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2. Correlation of adiponectin, body mass index and waist-toheight ratio with the risk of developing gestational diabetes mellitus in hypertensive patients.

Author

Wei Jiang, Mingyan Sheng, Changdong Sun, and Zhipeng He

Subjects
*GESTATIONAL diabetes, *BODY mass index, *HYPERTENSION, *ADIPONECTIN, *GLYCOSYLATED hemoglobin, *LDL cholesterol
Abstract

Purpose: To analyze the effects of abnormalities in adiponectin (APN), body mass index (BMI) and waist height ratio (WHtR) on the risk of a combination of gestational diabetes mellitus (GDM) and hypertensive disorders complicating pregnancy (HDCP). Methods: The clinical data of 62 patients with GDM who visited Ningbo Beilun District People's Hospital between January 2018 and June 2020 were assigned to the GDM group, and the clinical data of 38 patients with GDM and HDCP during the same period were assigned to the combination group. Blood lipid levels were measured with a fully automated biochemical analyzer. APN level was determined using double antibody sandwich enzyme immunoassay kit. Results: The combination group had higher BMI, WHtR, family history of diabetes and hypertension, levels of hyperlipidemia, triglycerides (TG), as well as very low-density lipoprotein cholesterol (VLDL-c), and lower APN levels than the GDM group (p < 0.05) Multifactorial logistic regression results revealed that BMI, WHtR, APN, TG and VLDL-c levels may be risk factors for the occurrence of a combination of HDCP and GDM (OR > 1, p < 0.05). Conclusion: BMI, WHtR and APN are closely related to the risk of HDCP in patients with GDM, Thus, these indicators may be used to predict, evaluate and diagnose the disease. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Integrative network analysis identifies an immune-based prognostic signature as the determinant for the mesenchymal subtype in epithelial ovarian cancer

Author

Xiaoyan Lu, Mingyan Sheng, Peng Shu, Xingguo Zhang, Ni Shanshan, B. A. Ashok Reddy, and Haofei Tong

Subjects
Adult, Oncology, China, medicine.medical_specialty, Epithelial-Mesenchymal Transition, Drug resistance, Carcinoma, Ovarian Epithelial, 03 medical and health sciences, 0302 clinical medicine, Immune system, Internal medicine, Humans, Medicine, Epithelial ovarian cancer, 030212 general & internal medicine, Prospective cohort study, Aged, Retrospective Studies, Aged, 80 and over, Prognostic signature, business.industry, Gene Expression Profiling, Mesenchymal stem cell, General Medicine, Middle Aged, Prognosis, medicine.disease, Phenotype, Gene Expression Regulation, Neoplastic, 030220 oncology & carcinogenesis, Female, business, Ovarian cancer
Abstract

BACKGROUND Epithelial ovarian cancer (EOC) has been classified into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop an immune-based prognostic signature by incorporating molecular subtypes for EOC patients. METHODS The gene expression profiles of EOC samples were collected from seven public datasets as well as an internal retrospective validation cohort, containing 1192 EOC patients. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for EOC (IPSEOC). The signature was trained and validated in eight independent datasets. RESULTS Seven immune genes were identified as key regulators of the mesenchymal subtype and were used to construct the IPSEOC. The IPSEOC significantly divided patients into high- and low-risk groups in discovery (OS: P

Published
2020
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