Objective To study the relationship between ammonia concentration levels,Child class in cirrhotic patients with helicobacter pylori(Hp) infection and to observe the Hp eradication effect on ammonia.Methods Group A was consisted of 49 cases of cirrhotic patients with Hp infection.Group B included 46 cirrhotic patients without Hp infection.Group C was non-cirrhotic patients with helicobacter pylori infection.Each patient in all three groups was compared with change in serum ammonia concentration before and 4 weeks after Hp eradication therapy.Results The serum ammonia concentration in group A was significantly higher than group B and group C(P0.05).In group A,the serum ammonia concentration was increased as Child classification from A to C(P

OBJECTIVE:To study the chemical constituents of the flower of Zang Epipremnum aureum. METHODS:The con-stituents of ethyl acetate extract of the flower of E. aureum were separated and purified with varied chromatographic techniques, and the structures were identified based on spectral data and chemical properties. RESULTS:Ten compounds were isolated from eth-yl acetate extract of the flower of E. aureum,they were identified as rutamontine(1),edgeworoside C(2),edgeworin(3),tiliro-side (4),helichrysoside (5),kaempferol (6),2,4-dihydroxypheny-2-hydroxy-4-metho-xybenzyl-ketone (7),ethyl caffeate (8), phthalic acid bis-(2-ethyl-hexyl) ester (9) and noreugenin (10). CONCLUSIONS:Compound 7 is firstly isolated from natural source,compounds 5,8 and 9 is firstly isolated from the family Thymelaeaceae,compound 6 is firstly isolated from Edgeworthia and compound 2 and 10 are firstly isolated from the flowers of E. aureum. The study lays certain foundation for the quality evalua-tion of E. aureum.

S100A9,a calcium-binding protein,participates in the inflammatory process and development of various tumors,thus attracting much attention in the field of cancer biology.This study aimed to investigate the regulatory mechanism of S100A9 and its function involvement in APL.We used real-time quantitative PCR to determine whether PML/RARα affects the expression of S100A9 in NB4 and PR9 cells upon ATRA treatment.ChiP-based PCR and dual-luciferase reporter assay system were used to detect how PML/RARα and PU.1 regulate S100A9 promoter activity.CCK-8 assay and flow cytometry were employed to observe the viability and apoptosis of NB4 cells when S100A9 was overexpressed.Results showed that S100A9 was an ATRA-responsive gene,and PML/RARα was necessary for the ATRA-induced expression of S100A9 in APL cells.In addition,PU.1 could bind to the promoter of S100A9,especially when treated with ATRA in NB4 cells,and promote its activity.More importantly,overexpression of S100A9 induced the apoptosis of NB4 cells and inhibited cell growth.Collectively,our data indicated that PML/RARα and PU.1 were necessary for the ATRA-induced expression of S100A9 in APL cells.Furthermore,S100A9 promoted apoptosis in APL cells and affected cell growth.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.