4 research outputs found

    Major causes of death in children less than 5 years of age in Fujian,China,2007-2010

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    目的分析福建省2007-2010年5岁以下儿童主要死亡原因情况及特征,为制定干预措施提供科学依据。方法 分析死因登记信息网络报告系统上报的福建省2007-2010年5岁以下儿童死因及特征。结果 2007-2010年福建省共报告6 166例5岁以下儿童死亡病例,年平均死亡率为6.28/万,各年龄组即0--岁、1--岁、2--岁、3--岁、4--5岁的死亡率分别为44.89/万、5.48/万、3.28/万、2.38/万、1.76/万。1岁以下占78.74%,1岁以下死亡儿童出生28d内死亡的占64.33%;病例的死亡时间均匀的分布在12个月份,没有明显的高峰时间;死亡地点主要在住院死亡(63.87%),最高诊断依据主要是单纯临床诊断(53.50%),最高级别诊断单位主要是地市级医院(42.96%);前5位的死因分别为围产期疾病、先天异常、伤害、流行性感冒和肺炎、传染病和寄生虫病,合计构成比为69.45%。结论 福建省2007-2010年5岁以下儿童死亡中,以婴儿死亡居多,婴儿死亡中又以新生儿死亡居多。围产期疾病、先天异常、伤害是5岁以下儿童死亡的三大死因。5岁以下儿童死因中先天性异常的比例在逐年上升,循环系统疾病比例在逐年下降。Objective To analyze major causes of death in children under 5 years of age in Fujian,China,and to provide scientific evidence for the protection of infants and young children.Methods The data of 2007-2010 death cases were collected from China Disease Report Information System for children under 5 years of age in Fujian.Microsoft Excel and SPSS 15.0 packages were respectively used to establish the database and to analyze the data.Results A total of 6 166 death cases were reported from 2007 to 2010 for children less than 5 years of age.Death rate was 62.8 /lakh on average.The death rates of children under 1,2,3,4 and 5 years of age were 448.9,54.8,32.8,23.8 and 17.6/lakh,respectively.The number of infant death accounted for 78.74% of cases.64.33% of infant death occurred in newborn babies.There was no seasonal difference for the cases of death.Most cases of death(63.87%) occurred in hospital.The top five causes of death were periparturient diseases,congenital malformations,injuries,influenza and following pneumonia,infectious and parasitic diseases,respectively,together of which accounted for 69.45% of death cases.Conclusion Newborn babies and infants were high risk groups for the death of children under 5 years of age in Fujian.The increasing trend of congenital malformation related death suggests the importance of prenatal screening testing

    JUNO Sensitivity on Proton Decay pνˉK+p\to \bar\nu K^+ Searches

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    The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in pνˉK+p\to \bar\nu K^+ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via pνˉK+p\to \bar\nu K^+ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is 9.6×10339.6 \times 10^{33} years, competitive with the current best limits on the proton lifetime in this channel

    JUNO sensitivity on proton decay pνK+p → νK^{+} searches

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    JUNO sensitivity on proton decay p → ν K + searches*

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    The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the pνˉK+ p\to \bar{\nu} K^+ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via pνˉK+ p\to \bar{\nu} K^+ is 36.9% ± 4.9% with a background level of 0.2±0.05(syst)±0.2\pm 0.05({\rm syst})\pm 0.2(stat) 0.2({\rm stat}) events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 9.6 \times 10^{33} years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies
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