OBJECTIVE To investigate present conditions about the fungal infection and analyze its risk factors and the measure of prevention. METHODS Before statistics and analysis,clinical data and culture results of 175 cases from Jan 2002 to Dec 2004 were collected. RESULTS The respiratory tract occupied the majority of the fungus infection(40.00%),the gastrointestinal tract was the second(23.43%),and the urinary tract was the third(19.43%).In infection strains,Candida albicans occupied the first place(68.57%),C.tropicalis and(C.glabrata) were the second and third(6.86% and 5.71%). CONCLUSIONS The fungal infection is relevant to some risk factors,such as using antibiotics,underlying disease and aging.Therefore,using antibiotics reasonably and improving the immunity of organism are the main measures of preventing the fungal infection.

OBJECTIVE: To study the effect of oxymatrine on pathological change in brain tissue of newborn mice infected by cytomegalovirus (CMV). METHODS: CMV of TCID50 was inoculated into the brain of the newborn mice, and the morphological change in the brain tissue infected by CMV was observed with light microscope and transmission electron microscope. RESULTS: In the model control group, the results showed that there were some inflammatory cellular infiltration and focal necrosis in the brain tissue of newborn mice infected by CMV. The ultrastructure change in the brain tissue showed that the nuclear membrane of cerebral neurons sunk, the chromatin deformed and fused into masses, the cytoplasm vacuolated, the endoplasmic reticulum disarranged and the Nissl's body was blurred or disappeared. After being treated with oxymatrine (50 mg/kg, ip) for 15 days, those pathological changes of the brain tissue in the newborn mice could be significantly improved. CONCLUSION: Oxymatrine has an obvious inhibition on CMV in vivo.

Objective To investigate the feasibility of subtraction coronary computed tomography angiography (Sub-CCTA) for the diagnosis of coronary heart disease in the segment with severe calcification.Methods A retrospective analysis was performed on 27 patients who underwent clinically indicated digital subtraction angiography (DSA) and CCTA using a 320-detector row CT.Compared with the results of DSA,sensitivity,specificity,positive predictive value,negative predictive value and accuracy of Con-CCTA and Sub-CCTA were calculated.The clinical diagnostic accuracy of the two imaging methods was evaluated using the receiver operating characteristic (ROC) curve.The stenosis of coronary segments was divided into four grades (Ⅰ,Ⅱ,Ⅲ,Ⅳ).Kappa coefficient was used to measure agreement between two imaging methods.Image quality of 4-scale grade scoring method was used and t test was conducted.Results A total of 52 segments with severe calcification were evaluated.The scores of image quality in Con-CCTA and Sub-CCTA were 2.8 ± 0.5 and 3.4 ± 0.7,respectively.There was significant difference between them (t =5.9,P ＜ 0.05).Compared with the result of DSA as the golden standard,the Kappa coefficients were 0.55 and 0.81 respectively in Con-CCTA and Sub-CCTA for the quantitative evaluation of the severe calcified segments.The sensitivity,specificity,positive predictive value and negative predictive value and accuracy of Con-CCTA were 81.0％,63.1％,63.1％,81.1％ and 70.8 ％;and for Sub-CCTA they were 90.5 ％,85.2％,82.1 ％,92.0％ and 87.5 ％ respectively.Compared with Con-CCTA,the area under the ROC curve of Con-CCTA and Sub-CCTA were 0.84 (95％CI:0.70-0.93) and 0.96 (95％ CI:0.86-1.00),respectively,and the difference was statistically significant (P =0.03).Conclusions Sub-CCTA can improve the diagnostic accuracy of coronary artery stenosis in severe calcified segment.Application of subtraction technique in CCTA can reduce or even eliminate the artifacts caused by severe calcified plaque,and has a good clinical application prospect.

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.