Objective To discuss the relationship of drip velocity of nitrate on blood pressure while treating coronary disease,in order to provide appropriate drip velocity for clinical treatment.Methods 155 patients with coronary disease using nitrate to lower blood pressure were selected.They were divided into the nitro glycerin group(85 cases) and the isosorbide mononitrate group( 70 cases) according to difference of medication.The velocity of drugs was adjusted on basis of blood pressure changes.The blood pressure changes at different drip velocities were observed and compared.Results The systolic pressure and the diastolic pressure between two groups showed no difference at 20 drops/min,but the results were the opposite at 30 drops/min.The systolic pressure and the diastolic pressure in the nitro glycerin group showed evident changes at different drip velocities,but in the isosorbide mononitrate group,these changes were not so significant.9 patients in the nitro glycerin group had headache during treatment,no headache occurred in the isosorbide mononitrate group.Conclusions Intravenous use of nitrate at a velocity of 20 drops/min is relative secure.The risk of hypotension will increase if the medication speed increases.lsosorbide mononitrate has little influence on blood pressure.

Pelvic organ prolapse (POP) is a common pelvic floor dysfunction disease caused by multiple factors in female.The main clinical manifestation is pelvic organ protrusion to the vaginal cavity.This disease not only affects the health and the quality of life of women and but also affects their mental health.At present,with the improvement of people's quality of life,reducing the occurrence of POP has become a urgent problem.Therefore,understanding its risk factors is the foundation for our research and development of prevention strategies.The risk factors of POP are summarized based on the principle of evidence-based medicine,which provides scientific basis for the prevention of POP.

Polycystic ovary syndrome (PCOS) is a common disease that lead to endocrine disorders and infertility in women of child-bearing age.A large number of studies have shown that the pathogenesis of PCOS is related to insulin resistance (IR),hyperandrogenism and high body mass index.At present,remarkable progress has been made in the study of conception methods and the reduction of multiple pregnancies in PCOS patients.However,there is relatively little research on the adverse pregnancy outcomes after conception.Therefore,this study will use evidence-based medicine to make a review of complications in maternal.For instance,pregnancy-induced hypertension syndrome (PIH),gestational diabetes mellitus (G DM),miscarriage,premature delivery and so on.This study provides an overall basis for early prevention and intervention in clinical work through discussing the pathophysiology of PCOS,the risk factors of its occurrence and development,and the management strategies of pre-pregnancy and gestation period.

China prospective multicenter birth cohort (Prospective Omics Health Atlas birth cohort, POHA birth cohort) study was officially launched in 2022. This study, in collaboration with 12 participating units, aims to establish a high-quality, multidimensional cohort comprising 20 000 naturally conceived families and assisted reproductive families. The study involves long-term follow-up of parents and offspring, with corresponding biological samples collected at key time points. Through multi-omics testing and analysis, the study aims to conduct multi-omics big data research across the entire maternal and infant life cycle. The goal is to identify new biomarkers for maternal and infant diseases and provide scientific evidence for risk prediction related to maternal diseases and neonatal health.

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.