人体全消化道动力检测信号自动分析处理方法研究

消化道腔内压力检测是胃肠动力疾病诊断和胃肠动力学研究的一种主要手段。利用MEMS技术，我们开发了人体全消化道微型智能介入式诊查系统，能在正常生理状态下对全消化道压力、pH值进行长时间连续监测，解决了长期困扰医学界的压力信号的获取问题。本文着重讨论了系统的软件部分，即建立信号自动分析处理系统的必要性和方法。该系统主要实现信号的预处理、信号特征值的提取和优化、样本的分类功能，从而使测试结果可以真正应用于辅助医学诊断和研究，完善整个系统的功用。     

编辑通量图控制调强治疗计划的剂量冷热点

目的 研究手动编辑通量图控制调强治疗计划靶区内外剂量冷热点的优势。方法选择5例头颈肿瘤患者，回顾性比较二次优化和编辑通量图两种方法对控制靶区内外冷热点剂量分布的差别。结果与第1次优化得到的凋强治疗计划相比，第2次优化使靶区内外冷热点剂量分别增加0．5％、减少1．1％，而编辑通量图使靶区内外冷热点剂量分别增加1．0％、减少4．2％。结论 编辑通量图，特别是二次优化后剂量改善不明显的计划．对改善靶区内外冷热点剂量分布很有效．     

Magnetic resonance imaging image intensity correction with extrapolation and adaptive smoothing.

A significant problem in magnetic resonance imaging (MRI) is the inhomogeneity of the image resulting from a number of factors that are hardware related. The obtained image can be treated as the true image multiplied by a signal modulator, which is usually smooth across the image. A class of MR image intensity correction methods extracts the slowly varying component from the image with low-pass filtering or smoothing to approximate the signal modulator. This usually causes the edge enhancement artifact in the corrected image. A novel method of extrapolating the image in advance is proposed to reduce this effect significantly. Closest point algorithm is implemented to minimize the calculation time for extrapolation. To remove bright spots caused by nonuniform sensitivity profiles, a gradient-weighted smoothing method is discussed in this work. The partial differential equations based model is applied for locally adaptive smoothing. The filtered gradient of the corrupted image is used as the weight for smoothing. Phantom and clinical data collected on various MRI systems are used for evaluation of our method. These experimental results show that the proposed method solves the edge enhancement and bright spots problem effectively and robustly.     

Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out

Conventional pattern-mixture models can be highly sensitive to model misspecification. In many longitudinal studies, where the nature of the drop-out and the form of the population model are unknown, interval estimates from any single pattern-mixture model may suffer from undercoverage, because uncertainty about model misspecification is not taken into account. In this article, a new class of Bayesian random coefficient pattern-mixture models is developed to address potentially non-ignorable drop-out. Instead of imposing hard equality constraints to overcome inherent inestimability problems in pattern-mixture models, we propose to smooth the polynomial coefficient estimates across patterns using a hierarchical Bayesian model that allows random variation across groups. Using real and simulated data, we show that multiple imputation under a three-level linear mixed-effects model which accommodates a random level due to drop-out groups can be an effective method to deal with non-ignorable drop-out by allowing model uncertainty to be incorporated into the imputation process.     

Effect measures in non-parametric regression with interactions between continuous exposures

In many biomedical studies, interest is often attached to calculating effect measures in the presence of interactions between two continuous exposures. Traditional approaches based on parametric regression are limited by the degree of arbitrariness involved in transforming these exposures into categorical variables or imposing a parametric form on the regression function. In this paper, we present: (a) a flexible non-parametric method for estimating effect measures through generalized additive models including interactions; and (b) bootstrap techniques for (i) testing the significance of interaction terms, and (ii) constructing confidence intervals for effect measures. The validity of our methodology is supported by simulations, and illustrated using data from a study of possible risk factors for post-operative infection. This application revealed a hitherto unreported effect: for patients with high plasma glucose levels, increased risk is associated, not only with low, but also with high percentages of lymphocytes.     

Cross‐validation and non‐parametric k nearest‐neighbour estimation

Summary In this paper we consider the problem of estimating a non‐parametric regression function using the k nearest‐neighbour method. We provide asymptotic theories for the least‐squares cross validation (CV) selected smoothing parameter k for both local constant and local linear estimation methods. We also establish the asymptotic normality results for the resulting non‐parametric regression function estimators. Some limited Monte Carlo experiments show that the CV method performs well in finite sample applications.     

不同功率密度的姜黄素-光动力疗法对白色念珠菌生物膜的影响

目的：探讨不同功率密度的姜黄素-光动力疗法对白色念珠菌生物膜的杀伤效果。方法：以白色念珠菌标准株和临床株为研究对象,进行体外不同功率密度（12、 22、 42、 62、 82、 102 mW/cm²）的姜黄素-光动力实验,通过XTT检测和激光共聚焦显微镜观察不同功率密度的姜黄素-光动力疗法对白色念珠菌生物膜的杀伤效果。采用SPSS 19.0软件包对数据进行统计学分析。结果：当姜黄素浓度为40 μmol/L,光照时间为4 min时,标准株和临床株的杀菌率均随着功率密度的增加而增大;功率密度增加至102 mW/cm²时,差异无统计学意义（P>0.05）。结论：姜黄素-光动力疗法杀伤白色念珠菌生物膜的效果与功率密度密切相关,建议将82 mW/cm²作为最佳功率密度。     

中药不同组方对胃癌前病变模型大鼠胃黏膜细胞Caspase-3表达的影响

[目的]探讨不同组方中药对慢性萎缩性胃炎伴不典型增生大鼠胃黏膜形态、组织学改变的影响.[方法]采用化学药物刺激的方法复制慢性萎缩性胃炎癌前病变动物模型并分组用药物干预.用酶标仪检测各组大鼠胃组织中Caspase-3表达的变化.[结果]无论是预防组还是治疗组,中药、西药的干预均可以使Caspase-3的表达增加,与模型大鼠比较差异有统计学意义(P＜0.05);健脾化瘀方要优于其他药物.[结论]各组中药均可以通过增加胃组织中Caspase-3的表达达到对胃癌前病变的治疗作用,其中健脾化瘀方治疗萎缩性胃炎癌前病变的作用要明显优于健脾益气方、活血化瘀方.     

基于时间序列模型的兰州市5岁以下儿童死亡预测分析

目的  分析2010-2018年兰州市5岁以下儿童死亡情况,构建时间序列模型预测2019年兰州市5岁以下儿童死亡趋势。 方法  采用描述流行病学方法综合分析兰州市2010年1月-2018年12月5岁以下儿童死亡情况,利用SPSS 21.0软件构建时间序列分析模型,筛选最佳模型并预测2019年兰州市5岁以下儿童死亡情况。 结果  兰州市2010-2018年共报告5岁以下儿童死亡病例1 650例,男、女报告死亡例数分别为871例和774例,年均死亡率为6.23‰。近几年兰州市5岁以下儿童死亡率总体呈下降趋势;5岁以下儿童死亡以新生儿为主,占65.27%;通过不同模型比较发现简单季节性指数平滑模型为最优模型,该模型较好的拟合了兰州市2010-2018年5岁以下儿童月死亡例数,预测2019年兰州市5岁以下儿童总死亡例数为140例,与2018年的死亡例数相近。 结论  兰州市5岁以下儿童死亡率总体呈逐年下降趋势,简单季节性指数平滑模型可以较好的反映兰州市5岁以下儿童的死亡趋势并进行短期预测。     

Testing for changes in spatial relative risk

The spatial relative risk function is a useful tool for describing geographical variation in disease incidence. We consider the problem of comparing relative risk functions between two time periods, with the idea of detecting alterations in the spatial pattern of disease risk irrespective of whether there has been a change in the overall incidence rate. Using case–control datasets for each period, we use kernel smoothing methods to derive a test statistic based on the difference between the log‐relative risk functions, which we term the log‐relative risk ratio. For testing a null hypothesis of an unchanging spatial pattern of risk, we show how p‐values can be computed using both randomization methods and an asymptotic normal approximation. The methodology is applied to data on campylobacteriosis from 2006 to 2013 in a region of New Zealand. We find clear evidence of a change in the spatial pattern of risk between those years, which can be explained in differences by response to a public health initiative between urban and rural communities. Copyright © 2017 John Wiley & Sons, Ltd.