Gitelman syndrome in pregnancy: a case series.

OBJECTIVE: To evaluate maternal and fetal outcomes in women with Gitelman syndrome (GS). METHODS: Retrospective analysis of the clinical data of five patients with the clinical diagnosis of GS during pregnancy, who were admitted to Beijing Shijitan Hospital, Capital Medical University between 2013 and 2019, was conducted. RESULTS: Five women with GS during pregnancy who finally gave birth to a total of eight newborns have been included. Three cases were primiparas and two cases were multiparas. Two cases were diagnosed before pregnancy and three cases were diagnosed in first or second trimester. The primary treatment was oral or intravenous electrolytes supplement. Three patients delivered through the vagina, and shoulder dystocia occurred in one patient. Two patients delivered by cesarean section, with one because of symptom of limb weakness during the course of labor and the other owing to gestational diabetes with fetal macrosomia. Postpartum hemorrhage and urinary retention were not reported in these cases. In perinatal period all the infants had good outcome. The children, aged between six months and five years, were healthy and well-developed during follow-up. CONCLUSION: The maternal and perinatal outcome is usually favorable. We should pay attention to electrolyte examination in the first trimester in order to diagnose and manage the GS efficiently. Well-controlled patients with Gitelman syndrome can deliver through the vagina.

A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data.

PURPOSE: Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions' diagnostic efficiency. METHODS: After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set. RESULTS: Selecting features with around 800 genes for training, the R 2-score of a 10-fold CV of training data can reach 96.38%, and the R 2-score of test data can reach 83.3%. CONCLUSION: These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors' location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

Low temperature methanation of CO2 over an amorphous cobalt-based catalyst.

CO2 methanation is an important reaction in CO2 valorization. Because of the high kinetic barriers, the reaction usually needs to proceed at higher temperature (>300 °C). High-efficiency CO2 methanation at low temperature (<200 °C) is an interesting topic, and only several noble metal catalysts were reported to achieve this goal. Currently, design of cheap metal catalysts that can effectively accelerate this reaction at low temperature is still a challenge. In this work, we found that the amorphous Co-Zr0.1-B-O catalyst could catalyze the reaction at above 140 °C. The activity of the catalyst at 180 °C reached 10.7 mmolCO2 gcat -1 h-1, which is comparable to or even higher than that of some noble metal catalysts under similar conditions. The Zr promoter in this work had the highest promoting factor to date among the catalysts for CO2 methanation. As far as we know, this is the first report of an amorphous transition metal catalyst that could effectively accelerate CO2 methanation. The outstanding performance of the catalyst could be ascribed to two aspects. The amorphous nature of the catalyst offered abundant surface defects and intrinsic active sites. On the other hand, the Zr promoter could enlarge the surface area of the catalyst, enrich the Co atoms on the catalyst surface, and tune the valence state of the atoms at the catalyst surface. The reaction mechanism was proposed based on the control experiments.