Different Timings of Early Cumulus Cells Removal Have Different Multiple Pronuclei Rates in Human in Vitro Fertilization

Background: Cumulus cells removal 4 h post-insemination has a significantly higher multiple pronuclei (MPN) rate than cumulus cells removal 20 h post-insemination. And, cumulus cells removal 6 h post-insemination has a significantly lower MPN rate than cumulus cells removal 20 h post-insemination. However, it remains unclear whether the different timings of early cumulus cells removal, such as the timings of 4, 5 and 6 h post-insemination, have significantly different MPN rates.Methods: This was a retrospective study. The included cycles were early cumulus cells removal cycles (n=752) at our center from January 2015 to August 2020. The included cycles were divided into two groups according to whether MPN exist (MPN=0% and MPN>0%). The patient and cycle stimulation characteristics of the two groups were compared. Binary logistic regression was performed to investigate the correlation between the timing of early cumulus cells removal and MPN. The cohort study was also performed to compare the patient characteristics, cycle stimulation characteristics, fertilization outcomes, and cultivation outcomes.Results: In the population of our study, the timing of early cumulus cells removal had a significant effect on the MPN. The cumulus cells removal ≤4 h post-insemination group had a high MPN rate, and the 5.5<time≤6 h group had a high fertilization failure rate. However, 2PN rate was not significantly different among the different timings of early cumulus cells removal. In addition, the ≤4 h post-insemination group had a high grade 1–2 embryo rate at day 3.Conclusion(s): Even if all the timings of cumulus cells removal are early, the different timings of early cumulus cells removal still have a significant effect on the MPN.

Holographic virtual staining of individual biological cells

Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and enable clinicians and biologists to differentiate between various cell organelles. However, cell staining is not always allowed in certain medical procedures. In other cases, staining may be time-consuming or expensive to implement. Staining protocols may be operator-sensitive, and hence may lead to varying analytical results, as well as cause artificial imaging artifacts or false heterogeneity. We present a deep-learning approach, called HoloStain, which converts images of isolated biological cells acquired without staining by holographic microscopy to their virtually stained images. We demonstrate this approach for human sperm cells, as there is a well-established protocol and global standardization for characterizing the morphology of stained human sperm cells for fertility evaluation, but, on the other hand, staining might be cytotoxic and thus is not allowed during human in vitro fertilization (IVF). After a training process, the deep neural network can take images of unseen sperm cells retrieved from holograms acquired without staining and convert them to their stainlike images. We obtained a fivefold recall improvement in the analysis results, demonstrating the advantage of using virtual staining for sperm cell analysis. With the introduction of simple holographic imaging methods in clinical settings, the proposed method has a great potential to become a common practice in human IVF procedures, as well as to significantly simplify and radically change other cell analyses and techniques such as imaging flow cytometry.