrated that sperm selection using a microfluidic device had no impact on euploid blastocyst rates or clinical pregnancy outcomes. What is known already Selecting competent spermatozoa with the highest genomic integrity for ICSI is a key to achieving normal embryo development and a healthy live birth. Several sperm preparation techniques have long been scrutinized with varied success. The novel microfluidic ZyMōt device appears promising for improving the quality of sperm used in Assisted Reproductive Technologies. Preliminary tests have shown that ZyMōt may increase sperm motility and considerably decrease DNA fragmentation, which could potentially enhance euploid blastocyst development rates. However, the impact of this sperm selection method on clinical outcomes has not been fully investigated. Study design, size, duration This is a retrospective cohort study performed in a single academic IVF centre (IRB # 16367). Data were collected from 1286 ICSI cycles in 2020-2021 (730 with ZyMōt and 556 with DGC), including 867 cycles with PGT-A outcomes. Morphological grading of 6640 blastocysts cultured in a time lapse imaging incubator (Embryoscope™) and ploidy of 4783 blastocysts using high resolution NGS (Illumina platform and Blue Gnome) were compared between the two study groups. Participants/materials, setting, methods All male partners were normo-spermic according to standard semen analysis. ICSI results for a total of 7277 mature oocytes injected with spermatozoa processed with ZyMōt device and 5977 oocytes injected with spermatozoa processed by gradient-density centrifugation were compared. For the analysis of 1478 euploid embryo transfer outcomes, all cases included a single blastocyst transferred in a frozen-thaw cycle. Associated patient characteristics, including age and fertility diagnosis were collected. Main results and the role of chance A significantly higher fertilization rate was observed with ZyMōt compared to the gradient prepared sperm (83.4% vs 81.0%, P = 0.0004, respectively). The blastocyst rates (61.3%, vs 60.4%, P = 0.34) as well as the proportion of high-quality blastocysts were similar in both groups (P = 0.20). Euploidy rates were not different between all female age groups, including young egg donors (≤ 30y of age) to patients with advanced maternal age (>40 y of age). Microfluidic sperm selection did not improve clinical pregnancy rates after euploid embryo transfer (42.9% vs 41.4%; P = 0.58). Limitations, reasons for caution Although this is the largest study to date evaluating application of the ZyMōt device, the retrospective nature and cohort design limited the interpretation of our results. The patient population did not include male factor cases and sperm DFI data was not available. Embryo transfer outcomes were limited to clinical pregnancies. Wider implications of the findings The ZyMōt device offers a convenient alternative to standard sperm processing by density gradient method. Further studies are needed to fully determined the impact of ZyMōt on embryo quality and clinical outcomes in different age and diagnosis groups of patients; particularly for those with high DFI and poor IVF/ICSI outcomes. Trial registration number not applicable

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How this classification was reachedexpand

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.123
GPT teacher head0.383
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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