Risk prevention of different forms of gestational diabetes mellitus based on energy metabolism prior to diagnosis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a metabolic disease that seriously endangers the health of mothers and children. It is important to monitor GDM in real-time before diagnosis and to prevent it effectively. OBJECTIVE: GDM was divided into the second trimester diagnosed diabetes mellitus (GDM_24) and the third trimester diagnosed diabetes mellitus (GDM_30). The risk prediction of two types of GDM was performed in normal pregnant women at 11-13 and 16-19 weeks of pregnancy, respectively. METHODS: By stages, the K-W test was used to analyze the differences between basic information and energy metabolism factors, and multiple logistic regression was used to analyze the risk of energy metabolism factors and to correct the confounders with significant differences. RESULTS: For the GDM_24 group, each additional unit of oxygen consumption (VO2), carbon dioxide production, and resting energy expenditure (REE) increased the risk by 2.4%, 3.5%, 0.4%, and 2.1%, 2.6%, and 0.3%, respectively, at 11-13 and 16-19 weeks of pregnancy. For the GDM_30 group, each additional unit of VO2 and REE was associated with an increased risk of 2.3% and 0.3%, respectively, at 16-19 weeks of pregnancy. CONCLUSION: The risk of GDM_30 only appeared in pregnant women during 16-19 weeks of pregnancy, which may indicate that GDM_24 and GDM_30 have different pathogenesis.

Gestational diabetes mellitus prediction model: A risk factors analysis of pregnant women with gestational diabetes mellitus but have normal oral glucose tolerance test results in the second trimester of pregnancy.

BACKGROUND: Oral glucose tolerance test (OGTT) is a standard for the diagnosis of gestational diabetes mellitus (GDM). However, clinically, some cases with normal results were diagnosed as GDM in the third trimester. OBJECTIVE: To establish a risk model based on energy metabolism, epidemiology, and biochemistry that could predict the GDM pregnant women with normal OGTT results in the second trimester. METHODS: Qualitative and quantitative data were analyzed to find out the risk factors, and the binary logistic backward LR regression was used to establish the prediction model of each factor and comprehensive factor, respectively. RESULTS: The risk factors including the rest energy expenditure per kilogram of body weight, oxygen consumption per kilogram of body weight, if more than the weight gain criteria of the Institute of Medicine, the increase of body mass index between the second trimester and pre-pregnancy, and fasting blood glucose. By comparison, the comprehensive model had the best prediction performance, indicating that 85% of high-risk individuals were correctly classified. CONCLUSION: Energy metabolism, epidemiology, and biochemistry had better recognition ability for the GDM pregnant women with normal OGTT results in the second trimester. The addition of metabolic factors in the second trimester also improved the overall prediction performance.

Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm.

BACKGROUND: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors. OBJECTIVE: To establish models for early prediction and intervention of HDP. METHODS: This study used the three types of risk factors and support vector machine (SVM) to establish prediction models of HDP at different gestational weeks. RESULTS: The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28-34 weeks and ⩾ 35 weeks, it reached more than 92%. CONCLUSION: Multi-risk factors combined with dynamic gestational weeks' prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy.

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.

Novel SPEG variant cause centronuclear myopathy in China.

BACKGROUND: Centronuclear myopathy (CNM), a subtype of congenital myopathy (CM), is a group of clinical and genetically heterogeneous muscle disorders. Centronuclear myopathy is a kind of disease difficult to diagnose due to its genetic diversity. Since the discovery of the SPEG gene and disease-causing variants, only a few additional patients have been reported. METHODS: A radiograph test, ultrasonic test, and biochemical tests were applied to clinical diagnosis of CNM. We performed trio medical exome sequencing of the family and conservation analysis to identify variants. RESULTS: We report a pair of severe CNM twins with the same novel homozygous SPEG variant c. 8710A>G (p.Thr2904Ala) identified by clinical trio medical exome sequencing of the family and conservation analysis. The twins showed clinical symptoms of facial weakness, hypotonia, arthrogryposis, strephenopodia, patent ductus arteriosus, and pulmonary arterial hypertension. CONCLUSIONS: Our report expands the clinical and molecular repertoire of CNM and enriches the variant spectrum of the SPEG gene in the Chinese population and helps us further understand the pathogenesis of CNM.

Estimation of 8-Electrode Configuration for Recognition of Uterine Contraction with Electrohysterogram.

As the representative of electrical activity from uterine muscle, electrohysterogram (EHG) is recorded non-invasively by multiple electrodes positioned on the abdominal surface. The purpose of our paper is to estimate different electrode configurations for recognizing uterine contractions (UCs) with EHG signals. 8-electrode configuration was taken as an example to show our novel method with convolutional neural network (CNN) classification and score. The open accessed Icelandic 16-electrode EHG database was adopted in our study. With 8-electrode configuration, EHG signals corresponding to UCs and non-UCs were segmented and saved as image patches. The CNN was established and trained by thousands of EHG segments. The performance of CNN was evaluated by the area under curve (AUC) and accuracy of recognizing UCs and non-UCs. Seven different 8-electrode configurations were scored and ranked. It was found the 8-electrode configuration with 4 on the uterine fundus, 2 on the body and 2 on the cervix achieved the AUC of 0.766 and the highest score of 2.197. Among the configurations we have tried, it is concluded that the 8 electrodes in 4-2-2 configuration placed along the uterus as an upside-down pear could provide the most important information for recognition of UC based on our experiments.

Role of serum vitamin A and E in pregnancy.

Serum levels of vitamin A and E in early, middle and late pregnancy were analyzed to evaluate vitamin nutritional status in pregnancy, and provide guidance for pregnant women about vitamin supplements in pregnancy. In total, 28,023 serum samples were randomly selected from pregnant women in early, middle and late pregnancy between January 2013 and June 2014 in Beijing. High performance liquid chromatography (HPLC) method was used to determine the concentration of serum vitamin A and E in pregnancy. The concentration of serum vitamin A in early, middle and late pregnancy was 0.33±0.08, 0.37±0.09 and 0.33±0.15 mg/l, respectively, total abnormal rate was 25.31%, and deficiency (24.98%) was the main feature. The rate of deficiency in the early pregnancy (38.22%) was greater than that in late pregnancy (35.13%). The serum vitamin E in early, middle and late pregnancy was 9.10±2.47, 14.24±3.66 and 15.80±5.01 mg/l, respectively, total abnormal rate was 5.60%, and excess (5.37%) was the main feature. The excess rate in early pregnancy was at the lowest level (0.50%), and reached the highest level (15.32%) in late pregnancy. The serum levels of vitamin A and E are different during pregnancy. Generally, vitamin A is deficient and vitamin E is in excess. Therefore, monitoring the vitamin A and E levels, and strengthening perinatal education and providing guidance for pregnant women to supply vitamins rationally play important role in guaranteeing maternal and fetal safety.

Gaussian Modelling Characteristics of Peripheral Arterial Pulse: Difference between Measurements from the Three Trimesters of Healthy Pregnancy.

Arterial pulse wave analysis has been attempted to monitor the maternal physiological changes of circulatory system during pregnancy. This study aimed to quantify the difference of Gaussian modelling characteristics derived from radial pulses measured from the three trimesters of healthy pregnant women. Radial pulses were recorded from seventy pregnant women between gestational week 11-13, week 20-22, and then week 37-39. They were then normalized and decomposed into three independent Gaussian waves for deriving four key modelling characteristic parameters: including the peak time interval (T) and peak amplitude ratio (R) between the first and second Gaussian waves (T 1,2 and R 1,2), and their corresponding values between the first and third Gaussian waves (T 1,3 and R 1,3). Post hoc multiple comparisons after analysis of variance was then applied to study the within-subject differences in Gaussian modelling characteristics between the three trimesters. The key results were that T 1,2 and T 1,3 increased significantly (T 1,2: 12.8 ± 1.3 vs 13.2 ± 1.3, p < 0.05; T 1,3: 39.5 ± 4.3 vs 45.4 ± 5.1, p < 0.001), and R 1,3 decreased significantly from the first to second trimester (0.60 ± 0.15 vs 0.53 ± 0.11, p < 0.001). From the second to third trimester, T 1,2 decreased significantly (13.2 ± 1.3 vs 12.8 ± 1.2, p < 0.01), and T 1,3 and R 1,3 decreased slightly but nonsignificantly. Since larger T 1,2 and T 1,3 and smaller R 1,3 are associated with more compliant peripheral arteries, our results indicated that peripheral arteries become more compliant from the first to second trimester and then have a tendency of returning to baseline during normal pregnancy. In conclusion, this study has quantitatively demonstrated significant changes of Gaussian modelling characteristics derived from radial pulses at the three trimesters of normal pregnant women, suggesting that these modelling characteristics could be used as parameters in monitoring maternal physiological changes during normal pregnancy.

Arterial Pulse Waveform Characteristics Difference between the Three Trimesters of Healthy Pregnant Women.

During pregnancy, the pregnant mother undergoes significant physiological changes in order to accommodate the developing fetus. In recent years, arterial pulse wave has been widely used to reflect these physiological changes. The aim of this study was to investigate the changes of radial pulse and photoplethysmography (PPG) pulse waveform characteristic with gestational age in normal pregnant women. 40 pregnant women volunteers were recruited from February 2016 to September 2016 from the Haidian Maternal & Child Health Hospital in Beijing. Both radial pulses and PPG pulses were recorded simultaneously using a PowerLab data collection system at a sampling rate of 1000Hz for offline analysis. Their pulses were measured from each pregnant woman at three trimesters (first trimester between week 11-13; second trimester between week 20-22 and the third trimester between week 3739). Three waveform characteristics (total pulse area; pulse area1: the area before the notch position; pulse area2: the area after the notch position) were derived. The results showed that the total pulse area and pulse area2 from both radial and PPG pulses decreased significantly between two paired consecutive trimesters (all P<0.01, except the comparisons between the second and third trimesters for PPG pulses). In summary, this study has quantified the pulse waveform characteristic differences in terms of pulse areas between the three trimesters, providing useful scientific evidence to better understand the cardiovascular physiological changes during normal pregnancy.

Changes of Arterial Pulse Waveform Characteristics with Gestational Age during Normal Pregnancy.

Arterial pulse waveform analysis has been widely used to reflect physiological changes in the cardiovascular system. This study aimed to comprehensively investigate the changes of waveform characteristics of both photoplethysmographic (PPG) and radial pulses with gestational age during normal pregnancy. PPG and radial pulses were simultaneously recorded from 130 healthy pregnant women at seven gestational time points. After normalizing the arterial pulse waveforms, the abscissa of notch point, the total pulse area and the reflection index were extracted and compared between different measurement points and between the PPG and radial pulses using post-hoc multiple comparisons with Bonferrioni correction. The results showed that the effect of gestational age on all the three waveform characteristics was significant (all p < 0.001) after adjusting for maternal age, heart rate and blood pressures. All the three waveform characteristics demonstrated similar changing trends with gestational age, and they were all significantly different between the measurements from gestational week 12-15 and the others (all p < 0.05, except for the PPG total pulse area between the first and second measurement points). In conclusion, this study has comprehensively quantified similar changes of both PPG and radial pulse waveform characteristics with gestational age.

Effect of recombinant human endostatin on endometriosis in mice.

BACKGROUND: Direct and indirect evidences have suggested that angiogenesis is a prerequisite for the development of endometriosis. Aiming at offering experimental evidences for anti-angiogenesis therapy, we transplanted the eutopic endometrium from patient with endometriosis into the severe combined immunodeficiency disease (SCID) mice, to evaluate the effect of the endostatin on the growth and angiogenesis of the established endometriosis lesions in SCID mice model. METHODS: Eutopic endometrium of women with endometriosis was transplanted into the SCID mice. The mice were randomized into treatment (n = 10) and control groups (n = 10). Two weeks after the implantation of endometrium fragment, the treatment group was injected with recombinant human endostatin YH-16 into the peritoneal cavity (2 mgxkg(-1)xd(-1)), whereas the control group received equivalent volume of PBS (200 microl/d). The volume of endometriotic lesions in SCID mice was measured every three days, and all the treatment lasted for 14 days. Immunohistochemistry was used to determine microvessel density (MVD) and the expression of VEGF. The results were analyzed by t test and chi(2) test to value the treating effect. RESULTS: Compared with the control group, growth of endometriosis lesion was reduced in the mice treated with YH-16. Statistically significant differences in the volume and weight of the ectopic lesions were observed between the treatment and the control groups (P < 0.05). Microscopical examination showed that after being treated with YH-16, the volume of the endometrial tissues decreased, the glands depauperated, and the glandular epithelium partially degenerated. Necrotic debris was observed in the endometrial stroma. MVD and expression of VEGF in the treatment group were significantly lower than those in the control group (P < 0.05). CONCLUSIONS: Recombinant human endostatin affects the maintenance and growth of endometriotic tissues by inhibiting angiogenesis and reducing the expression of VEGF in ectopic lesion. The angiostatic agent may be promising as a therapy for endometriosis.