Objective:To investigate the correlation between coronary artery calcification(CAC)scores based on computed tomography(CT)on chest and perioperative complications of emergency percutaneous coronary intervention(PCI)of patients with acute ST-segment elevation myocardial infarction(STEMI).Methods:A total of 263 STEMI patients who admitted to the Chest Pain Center of the Department of Cardiovascular Medicine of Baoding NO.1 Central Hospital from March 2020 to July 2021 were selected.All of them underwent CT examination on chest,and were assessed by CAC scores.They were divided into no calcification and mild group(0～2 scores,129 cases),moderate calcification group(3～5 scores,88 cases)and severe calcification group(6-9 scores,46 cases)according to CAC scores.Perioperative complications and major cardiovascular events(MACE)of patients with different degrees of calcification in the follow-up period were analyzed.The differences among different groups were compared.The correlation between CAC scores and perioperative complications of emergency PCI was further analyzed.Results:Compared with patients of no calcification and mild group,the ages of patients of moderate and severe calcification groups were older(x2=45.815,P＜0.05),and the incidences of hypertension,cerebral infarction,diabetes,multi-vessel disease and MACE of moderate and severe calcification groups were significantly higher(x2=6.762,11.071,6.064,25.036,21.694,P＜0.05).There were significant differences in eGFR and NT-ProBNP levels among the 3 groups(F=8.592,Z=20.890,P＜0.05).Compared with the severe calcification group,the incidence of coronary thrombosis was higher in the no calcification and mild group(x2=7.748,P＜0.05).According to logistic regression analysis,the patients with coronary thrombosis,moderate and severe calcification were more likely to have minor complications(OR=4.847,5.280,11.135,P＜0.001).Patients with older age,hypertension or severe calcification of coronary artery were more likely to occur serious complications,and the MACE incidence was higher within 1 year after surgery(OR=1.151,7.982,10.555,21.729,P＜0.05).Conclusion:Patients with moderate and severe calcification lesions who are assessed by CAC scores based on chest CT have a higher incidence of perioperative complications.CAC scores based on chest CT can be used to assess perioperative complications of emergency PCI and MACE within 1 year after surgery.

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.