Ontology: Footstone for Strong Artificial Intelligence.

In the past ten years, the application of artificial intelligence (AI) in biomedicine has increased rapidly, which roots in the rapid growth of biomedicine data, the improvement of computing performance, and the development of deep learning methods. At present, there are great difficulties in front of AI for solving complex and comprehensive medical problems. Ontology can play an important role in how to make machines have stronger intelligence and has wider applications in the medical field. By using ontologies, (meta) data can be standardized so that data quality is improved and more data analysis methods can be introduced, data integration can be supported by the semantics relationships which are specified in ontologies, and effective logic expression in nature language can be better understood by machine. This can be a pathway to stronger AI. Under this circumstance, the Chinese Conference on Biomedical Ontology and Terminology was held in Beijing in autumn 2019, with the theme "Making Machine Understand Data". The success of this conference further improves the development of ontology in the field of biomedical information in China, and will promote the integration of Chinese ontology research and application with the international standards and the findability, accessibility, interoperability, and reusability(FAIR) Data Principle.

Ontology: Footstone for Strong Artificial Intelligence.

In the past ten years, the application of artificial intelligence (AI) in biomedicine has increased rapidly, which roots in the rapid growth of biomedicine data, the improvement of computing performance, and the development of deep learning methods. At present, there are great difficulties in front of AI for solving complex and comprehensive medical problems. Ontology can play an important role in how to make machines have stronger intelligence and has wider applications in the medical field. By using ontologies, (meta) data can be standardized so that data quality is improved and more data analysis methods can be introduced, data integration can be supported by the semantics relationships which are specified in ontologies, and effective logic expression in nature language can be better understood by machine. This can be a pathway to stronger AI. Under this circumstance, the Chinese Conference on Biomedical Ontology and Terminology was held in Beijing in autumn 2019, with the theme "Making Machine Understand Data". The success of this conference further improves the development of ontology in the field of biomedical information in China, and will promote the integration of Chinese ontology research and application with the international standards and the findability, accessibility, interoperability, and reusability(FAIR) Data Principle.

Prevalence of Prediabetes Risk in Offspring Born to Mothers with Hyperandrogenism.

BACKGROUND: Excessive androgen exposure during pregnancy has been suggested to induce diabetic phenotypes in offspring in animal models. The aim of this study was to investigate whether pregestational maternal hyperandrogenism in human influenced the glucose metabolism in offspring via epigenetic memory from mother's oocyte to child's somatic cells. METHODS: Of 1782 reproductive-aged women detected pregestational serum androgen, 1406 were pregnant between 2005 and 2010. Of 1198 women who delivered, 1116 eligible mothers (147 with hyperandrogenism and 969 normal) were recruited. 1216 children (156 children born to mothers with hyperandrogenism and 1060 born to normal mother) were followed up their glycometabolism in mean age of 5years. Imprinting genes of oocyte from mothers and lymphocytes from children were examined. A pregestational hyperandrogenism rat model was also established. FINDINGS: Children born to women with hyperandrogenism showed increased serum fasting glucose and insulin levels, and were more prone to prediabetes (adjusted RR: 3.98 (95%CI 1.16-13.58)). Oocytes from women with hyperandrogenism showed increased insulin-like growth factor 2 (IGF2) expression. Lymphocytes from their children also showed increased IGF2 expression and decreased IGF2 methylation. Treatment of human oocytes with dihydrotestosterone upregulated IGF2 and downregulated DNMT3a levels. In rat, pregestational hyperandrogenism induced diabetic phenotypes and impaired insulin secretion in offspring. In consistent with the findings in human, hyperandrogenism also increased Igf2 expression and decreased DNMT3a in rat oocytes. Importantly, the same altered methylation signatures of Igf2 were identified in the offspring pancreatic islets. INTERPRETATION: Pregestational hyperandrogenism may predispose offspring to glucose metabolism disorder via epigenetic oocyte inheritance. Clinical trial registry no.: ChiCTR-OCC-14004537; www.chictr.org.

Down-regulation of S100A11, a calcium-binding protein, in human endometrium may cause reproductive failure.

BACKGROUND: Low expression levels of S100A11 proteins were demonstrated in the placental villous tissue of patients with early pregnancy loss, and S100A11 is a Ca2+-binding protein that interprets the calcium fluctuations and elicits various cellular responses. OBJECTIVES: The objective of the study was to determine S100A11 expression in human endometrium and its roles in endometrial receptivity and embryo implantation. METHODS: S100A11 expression in human endometrium was analyzed using quantitative RT-PCR, Western blot, and immunohistochemical techniques. The effects of S100A11 on embryo implantation were examined using in vivo mouse model, and JAr (a human choriocarcinoma cell line) spheroid attachment assays. The effects of endometrial S100A11 on factors related to endometrial receptivity and immune responses were examined. Using a fluorescence method, we examined the changes in cytosolic Ca2+ and Ca2+ release from intracellular stores in epidermal growth factor (EGF)-treated endometrial cells transfected with or without S100A11 small interfering RNA. RESULTS: S100A11 was expressed in human endometrium. S100A11 protein levels were significantly lower in endometrium of women with failed pregnancy than that in women with successful pregnancy outcomes. The knockdown of endometrial S100A11 not only reduced embryo implantation rate in mouse but also had adverse effects on the expression of factors related to endometrial receptivity and immune responses in human endometrial cells. Immunofluorescence analysis showed that S100A11 proteins were mainly localized in endoplasmic reticulum. The EGF up-regulated endometrial S100A11 expression and promoted the Ca2+ uptake and release from Ca2+ stores, which was inhibited by the knockdown of S100A11. CONCLUSIONS: Endometrial S100A11 is a crucial intermediator in EGF-stimulated embryo adhesion, endometrium receptivity, and immunotolerance via affecting Ca2+ uptake and release from intracellular Ca2+ stores. Down-regulation of S100A11 may cause reproductive failure.