SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine

This paper proposes a scheme consisting of two novel components to recognize multiple hand motions from surface electromyography (SEMG). First, we use the cumulative residual entropy (CREn), a measure of uncertainty in a random variable, as the feature. Second, we employ the extreme learning machine (ELM), a fast and effective classifier using single-hidden layer feedforward neural network with additive neurons, to distinguish different motions. To evaluate performance of the proposed system, we compare CREn with fuzzy entropy, sample entropy, and approximate entropy, and a state-of-the-art time-domain feature; and ELM with linear discriminant analysis and support vector machine. They are tested on four channel SEMG signals acquired from ten normal subjects. Experimental results indicate that the classification accuracies of CREn are not only better than those of other entropies with all the classifiers, but also comparable to the time-domain feature for all the segment lengths of 200, 250 and 1,000 ms with all classifiers that are evaluated. Furthermore, the computational complexity of CREn is lower than those of other features, and ELM performs significantly faster than other classifiers without sacrificing any performance. It suggests that the proposed CREn-ELM scheme has the potential to be applied to real-time control of SEMG-based multifunctional prosthesis.     

High-Resolution Melting Curve Analysis for Rapid Detection of Rifampin Resistance in Mycobacterium tuberculosis: a Meta-Analysis

A rapid, simple, accurate, and affordable method for the detection of drug-resistant tuberculosis is very critical for the selection of antimicrobial therapy and management of patient treatment. High-resolution melting curve analysis has been used for the detection of rifampin resistance in Mycobacterium tuberculosis and has shown promise. We did a systematic review and meta-analysis of published studies to evaluate the accuracy of high-resolution melting curve analysis for the detection of rifampin resistance in clinical M. tuberculosis isolates. We searched the PubMed, BIOSIS Previews, and Web of Science databases to identify studies and included them according to predetermined criteria. We used the DerSimonian-Laird random-effects model to calculate pooled measures and applied Moses'' constant for linear models to fit the summary receiver operating characteristic curve. According to the selection criteria, most of the identified studies were excluded, and only seven studies were included in the final analysis. The overall sensitivity of the high-resolution melting curve analysis was 94% (95% confidence interval [CI], 92% to 96%), and the overall specificity was very high at 99% (95% CI, 98% to 100%). The values for the pooled positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 63.39 (95% CI, 30.21 to 133.00), 0.06 (95% CI, 0.04 to 0.09), and 892.70 (95% CI, 385.50 to 2,067.24), respectively. There was no significant heterogeneity across all included studies for the measurements we evaluated. The summary receiver operating characteristic curve for the same data shows an area of 0.99 and a Q* value of 0.97. High-resolution melting curve analysis has high sensitivity and specificity for the detection of rifampin resistance in clinical M. tuberculosis isolates. This method might be a good alternative to conventional drug susceptibility tests in clinical practice.     

Mapping default mode connectivity alterations following a single season of subconcussive impact exposure in youth football

Repetitive head impact (RHI) exposure in collision sports may contribute to adverse neurological outcomes in former players. In contrast to a concussion, or mild traumatic brain injury, “subconcussive” RHIs represent a more frequent and asymptomatic form of exposure. The neural network‐level signatures characterizing subconcussive RHIs in youth collision‐sport cohorts such as American Football are not known. Here, we used resting‐state functional MRI to examine default mode network (DMN) functional connectivity (FC) following a single football season in youth players (n = 50, ages 8–14) without concussion. Football players demonstrated reduced FC across widespread DMN regions compared with non‐collision sport controls at postseason but not preseason. In a subsample from the original cohort (n = 17), players revealed a negative change in FC between preseason and postseason and a positive and compensatory change in FC during the offseason across the majority of DMN regions. Lastly, significant FC changes, including between preseason and postseason and between in‐ and off‐season, were specific to players at the upper end of the head impact frequency distribution. These findings represent initial evidence of network‐level FC abnormalities following repetitive, non‐concussive RHIs in youth football. Furthermore, the number of subconcussive RHIs proved to be a key factor influencing DMN FC.     

心血管MR T1mapping/iECV定量分析在主动脉瓣关闭不全评价中的价值

目的探讨心脏MR(CMR)定量技术纵向弛豫时间定量成像/心肌细胞外容积分数指数(T₁ mapping/iECV)对主动脉瓣关闭不全(AI)患者的临床价值,并探索其与传统心功能参数的相关性。方法回顾性收集2012年5月至2016年2月在中国医学科学院阜外医院经影像及临床资料确诊为慢性AI患者36例。所有患者均接受CMR常规扫描序列、初始及增强后T₁ mapping检查,CMR图像经后处理分析,计算主动脉瓣反流分数、钆对比剂延迟强化(LGE)质量分数、心肌细胞外容积分数(ECV)和iECV。基于CMR反流分数结果,将AI患者分为轻度组(9例)、中度组(14例)和重度组(13例)。3组AI患者初始及增强后T₁值、ECV及iECV等计量资料比较采用单因素方差分析,进一步两两比较采用LSD检验;心血管相关病史、纽约心脏协会(NYHA)心功能分级及LGE阳性率等计数资料比较采用χ2检验或Fisher确切概率法;左心室常规功能参数LVEF与iECV的相关关系采用Spearman相关分析。结果3组AI患者年龄、性别、心血管相关病史差异无统计学意义(P均>0.05)。3组不同反流程度AI患者比较:(1)3组患者LGE阳性率总体差异有统计学意义(P=0.023),随着主动脉瓣反流分级程度增高,替代性纤维化发生率增加。(2)3组患者初始T₁值、增强后T₁值及ECV总体差异均无统计学意义(H值分别为1.815、0.929、2.496,P均>0.05)。3组iECV总体差异有统计学意义(H=16.725,P<0.001),重度组iECV值明显高于其他2组(P<0.05)。iECV与LVEF呈负相关(r=-0.649,P<0.001)。结论无创性CMR定量参数技术T₁ mapping/iECV能识别不同反流程度AI患者发生弥漫性心肌纤维化的程度,并与传统心功能参数LVEF有较高的相关性,具有反映左心室功能失代偿前可逆阶段的潜力。     

系统性内皮病在创伤性休克领域研究

创伤导致的出血和组织损伤会引发休克,而休克所造成的组织灌注不足会进一步引起交感肾上腺素系统激活、炎症、内皮糖萼(EG)脱落、血小板激活和功能紊乱、内源性肝素化、凝血因子活性降低和纤溶亢进等一系列综合的病理生理反应,这些反应统称为系统性内皮病(SE),SE在创伤相关因素和复苏相关因素的影响下会进一步发展为创伤性凝血病(TC).因此探寻SE相关病理生理机制和诊断治疗进展,对于早期阻断TC的发生发展,改善创伤患者预后具有重要意义.     

Technical note: multi-alleles at the DYS385ab locus with high frequency in a Han Chinese population from southwestern China

International Journal of Legal Medicine - Y-chromosome short tandem repeat (Y-STR) markers have been widely used in forensic applications and usually show monoallelic or diallelic genotypic...     

18F-FDG PET/CT影像组学在预测肺腺癌患者EGFR突变中的价值

目的分析并比较¹⁸F-脱氧葡萄糖(FDG)PET及CT影像组学参数在预测肺腺癌患者表皮生长因子受体(EGFR)表达中的作用。方法回顾性收集2017年1月至12月间于南方医院就诊的114例[男64例,女50例,年龄35~84(平均61)岁]肺腺癌初治患者的治疗前¹⁸F-FDG PET/CT图像及其EGFR表达资料。用LIFEx软件手动逐层勾画感兴趣体积并提取图像参数,通过最小绝对收缩和选择算子(LASSO)方法重复200次进行参数选择,经十倍交叉验证选择模型的最优调和参数λ,再采用logistics逐步回归进一步筛选参数建模。针对3种数据集(PET参数、CT参数、PET+CT参数)构建3种模型,分别记为MPET、MCT、MPET+CT。用受试者工作特征(ROC)曲线分析模型预测EGFR突变的价值,获得曲线下面积(AUC)、灵敏度、特异性、准确性,采用Delong检验比较三者的AUC。结果114例肺腺癌患者中,EGFR野生型61例(53.51%),EGFR突变型53例(46.49%)。最终从PET、CT、PET+CT图像参数中分别筛选出3、3、7个参数构成回归模型MPET、MCT、MPET+CT,三者AUC分别为0.730、0.752、0.866。当三者界值分别取0.427、0.522、0.378时,约登指数最大,分别为0.420、0.405、0.630,灵敏度分别为83.0%(44/53)、58.5%(31/53)、92.5%(49/53),特异性分别为59.0%(36/61)、82.0%(50/61)、70.5%(43/61),准确性分别为70.2%(80/114)、71.1%(81/114)、80.7%(92/114)。MPET与MCT的AUC之间差异无统计学意义(z=-0.320,P>0.05),MPET+CT与MPET、MCT之间AUC差异均有统计学意义(z值:2.963、2.523,均P<0.05)。结论PET、CT、PET+CT影像组学均与肺腺癌EGFR基因表达状态相关,其中MPET+CT预测效能最高。