1.Cost-minimization Analysis on 2 Drug Therapeutic Regimens for Children Pneumonia
Xuqiang HUANG ; Li DENG ; Huihong WEN ; Jialu YU ; Chang'An ZHAO ;
China Pharmacy 2005;0(20):-
0.05),but the total expenses and antibiotic cost of the medical treatment in group B were obviously lower than those in group A(P
2.The design and application of small signal clinic sensor.
Journal of Biomedical Engineering 2004;21(6):1003-1005
The performance and the characteristics of various sensors are analyzed to meet the requirement of measuring the bone force-electromechanical potentials. The strain sensor that is suitable small signal and bone force-electromechanical potential measure is developed and the curved structural form of the strain sensor is chosen. The strain sensor has the advantage of small volume, large linear range, convenient installation and high sensitivity. The relationship between the strain and force-electromechanical potential of bone specimen is determined in the experiment on the bone of femur and bone of teeth using this type of sensor which provides the valuable data for clinical use.
Biomechanical Phenomena
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Biosensing Techniques
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Electrophysiology
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Equipment Design
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Femur
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physiology
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Humans
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Mandible
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physiology
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Maxilla
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physiology
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Osteogenesis
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physiology
3. Establishment of β-aminopropionitrile-induced aortic dissection model in C57Bl/6J mice
Yanxiang GAO ; Yuting LIU ; Yayun ZHANG ; Jiaojiao QIU ; Tingting ZHAO ; Chang'an YU ; Jingang ZHENG
Chinese Journal of Cardiology 2018;46(2):137-142
Objective:
To establish the mouse aorta dissection (AD) model through drinking water containing β-aminopropionitrile (BAPN).
Methods:
Forty 3-week-old C57B1/6J male mice were divided into four groups according to randomized block design: control, 0.2, 0.4 and 0.8 g·kg-1·d-1 BAPN groups (dissolving respective dose of BAPN in the drinking water,
4.A Bayesian Stepwise Discriminant Model for Predicting Risk Factors of Preterm Premature Rupture of Membranes: A Case-control Study.
Li-Xia ZHANG ; Yang SUN ; Hai ZHAO ; Na ZHU ; Xing-De SUN ; Xing JIN ; Ai-Min ZOU ; Yang MI ; Ji-Ru XU
Chinese Medical Journal 2017;130(20):2416-2422
BACKGROUNDPreterm premature rupture of membrane (PPROM) can lead to serious consequences such as intrauterine infection, prolapse of the umbilical cord, and neonatal respiratory distress syndrome. Genital infection is a very important risk which closely related with PPROM. The preliminary study only made qualitative research on genital infection, but there was no deep and clear judgment about the effects of pathogenic bacteria. This study was to analyze the association of infections with PPROM in pregnant women in Shaanxi, China, and to establish Bayesian stepwise discriminant analysis to predict the incidence of PPROM.
METHODSIn training group, the 112 pregnant women with PPROM were enrolled in the case subgroup, and 108 normal pregnant women in the control subgroup using an unmatched case-control method. The sociodemographic characteristics of these participants were collected by face-to-face interviews. Vaginal excretions from each participant were sampled at 28-36+6 weeks of pregnancy using a sterile swab. DNA corresponding to Chlamydia trachomatis (CT), Ureaplasma urealyticum (UU), Candida albicans, group B streptococci (GBS), herpes simplex virus-1 (HSV-1), and HSV-2 were detected in each participant by real-time polymerase chain reaction. A model of Bayesian discriminant analysis was established and then verified by a multicenter validation group that included 500 participants in the case subgroup and 500 participants in the control subgroup from five different hospitals in the Shaanxi province, respectively.
RESULTSThe sociological characteristics were not significantly different between the case and control subgroups in both training and validation groups (all P > 0.05). In training group, the infection rates of UU (11.6% vs. 3.7%), CT (17.0% vs. 5.6%), and GBS (22.3% vs. 6.5%) showed statistically different between the case and control subgroups (all P < 0.05), log-transformed quantification of UU, CT, GBS, and HSV-2 showed statistically different between the case and control subgroups (P < 0.05). All etiological agents were introduced into the Bayesian stepwise discriminant model showed that UU, CT, and GBS infections were the main contributors to PPROM, with coefficients of 0.441, 3.347, and 4.126, respectively. The accuracy rates of the Bayesian stepwise discriminant analysis between the case and control subgroup were 84.1% and 86.8% in the training and validation groups, respectively.
CONCLUSIONSThis study established a Bayesian stepwise discriminant model to predict the incidence of PPROM. The UU, CT, and GBS infections were discriminant factors for PPROM according to a Bayesian stepwise discriminant analysis. This model could provide a new method for the early predicting of PPROM in pregnant women.