Objective::This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods::A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test. Results::The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t=-3.55 , P=0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t= 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t= 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t=-9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t= -2.74, P= 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion::The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.

Objective::This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods::A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test. Results::The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t=-3.55 , P=0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t= 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t= 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t=-9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t= -2.74, P= 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion::The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.

Objective Essential polyunsaturated fatty acids(PUFA) can not been synthesized by the body-self.Serum triglycerides n-6 and n-3 PUFAs directly or indirectly reflect the corresponding unsaturated fatty acids intake from meals.This study was to investigate the relationship of serum triglycerides polyunsaturated fatty acid composition with the ratio of n-6 to n-3 PUFA(n-6/n-3 PUFA) and magnetic resonance imaging measured intra-abdominal fat(MRI-IAF) with other body fat parameters of patients with metabolic syndrome.Methods Thirty-six patients with metabolic syndrome and 41 healthy controls were enrolled in this investigation.The relevance of serum triglycerides polyunsaturated fatty acids with MRI-IAF was observed.A stepwise regression analysis was applied to determine which kind of triglycerides polyunsaturated fatty acid could predict MRI-IAF,waist circumference and body mass index (BMI) more potent.Results In the metabolic syndrome group,blood sugar,lipid profiles,blood pressure,visceral fat accumulation-related parameters and serum triglycerides polyunsaturated fatty acid composition was worse than those in the control group.Compared with the healthy controls,n-6/n-3 PUFA was significantly increased(t=8.564,P＜0.05),although C18∶3 n-3,C20∶5 n-3(EPA),C22∶6 n-3(DHA) and n-3PUFA were significantly declined(t=-2.920,-7.034,all P＜0.05) in metabolic syndrome group.The difference of n-6 PUFA showed no statistically significant difference(t=-0.957,-1.494,P＞0.05).n-6/n-3 PUFA,n-3 PUFA,EPA and DHA were associated with MRI-IAF(r=-0.377,0.565,all P＜0.05); n-6/n-3 PUFA was correlated with the waist circumference(r=0.400,P=0.016) and BMI(r=0.357,P=0.033),while n6 PUFA showed no correlation with body fat parameters.N-6/n-3 PUFA was more potent to predict MRIIAF,waist circumference and BMI(adjusted R2=0.102,0.299,all P＜0.05) than other polyunsaturated fatty acids.Conclusions The ratio of n-6/n-3 PUFA and n-3 PUFA may be positively correlated with EPA and DHA could be inversely associated with MRI-IAF and other body fat-related parameters in patients with metabolic syndrome,while n-6 PUFA did not show such a relationship.The ratio of n-6/n-3 PUFA might be more potent to predict MRI-IAF and other body fat-related parameters.

A facial allotransplanted patient presented hyperglycemia with blood glucose ranged 14. 3 -33. 3 mmol/L after receiving immunosuppressive drugs and glucocorticoids. To control the blood glucose level, the patient was treated with two subcutaneous doses of 10 U human neutral protamine hagedorn (NPH) insulin, and the fasting glucose level came down to 3. 6 - 9. 4 mmol/L. Then the continuous subcutaneous infusion of insulin aspart ( Novo Industri) was administrated (from 96 to 21 U/d) , and the fasting blood glucose levels were 3. 9 -4. 6 mmol/L. With oral administration of Metformin and Repaglinide, the fasting blood glucose was maintained to 4. 3 -5.9 mmol/L. With these medications, the blood glucose level of the patient was under good control and the acute and chronic complications of hyperglycemia were effectively prevented.