Objective:To detect the biological markers related to Alzheimer's disease(AD)in the peripheral blood of AD patients,and to explore the activities and levels of the antioxidant function indexes and the expressions of related genes and proteins in the blood of AD patients and the changes after intervention of selenium-rich food.Methods:The Mini-Mental State Examination(MMSE)combined with electroencephalogram or brain CT and clinician diagnosis were used for screening AD.Fifty-six elderly patients with AD aged 75-90 years old were selected.Among them,28 cases were selected as normal diet group for AD(AD group),and 28 cases were selected as dietary selenium intervention group(Se-AD group).The patients in Se-AD group were given daily dietary selenium supplementation(increaseing dietary selenium by 15-20 μg per day)for 3 months.Meanwhile,30 people with the same age were selected as healthy control group.The activities of serum superoxide dismutase(SOD),cholinesterase(CHE),and glutathione peroxidase(GSH-Px)and the levels of serum malondialdehyde(MDA),homocysteine(Hcy),and nitric oxide(NO)as well as reagent kit the levels of serum β-amyloid protein(Aβ),and microtubule-associated protein(Tau)and phosphorylated microtubule-associated protein(p-Tau)of the subjects in various groups were detected by and enzyme-linked immunosorbent assay(ELISA)method;the expression levels of apolipoprotein E4(ApoE4),presenilin 1(PS1),presenilin 2(PS2),cysteinyl aspartate specific proteinase 3(Caspase3),sorting associated protein receptor 1(SORL1),β-site amyloid precursor protein cleaving enzyme 1(BACE1),hypoxia-inducible factor 1(HIF1),nuclear factor-kappa B(NF-κB),β-amyloid precursor protein(APP),protein kinase C(PKC),and Aβ mRNA in peripheral blood of the subjects various groups were detected by real-time fluorescence quantitative PCR(RT-qPCR)method.Results:Compared with healthy control group,the serum SOD activities of the patients in Se-AD group and AD group were significantly decreased(P＜0.05),while serum CHE activity and the levels of MDA and Hcy were significantly increased(P＜0.05);the serum GSH-Px activity of the patients in AD group was significantly decreased(P＜0.05),and the level of NO was significantly increased(P＜0.05).Compared with Se-AD group,serum CHE activity and the level of Hcy of the patients in AD group were significantly increased(P＜0.05).The expression levels of ApoE4,PS1,Caspase3,BACE1,NF-κB and APP mRNA of the patients in Se-AD group and AD group were significantly increased(P＜0.05),and the expression levels of PKC mRNA were significantly decreased(P＜0.05);the expression level of PS2 mRNA of the patients in AD group was significantly increased(P＜0.05),and the expression levels of Aβ mRNA of the patients in Se-AD group and AD group were significantly increased(P＜0.05).Conclusion:The activities of serum SOD,GSH-Px and CHE and the levels of MDA,Hcy and NO,the levels of Aβ,Tau and p-Tau proteins,and the expression levels of ApoE4,PS1,Caspase3,BACE1,NF-κB,PKC,PS2,Aβ and APP mRNA in peripheral blood of the AD patients may vary and can be used for clinical diagnosis of the AD patients.Selenium-rich food can improve AD to some extent,and its mechanism is related to reducing the oxidative damage of brain tissue and decreasing the expression of AD related genes PS2 and Aβ.

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.