Background: The adverse effects of maternal diabetes on oocyte maturation and embryo development have been reported. Methods: In this study, we used time-lapse live cell imaging confocal microscopy to investigate the dynamic changes of ER and the effects of diabetes on the ER’s structural dynamics during oocyte maturation, fertilization and early embryo development. Results: We report that the ER first became remodeled into a dense ring around the developing MI spindle, and then surrounded the spindle during migration to the cortex. ER reorganization during mouse early embryo development was characterized by striking localization around the pronuclei in the equatorial section, in addition to larger areas of fluorescence deeper within the cytoplasm. In contrast, in diabetic mice, the ER displayed a significantly higher percentage of homogeneous distribution patterns throughout the entire ooplasm during oocyte maturation and early embryo development. In addition, a higher frequency of large ER aggregations was detected in GV oocytes and two cell embryos from diabetic mice. Conclusions: These results suggest that the diabetic condition adversely affects the ER distribution pattern during mouse oocyte maturation and early embryo development.

Synaptotagmin1, a calcium sensor for exocytosis, forms the 7S complex, or so-called SNARE protein complex, together with SNAP -25, syntaxin and synaptobrevin to mediate docking and fusion of synaptic vesicles to the plasma membrane of the nerve terminal. Here, we identified the unique localization, expression and function of Syt1 during mouse oocyte meiotic maturation by using confocal microscopy, western blotting, Morpholino-based knockdown and time-lapse live cell imaging. We showed that Syt1 expression was gradually increased during oocyte maturation. Syt1 was localized at the oocyte cortex from GV to MII stages and at the spindle poles in MI and MII phases, with one third of a signal-free zone at the oocyte cortex, where the chromosomes are located, which is similar to the distribution pattern of CGs from the pro-MI to MII stages. Knockdown of Syt1 resulted in pro-MI/MI arrest and PB1 extrusion decrease, with severely disrupted spindles and misaligned chromosomes. Knockdown of Syt1 also caused abnormal localization of γ-tubulin, which became redistributed into the cytoplasm. Chromosome spreading showed failure of homologous chromosome segregation. The spindle assembly checkpoint protein Bub3 was detected at the kinetochores even after 10 h of oocyte culture. Live cell imaging analysis revealed that knockdown of Syt1 resulted in abnormal spindles with various morphologies and chromosomes arrested at the pro-MI/MI stage. Defective spindles failed to support chromosome alignment along microtubules, which led to repetitive unsuccessful metaphase-anaphase transitions and failure of PB1 extrusion after extended culture. Taken together, we suggest that Syt1 may act as a MTOC-associated protein to play important roles in mouse oocyte spindle organization/stability, and that it is indispensable for the metaphase-anaphase transition to promote mouse oocyte meiotic maturation.

Background: Predicting clinical pregnancy following intrauterine insemination (IUI) remains challenging because of the complex interplay of multiple biological and treatment-related factors. Machine learning approaches may improve predictive accuracy compared with conventional statistical methods. This study aimed to compare the predictive performance of logistic regression (LR), random forest (RF), and multilayer perceptron (MLP) models and to explore their clinical interpretability. Methods: A total of 957 IUI cycles were randomly divided into a training set (80%) and an independent validation set (20%). Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set to address class imbalance. Model performance was evaluated using 10-fold cross-validation, and discrimination was assessed by area under the receiver operating characteristic curve (AUC). Model interpretability was examined using SHAP (Shapley Additive Explanations) analysis. Results: Across 10-fold cross-validation, all three models demonstrated modest and comparable discriminative ability. The MLP achieved a mean AUC of 0.547 (cross-validation mean AUC 0.560 ± 0.062), while RF and LR yielded mean AUC values of 0.549 and 0.541, respectively. Although overall discrimination was limited, the MLP showed relatively greater stability across folds. SHAP analysis consistently identified female age, infertility etiology, body weight, antral follicle count, estradiol, and treatment regimen as important predictors across models. Conclusion: The MLP model showed modest discriminative ability in predicting IUI clinical pregnancy and identified several relevant clinical features. While its predictive performance is limited, the model may contribute to risk stratification and exploratory individualized assessment in assisted reproduction.