麻醉机呼吸环路碱石灰尘埃污染的情况

目的：观察通气时碱石灰尘埃向呼吸环路中的排放及被呼吸道阻留的情况。方法：麻醉机环路连接集尘袋及筛装新碱石灰。ＪＴ库尔特颗粒计数仪动态测定排尘量。其颗粒测量范围为１～１２５μｍ３，通气１０分后测定呼吸环路加集尘袋内的尘埃量即为总尘量，收取称干重得尘埃分散度。通过对Ｎａｒ－ｃｏｍｅｄ、Ｄｒａｇｅｒ、Ｓｕｌａｒ８０８和１０３麻醉机行上述测量后，另以前者（Ｎ机）为例，其环路按相应处理分干燥、湿化和过滤三组，志愿者模拟通气１０分测定环路含尘量后与总尘量差得气管肺的阻留量。结果：四种麻醉机环路内碱石灰尘埃的分散度达１３ｍｇ／ｍ３以上。尘埃排放量与通气时间呈正比（ｒ＝０．９５，Ｐ＜０．０１）。Ｎ机干燥组中总排尘量的半数以上被气管肺阻留。湿化组该阻留量明显下降（Ｐ＜０．０５），而过滤组环路内总排尘量和气管肺阻流量下降更为显著（Ｐ＜０．０１）。结论：使用新碱石灰通气时，麻醉机环路内存在严重的尘埃污染，环路内湿化有一定的防尘作用，但以滤过处理效佳。     

岛津数字胃肠机IF OVER故障产生的原理及维修

分析了岛津3200数字胃肠机IFOVER故障产生的原因，并结合实际故障现象，具体介绍了排除方法。     

下颌骨侧位摄影X线中心线倾斜角度与垫板倾斜角度关系之探讨

目的探讨下颌骨侧位摄影分别采用一倾斜角度为α的垫板及未采用时，X线中心线倾斜角度各为β、γ之间的关系，为更简捷地拍好下颌骨提供理论参数和实践依据。方法立足于教科书，运用X线摄影原则与数学几何原理进行推理演绎。结果二者之间存在着可替换的关系，即γ=α＋β，可以大胆地去除垫板进行直接拍摄。结论不用垫板，只需通过增大X线中心线倾斜角度的方法，同样也能拍摄出一张好照片，且可操作性强，简便易行，实际使用价值更大。     

基于支持向量机的脊柱离体组织电阻抗识别

目的 测量不同脊柱组织的电阻抗,基于支持向量机建立电阻抗数据的组织分类算法并验证算法的准确性,寻找不同组织电阻抗分类阈值。 方法 取离体脊柱组织,应用电化学分析仪采集10~100 kHz频率范围内皮质骨、松质骨、脊髓、肌肉、髓核的电阻抗。将两只猪采集的数据集分别作为训练集和测试集,应用主成分分析降维至二维数据,训练和验证基于支持向量机(SVM)建立的分类算法,应用集成学习的方法计算不同组织分类的电阻抗阈值。 结果 5种组织在10~100 kHz的测量频率内,电阻抗值差异有统计学意义(P<0.001)。应用主成分分析降维的数据集建立的支持向量机分类算法识别不同组织的准确率为100%。应用集成学习建立的多个分类器计算出了不同组织的电阻抗分类阈值。 结论 基于支持向量机可以实现脊柱术区组织电阻抗的准确识别,有望应用于临床协助医生提升组织识别准确率。     

Predicting development of sustained unresponsiveness to milk oral immunotherapy using epitope-specific antibody binding profiles

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Artificial Circulation of the Liver: Machine Perfusion as a Preservation Method in Liver Transplantation

Due to the sharp increase in liver transplant candidates and the subsequent shortage of suitable donor livers, an extension of the current donor criteria is necessary. Simple cold storage, the current standard in organ preservation has proven to be insufficient to preserve extended criteria donor livers. Therefore a renewed interest grew toward alternative methods for liver preservation, such as hypothermic machine perfusion and normothermic machine perfusion. These “new” preservation methods were primarily assessed in rat models, and only a few clinically relevant large animal models have been described so far. This review will elaborate on these alternative preservation methods. Anat Rec, 291:735–740, 2008. © 2008 Wiley‐Liss, Inc.     

急性白血病的基因表达谱分析与亚型分类特征的鉴别

本研究基于生物信息学理论，运用模式识别方法和计算技术，对急性白血病的基因表达谱数据进行分析，研究急性白血病的亚型识别与分类信息基因选取问题。首先去除无关基因，然后利用浮动顺序搜索算法搜索特征空间生成候选特征子集，最后以支持向量机作为分类器进行急性白血病的亚型识别，并以误识率为依据鉴别出了5个包含完整分类信息的基因。实验结果表明，本研究鉴别出的5个信息基因能以100％的正确率准确识别急性白血病亚型。     

Hypothermic Machine Preservation in Liver Transplantation Revisited: Concepts and Criteria in the New Millennium

To overcome the present shortage of liver donors by expansion of the existing donor pool and possibly lengthening of the storage time, hypothermic machine perfusion of the liver as a dynamic preservation method is revisited. The three most important aspects are defined to be the type of preservation solution, the characteristics of perfusion dynamics, and the oxygen supply. Reviewing hypothermic liver machine perfusion experiments, the University of Wisconsin machine preservation solution is the solution most used. It is also found that nothing conclusive can be said about the optimal perfusion characteristics, since either perfusion pressure or perfusion flow is reported. The best estimation is perfusion of the liver in a physiological manner, i.e. pulsatile arterial perfusion and continuous portal venous perfusion. The applied pressures could be chosen to be somewhat lower than physiological pressures to prevent possible endothelial cell damage. Oxygen supply is necessary to achieve optimal preservation of the liver. The minimal amount of partial oxygen pressure required is inversely related to the normalized flow. Incorporating these features in a system based on existing standard surgical and organ sharing procedures and which is able to work stand-alone for 24 h, weighing less than 23 kg, could successfully implement this technique into every day clinical practise.     

基因功能预测问题中的样本不平衡处理

应用机器学习进行分类是基因功能预测的一种重要手段。但是许多预测集中的阳性样本过少，会降低功能预测的效果。针对此问题，本研究对结合支持向量机（SVM）算法的几种常用非平衡数据分类方法进行实验比较，包括投票整合分类器和移动分类面等。在此基础上提出通过加权修正投票的整合策略，以提高预测效果。实验结果显示，结合多数类样本限数取样及整合思想的投票整合法预测效果优于移动分类面法，而在投票整合法基础上的加权修正整合方法在所有方法中获得更好更稳定的结果。     

A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning–Based Risk Prediction Models

ObjectivesWe propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study.MethodsWe used longitudinal RWD for a cohort of adults (n = 4247) from the Cystic Fibrosis Foundation Patient Registry to compare outcomes of an LTx referral policy based on machine learning (ML) mortality risk predictions to referral based on (1) forced expiratory volume in 1 second (FEV₁) alone and (2) heterogenous usual care (UC). We then developed a patient-level simulation model to project number of patients referred for LTx and 5-year survival, accounting for transplant availability, organ allocation policy, and heterogenous treatment effects.ResultsOnly 12% of patients (95% confidence interval 11%-13%) were referred for LTx over 5 years under UC, compared with 19% (18%-20%) under FEV₁ and 20% (19%-22%) under ML. Of 309 patients who died before LTx referral under UC, 31% (27%-36%) would have been referred under FEV₁ and 40% (35%-45%) would have been referred under ML. Given a fixed supply of organs, differences in referral time did not lead to significant differences in transplants, pretransplant or post-transplant deaths, or overall survival in 5 years.ConclusionsHealth outcomes modeling with RWD may help to identify novel ML risk prediction models with high potential real-world clinical utility and rule out further investment in models that are unlikely to offer meaningful real-world benefits.