针灸治疗腰椎间盘突出症伴疼痛的效果研究

目的:探讨针灸治疗腰椎间盘突出症伴疼痛的效果。方法:研究对象为2018年1-12月于我院接受治疗的腰椎间盘突出症伴疼痛患者78例,以随机数表法分为对照组与研究组,各39例。对照组采取常规牵引治疗,研究组在此基础上应用针灸治疗。治疗1个月后评价两组临床疗效,并对比两组治疗前后疼痛评分。结果:研究组治疗总有效率为97.44%(38/39),高于对照组的79.49%(31/39)(P<0.05);治疗后研究组VAS评分低于对照组(P<0.05)。结论:针灸治疗腰椎间盘突出症伴疼痛疗效确切,能够有效改善症状,降低疼痛程度,值得临床推广。     

后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

Polyethylene in knee arthroplasty: A review

Polyethylene (PE) has been used extensively in knee arthroplasty since the mid 20th century. Progress in material manufacturing and processing has led to newer polyethylenes over last few decades with different material properties. It has been established that PE wear in knee arthroplasty causes particle induced osteolysis which is the main reason for late failure and requires revision surgery. Although there are various causes of wear, the properties of PE have long been a matter of investigation as a contributory factor. The advent of newer highly cross linked PE has been shown to improve wear rates in hip arthroplasty but the benefits have not been shown to be of the same degree in knee arthroplasty. The laboratory and clinical studies so far are limited and slightly conflicting in their conclusions. The risks of using highly cross linked PE in knee arthroplasty include tibial post fracture, disruption of locking mechanism, liner fracture which can lead to increased wear and osteolysis. The current evidence suggests that highly cross linked polyethylenes should be used with caution and only considered in younger active patients. The results of a recently completed randomized trial to compare the conventional with high molecular weight PE in knee arthroplasty are awaited.     

Structured Sequence Learning: Animal Abilities,Cognitive Operations,and Language Evolution

Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in species ranging from mammals (primates and rats) to birds (pigeons, songbirds, and parrots) considering also cross‐species comparisons. The findings are contrasted with seminal studies in human infants that motivated the work in nonhuman animals. This synopsis identifies advances in knowledge and where uncertainty remains regarding the various strategies that nonhuman animals can adopt for processing sequencing dependencies. The paucity of evidence in the few species studied to date and the need for follow‐up experiments indicate that we do not yet understand the limits of animal sequence processing capacities and thereby the evolutionary pattern. This vibrant, yet still budding, field of research carries substantial promise for advancing knowledge on animal abilities, cognitive substrates, and language evolution.     

非手术治疗破裂型腰椎间盘突出症5年随访研究

目的:探讨非手术治疗破裂型腰椎间盘突出症的近中期疗效及预后转归。方法:选取2011年2月至2014年2月接受非手术治疗的75例单节段破裂型腰椎间盘突出症患者进行前瞻性研究,男53例,女22例;年龄18～58(35.62±9.96岁);病程5 d～6个月,平均(46.45±40.66) d。突出节段:L_(3,4) 4例,L_(4,5) 29例,L_5S_1 42例。放射痛左侧46例,右侧29例。选取治疗前,治疗后3个月、6个月、1年、2年、5年6个时间点对患者JOA评分、直腿抬高角度(SLRT)、指地距统计分析。计算末次随访时(治疗后5年)JOA改善率,根据JOA评分评定疗效;分析治疗前、末次随访(治疗后5年)椎间盘突出物体积变化,计算突出物体积吸收率,观察突出物吸收情况;分析JOA改善率与突出物吸收率之间关系。结果:71例患者完成随访,非手术治疗后3个月、6个月、1年、2年、5年JOA评分、SLRT、指地距与治疗前比较,差异有统计学意义(P0.05)。治疗后5年与6个月、治疗后5年与2年、治疗后2年与6个月JOA评分比较,差异无统计学意义(P0.05),其余各时间点两两比较,差异均有统计学意义(P0.05);治疗后5年与6个月、治疗后5年与2年、治疗后2年与6个月SLRT、指地距比较,差异亦无统计学意义(P0.05),其余各时间点两两比较,差异均有统计学意义(P0.05)。末次随访JOA改善率为(62.69±2.47)%,按照JOA评分评定疗效,结果优26例,良26例,可14例,差5例,优良率73.24%;突出物体积由起始的(1 981.73±588.72) mm3减少至(1 011.82±395.47) mm3,总体吸收率(45.65±2.83)%,突出物发生明显吸收24例,部分吸收26例,未吸收19例,增大2例。JOA改善率与突出物吸收率作Spearman秩相关分析,发现两者呈中等以上正相关(r=0.679,P0.001)。结论:非手术治疗破裂型腰椎间盘突出症可取得良好疗效,明确了破裂型腰椎间盘突出症的病情特点及预后转归,同时部分患者发生"重吸收"现象。     

Outcomes After Aortic Valve Replacement for Asymptomatic Severe Aortic Regurgitation and Normal Ejection Fraction

1. Download : Download high-res image (123KB)
2. Download : Download full-size image

     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.