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Journal of Zhejiang University SCIENCE B

ISSN 1673-1581(Print), 1862-1783(Online), Monthly

Reduced glycodeoxycholic acid levels are associated with negative clinical outcomes of gestational diabetes mellitus

Abstract: Gestational diabetes mellitus (GDM) is characterized by glycemia and insulin disorders. Bile acids (BAs) have emerged as vital signaling molecules in glucose metabolic regulation. BA change in GDM is still unclear, which exerts great significance to illustrate the change of BAs in GDM. GDM patients and normal pregnant women were enrolled during the oral glucose tolerance test (OGTT) screening period. Fasting serums were sampled for the measurement of BAs. BA metabolism profiles were analyzed in both pregnant women with GDM and those with normal glucose tolerance (NGT). Delivery characteristics, delivery gestational age, and infant birthweight were extracted from medical records. GDM patients presented distinctive features compared with NGT patients, including higher body mass index (BMI), elevated serum glucose concentration, raised insulin (both fasting and OGTT), and increased hemoglobin A1c (HbA1c) levels. Higher homeostasis model assessment of insulin resistance (HOMA-IR) and decreased β-cell compensation (i.e., oral disposition index (DIo)) were also prevalent in this group. Total BAs (TBAs) remained stable, but glycodeoxycholic acid (GDCA) and taurodeoxycholic acid (TDCA) levels declined significantly in GDM. GDCA was inversely correlated with HOMA-IR and positively correlated with DIo. No obvious differences in clinical outcome between the GDM and NGT groups were observed. However, GDM patients with high HOMA-IR and low DIo tended to have a higher cesarean delivery rate and younger delivery gestational age. In conclusion, GDCA provides a valuable biomarker to evaluate HOMA-IR and DIo, and decreased GDCA levels predict poorer clinical outcomes for GDM.

Key words: Gestational diabetes mellitus (GDM); Bile acid; Insulin resistance; β-Cell compensation

Chinese Summary  <23> 基于振动的超高速碰撞识别与定位

包姣1,刘力夫2,曹九稳2,3
1成都工业学院计算机工程学院,中国成都市,611730
2杭州电子科技大学浙江省机器学习与健康国际合作基地,中国杭州市,310018
3杭州电子科技大学人工智能研究院,中国杭州市,310018
摘要:超高速碰撞(HVI)振动源识别与定位在载人航天器防护、机床碰撞损伤检测与定位等领域有着广泛应用。本文研究了基于同步压缩变换(SST)和纹理颜色分布(TCD)的冲击图像HVI源识别和定位算法。提出基于SST和TCD图像特征融合的HVI图像表示方法。为实现更精确的检测和定位,通过关联和评估样本标签与特征维度之间的相似性,获得最优选择性特征OSSST+TCD。将常用的分类和回归模型通过投票和堆叠融合,实现最终的检测和定位。基于所采集的3种高速子弹撞击铝合金板产生的HVI数据,验证了所提算法的有效性。实验结果表明本文提出的HVI识别与定位算法具有更高精准度。最后基于传感器分布,提出一种精确的四圆质心定位算法用于HVI源坐标定位。

关键词组:集成学习;同步压缩变换;灰度共生矩阵;图像熵;距离估计


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DOI:

10.1631/jzus.B2000483

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On-line Access:

2021-03-12

Received:

2020-08-18

Revision Accepted:

2021-01-03

Crosschecked:

2021-02-22

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