Reproductive biomedicine online最新文献

Adenomyosis is an important clinical condition with uncertain prevalence, and clinical focus on adenomyosis in patients undergoing assisted reproductive technology (ART) has increased during recent years. Recognizing the limited clinical knowledge on the impact of adenomyosis on ART outcomes, the First Lugano Adenomyosis Workshop was a symposium involving experts in the field of adenomyosis, covering basic research, imaging, surgery and infertility to highlight current advances and future research areas over a wide range of topics related to adenomyosis. Adenomyosis is characterized by altered oestrogen and progesterone signalling pathways. Although the criteria of the Morphological Uterus Sonographic Assessment (MUSA) Consortium apply to patients with infertility, the presence of direct signs and localization in the different myometrial layers, particularly the inner myometrium, need more focus. In addition to the MUSA criteria, clinical symptoms and the magnitude of uterine enlargement should also be considered. Whilst pre-treatment with gonadotrophin-releasing hormone agonist with or without an aromatase inhibitor in frozen embryo transfer cycles seems promising, many issues related to therapy remain unanswered. During the Workshop, therapeutic progress over the past decades as well as novel insights were presented and discussed. The role of this opinion paper is to stimulate discussion and spark further interest in adenomyosis and the role of adenomyosis in infertility.

Research question: Can a biomimetic microfluidic sperm sorter isolate motile sperm while minimizing DNA damage in comparison with density gradient centrifugation (DGC)?

Design: This was a two-phase study of 61 men, consisting of a proof-of-concept study with 21 donated semen samples in a university research laboratory, followed by a diagnostic andrology study with 40 consenting patients who presented at a fertility clinic for semen diagnostics. Each sample was split to perform DGC and microfluidic sperm selection (one-step sperm selection with 15 min of incubation) side-by-side. Outcomes evaluated included concentration, progressive motility, and DNA fragmentation index (DFI) of raw semen, and sperm isolated using DGC and the microfluidic device. Results were analysed using Friedman's test for non-parametric data (significant when P < 0.05). DFI values were assessed by sperm chromatin dispersion assay.

Results: Sperm isolated using DGC and the microfluidic device showed improved DFI values and motility compared with the raw semen sample in both cohorts. However, the microfluidic device was significantly better than DGC at reducing DFI values in both the proof-of-concept study (P = 0.012) and the diagnostic andrology study (P < 0.001). Progressive motility was significantly higher for sperm isolated using the microfluidic device in the proof-of-concept study (P = 0.0061) but not the diagnostic andrology study. Sperm concentration was significantly lower for samples isolated using the microfluidic device compared with DGC for both cohorts (P < 0.001).

Conclusions: Channel-based biomimetic sperm selection can passively select motile sperm with low DNA fragmentation. When compared with DGC, this method isolates fewer sperm but with a higher proportion of progressively motile cells and greater DNA integrity.

Research question: Can machine learning tools predict the number of metaphase II (MII) oocytes and trigger day at the start of the ovarian stimulation cycle?

Design: A multicentre, retrospective study including 56,490 ovarian stimulation cycles (primary dataset) was carried out between 2020 and 2022 for analysis and feature selection. Of these, 13,090 were used to develop machine learning models for trigger day and the number of MII prediction, and another 5103 ovarian stimulation cycles (clinical validation dataset) from 2023 for clinical validation. Machine learning algorithms using deep learning were developed using optimal features from the primary dataset based on correlation.

Results: A tool with two novel progressive machine learning algorithms using deep learning was able to predict the trigger day and number of MII oocytes: mean absolute error 1.60 (95% CI 1.56 to 1.64) and 3.75 (95% CI 3.65 to 3.86), respectively. The R² value for the algorithm to predict the number of MII in the interquartile (Q3-Q1/P75-P25) range was 0.88; the entire dataset was 0.70 after removing the outliers at the planning phase of the stimulation cycle, which shows high accuracy. The interquartile root mean square error was 1.10 and 0.66 for the trigger day and the number of oocytes algorithm, respectively.

Conclusion: The tool using deep learning algorithms has high prediction power for trigger day and number of MII outcomes, and can be retrieved from patients at the start of the ovarian stimulation cycle; however, inclusion of more data and validation from different clinics are needed.