Internal models underlying grasp can be additively combined

Our ability to additively combine two learned internal models was investigated by studying the forces people generate when lifting objects with a precision grip. Subjects were required to alternately lift two objects of identical physical appearance but differing weight. Grip force scaling prior to lift-off was used to estimate the output of the internal model associated with each object. Appropriate internal models were formed when alternately lifting two objects of different weight. The objects were then combined by stacking them one upon the other, and the combined object was lifted. Results show that subjects can additively combine internal models of object dynamics but the sum is biased by a default estimate of the objects weight.     

Precision and intersite correlation of bone densitometry at the radius, tibia and femur with peripheral quantitative CT

Objective. To compare the in situ precision of peripheral quantitative CT (pQCT) at the radius, tibia and femur, and to analyze the intersite correlation, in order to determine whether measurements at the lower extremity reproduce results at the radius or are of additional informative value. Design and material. pQCT measurements were performed in 86 elderly cadavers (mean age 80.5 years) at trabecular and cortical locations in the radius, tibia and femur, determining densitometric (bone mineral content and density) as well as geometric parameters (cross-sectional area, cortical thickness, polar moment of inertia and others). In 14 cadavers, repeated measurements were obtained at all sites on four different days. Results and conclusions. At cortical sites, the precision for the densitometric and geometric variables ranged from 0.4% to 4.3%, and was similar for the radius, tibia and femur. At trabecular locations, the reproducibility of the density measurements ranged from 1.8% to 2.5% at the radius, and from 3.2% to 5.9% at the femur and tibia. The intersite correlation of the total bone mineral content ranged from 0.87 and 0.97 at cortical sites, and from 0.63 to 0.85 at trabecular locations. The trabecular density showed a higher similarity between the tibia and femur (r=0.68–0.78) than between the radius and the lower extremity (r=0.41–0.45). The results demonstrate a substantial heterogeneity of trabecular bone in elderly individuals and advocate measurements directly at the site of clinical or scientific interest. Received: 5 July 1999 Revision requested: 12 August 1999 Revision received: 31 August 1999 Accepted: 13 September 1999     

正确理解“精准医学”在临床实践中的意义

2015年初在美国正式提出一个在研究基因序列的基础上,探索疾病的根源,预防和治疗的规划并被定名为Precision medicine,译称"精确医学"或"精准医学"。该定义在临床医生中引起了不同的意见和疑虑,因为这涉及到临床医学研究和实践中的导向问题。精准医学应该是对医学服务的一种要求,要求医学服务从行为和方法上达到精准,以基因序列和表达调控为基础的分子医学是狭义的精准医学,与临床医学所要求的精准相互关联又有所不同。临床工作者直接面对大多数病人,须要运用符合现代医学认可的各种各样的方法(包括基因系列的成果)解决病人的问题,而不管这种方法是否基于基因分析的结果。这种以循证医学为基础的临床医学属广义的精准医学。     

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput,portable, and computational methods

ObjectiveThe combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale.MethodsBased on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review.ResultsThe practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources.ConclusionsMachine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.     

新疆健康扶贫模式:结核病精准服务平台

通过对国内尤其是新疆结核病现状进行分析,阐述通过大数据和人工智能技术构建结核病精准防控体系，搭建“大数据智能监测、AI辅助诊断、AI临床诊疗管理、康复随访干预”四位一体，探索临床诊疗和公共卫生数据整合衔接的新疆模式。认为精准平台有利于早发现、早诊断，使上报，转诊、治疗、随访实现全流程的闭环管理，为西部地区传染病防治提供借鉴     

基于循证指南和精准医学时代背景下肝癌诊疗的思考与总结

正信息时代,瞬息万变,新的医学理念和技术层出不穷,不断更新着人们对于疾病的认知模式。近些年来,诸多国家和地区遵循循证医学,筛选出最可靠并具有一定的临床运用价值的研究成果,更新发表了各种专家共识或临床指南,指导世界各地医师的诊疗[1]。肝癌的诊治作为全球性的难