Cell types have been established during organogenesis based on early mouse embryos. However, our understanding of cell types and molecular mechanisms in the early embryo development of Mongolian sheep has been hampered. This study presents the first comprehensive single-cell transcriptomic characterization at E16 in Ujumqin sheep and Hulunbuir short-tailed sheep. Thirteen major cell types were identified at E16 in Ujumqin sheep, and eight major cell types were identified at E16 in Hulunbuir short-tailed sheep. Function enrichment analysis showed that several pathways were significantly enriched in the TGF-beta signaling pathway, the Hippo signaling pathway, the platelet activation pathway, the riboflavin metabolism pathway, the Wnt signaling pathway, regulation of the actin cytoskeleton, and the insulin signaling pathway in the notochord cluster. Glutathione metabolism, glyoxylate, and dicarboxylate metabolism, the citrate cycle, thyroid hormone synthesis, pyruvate metabolism, cysteine and methionine metabolism, thermogenesis, and the VEGF signaling pathway were significantly enriched in the spinal cord cluster. Steroid biosynthesis, riboflavin metabolism, the cell cycle, the Hippo signaling pathway, the Hedgehog signaling pathway, the FoxO signaling pathway, the JAK-STAT signaling pathway, and the Wnt signaling pathway were significantly enriched in the paraxial mesoderm cluster. The notochord cluster, spinal cord cluster, and paraxial mesoderm cluster were found to be highly associated with tail development. Pseudo-time analysis demonstrated that the mesenchyme can translate to the notochord in Ujumqin sheep. Molecular assays revealed that the Hippo signaling pathway was enriched in Ujumqin sheep. This comprehensive single-cell map revealed previously unrecognized signaling pathways that will further our understanding of the mechanism of short-tailed sheep formation.

Threatened abortion, a common pregnancy complication that often leading to abortion, is hard to predict due to its non-specific symptoms and difficulty in differentiating from other early pregnancy bleeding causes. Current diagnostic methods like serial ultrasounds and clinical monitoring are time-consuming and lack timeliness. To fill the gap in using advanced analytics for early detection and risk stratification, this study develops a machine learning (ML) model based on routine blood data to better predict threatened abortion, providing a reference for early detection and intervention. In this study, we collected medical records from January 2022 to March 2024. We analyzed data from 1764 patients with threatened abortion and 1489 healthy controls. Blood test data of all participants were gathered. The Z-score normalization technique was applied to standardize blood routine indicators. This reduced the influence of outliers and noise. During hyperparameter optimization, ‘class_weight="balanced"’ was set to handle sample imbalance. The screening data was partitioned into a training set of 2928 cases (including the validation set) and a test set of 325 cases at an 8:1:1 ratio. Python was used to facilitate data transformation. Eight different ML algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GBM), Extreme Gradient Boosting (XGB), Deep Neural Network (DNN), Decision Tree (DT) and Naive Bayes (NB)—were used to construct a threatened abortion prediction model. The prediction performances of the ML models were evaluated by calculating the area under the curve (AUC) values. We used the SHapley Additive exPlanation (SHAP) method to explain the models. Comparatively, the DNN model showed the highest predictive performance among the eight models, with the highest AUC value of 96.76% and top metrics for accuracy (91.88%), specificity (91.62%), sensitivity (92.11%), and F1 score (92.48%). SHAP analysis identified Red Cell Distribution Width - Standard Deviation (RDW-SD), Platelet Distribution Width (PDW), Mean Platelet Volume (MPV), Red Cell Distribution Width - Coefficient of Variation (RDW-CV), Absolute Basophil Count (BAS#), Platelet Count (PLT), Mean Corpuscular Hemoglobin Concentration (MCHC) and Lymphocyte Percentage (LYM) as the most influential features in predicting threatened abortion, with PDW, RDW-CV, BAS#, PLT, MCHC and LYM positively contributing to the prediction, whereas RDW-SD and MPV had negative contributions. Our research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of ML algorithms in detecting threatened abortion. This algorithm is expected to analyse routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection of this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of abortion. However, before the model can be translated into routine clinical applications, more extensive validation studies are still needed.

Dosage compensation is a mechanism first proposed by Susumu Ohno, whereby X inactivation balances X gene output between males (XY) and females (XX), while X upregulation balances X genes with autosomal gene output. These mechanisms have been actively studied in Drosophila and mice, but research regarding them lags behind in domestic species. It is unclear how the X chromosome is regulated in the sheep male germline. To address this, using single-cell RNA sequencing, we analyzed testes in three important developmental stages of sheep. We observed that the total RNA per cell from X and autosomes peaked in SSCs and spermatogonia and was then reduced in early spermatocytes. Furthermore, we counted the detected reads per gene in each cell type for X and autosomes. In cells experiencing dose compensation, close proximity to MSL (male-specific lethal), which is regulated the active X chromosome and was observed. Our results suggest that there is no dose compensation in the pre-meiotic germ cells of sheep testes and, in addition, MSL1 and MSL2 are expressed in early germ cells and involved in regulating mammalian X-chromosome inactivation and activation.

Background and Objective: Threatened abortion, a common pregnancy complication often leading to abortion, is hard to predict due to its non-specific symptoms and difficulty in differentiating from other early pregnancy bleeding causes. Current diagnostic methods like serial ultrasounds and clinical monitoring are time-consuming and lack timeliness. To fill the gap in using advanced analytics for early detection and risk stratification, this study develops a machine learning model based on routine blood data to better predict threatened abortion, providing a reference for early detection and intervention. Methods In this study, we collected medical records from January 2022 to March 2024 and analyzed data from 1764 patients with threatened abortion and 1489 healthy controls blood test data of all participants were gathered, and the Z-score normalization technique was applied to standardize blood routine indicators while reducing the influence of outliers and noise. To handle sample imbalance, 'class_weight="balanced"' was set during hyperparameter optimization. The screening data was partitioned into a training set of 2928 cases (including the validation set) and a test set of 325 cases at an 8:1:1 ratio, with Python (version 3.7.0) facilitating data transformation. Eight different machine learning algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GBM), Extreme Gradient Boosting (XGB), Deep Neural Network (DNN), Conditional Random Field (CRF), and Naive Bayes (NB)—were used to construct a threatened abortion prediction model. The prediction performances of the eight machine learning (ML) models were evaluated by calculating the area under the curve (AUC) values. We used the SHapley Additive exPlanation (SHAP) method to explain the models. Results Comparatively, the DNN model showed the highest predictive performance among the eight models, with the highest AUC value of 94.51% and top metrics for accuracy (87.09%), specificity (84.09%), sensitivity (89.18%), and F1 score (89.18%). SHAP analysis identified platelet distribution width (PDW), red cell distribution width (RDW-SD), mean platelet volume (MPV), platelet count (PLT), lymphocyte percentage (LYM%), and absolute basophil count (BAS#) as the most influential features in predicting threatened abortion, with PDW, PLT, and LYM% positively contributing to the prediction, while RDW-SD and MPV had negative contributions. Conclusions Our research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of machine learning algorithms in detecting threatened abortion. This algorithm is expected to analyze routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection method for this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of abortion. However, before the model can be translated into routine clinical applications, more extensive validation studies are still needed.