脂蛋白(a)检测在脑梗死患者中的应用

目的 :探讨脂蛋白 (a) [Lp(a) ]在脑梗死患者中的应用价值。方法 :检测 68例脑梗死患者急性期和恢复期中血清Lp(a)、甘油三酯 (TG)、总胆固醇 (TC)、高密度脂蛋白胆固醇 (HDL)、低密度脂蛋白胆固醇 (LDL)、载脂蛋白A1(ApoA1)、载脂蛋白B(ApoB)等水平的变化 ,并选 71例健康人作为对照组 ;同时比较脑梗死患者急性期与恢复期血脂的变化。结果 :脑梗死组的Lp(a)、TC、LDL、ApoB明显高于对照组 ,具有显著性差异 (P <0 0 1) ;ApoA1明显低于对照组 ,具有显著性差异 (P <0 0 1)。脑梗死患者急性期LDL水平比恢复期明显降低 ,具有显著性差异 (P <0 0 1) ,其他指标无显著性差异 (P >0 0 5 )。Lp(a)与其他 6项指标之间均无相关性。结论 :Lp(a)在脑梗死患者发病急性期与稳定期中是一个独立的危险因素 ,可作为反映脑梗死患者脂类代谢异常的重要指标 ,对判断脑梗死的预后可能具有重要意义     

宫腔镜检查在诊断异常子宫出血中的临床价值

目的:探讨宫腔镜检查在诊断异常子宫出血中的临床价值。方法:回顾性分析因异常子宫出血行宫腔镜检查,同时在宫腔镜下进行相应治疗和子宫内膜定位活检的287例患者资料。结果:287例异常子宫出血患者的宫腔镜诊断与病理组织学诊断总符合率为80.84%,其中子宫黏膜下肌瘤100.00%、子宫内膜息肉96.83%、子宫内膜增生68.42%、子宫内膜炎77.27%、萎缩性子宫内膜100.00%、子宫内膜癌100.00%。结论:宫腔镜能直接清晰地观察宫腔内及宫颈管的形态与结构,具有诊断性刮宫和超声检查不可比拟的优点,是诊断异常子宫出血的金标准。     

电话网络心电模式在社区基础医疗的应用

目的:探讨电话网络心电模式在社区基础医疗的应用效果。方法:对社区医院的142例有心脏不适症状而普通心电图多次检查均正常的患者进行电话网络心电监测(TTM)1056次,通过电话和网络传送心电信息至本院心脏监测中心,专职人员分析、诊断和出具报告,指导患者用药和治疗。结果:142例患者中发现早期心肌梗死心电图改变者1例,缺血型ST-T改变者16例,阵发性室上速22例,阵发性房颤12例,间歇性预激综合症8例,房性早搏和(或)室性早搏35例,6例安装永久起搏器和14例冠状动脉支架置入(PCI)术后患者TTM监测无新的异常出现,28例未见异常。结论:TTM监测能及时将平时心电图、Holter等难以捕捉的心电信息记录下来,为临床诊断和治疗提供有效依据,为患者提供早期诊断心脏病的检查手段。     

产力异常产妇62例临床护理

目的探讨产力异常产妇的临床护理。方法对62例产力异常产妇进行护理评估,并针对性采取护理措施。结果 62例产力异常产妇经过精心细致的治疗和护理,均成功分娩,无并发症发生。结论对产力异常产妇在围生期进行密切观测,并有针对性地采取护理干预措施,能够提高分娩成功率,保证产妇及胎儿安全度过分娩期。     

癫痫患者治疗后脑部异常灌注灶修复的相关因素分析

目的：探讨治疗后癫痫患者发作间期单光子发射断层扫描（SPECT）脑部异常灌注致痫灶修复的相关因素。方法：回顾性分析兰州大学第二医院癫痫中心6年来治疗的160例癫痫患者的临床资料，对影响癫痫患者发作间期SPECT脑部异常灌注致痫灶治疗后修复的相关因素进行分析。结果：单因素logistic回归分析结果显示癫痫发作类型、病因、影像学异常、治疗前脑SPECT及脑电图类型与治疗后脑部异常灌注致痫灶修复显著相关（P〈0．05）；多因素logistic回归分析最终进入模型的有癫痫类型（P〈0．01）、病因（P〈0．01）、影像学表现（P〈0．01）及治疗前SPECT异常灶类型（P〈0．01）。结论：癫痫患者脑部异常灌注致痫灶的修复与多种因素有关，症状性癫痫、低灌注灶、有明确病因及影像学异常者的异常灌注致痫灶的修复率明显低于特发性癫痫、高灌注灶、病因不明确及影像学无异常者。     

三酰甘油/高密度脂蛋白胆固醇比值与糖尿病并发症的关系研究进展

糖尿病作为重要公共健康问题，严重影响人们生活质量，给社会经济带来严重负担，特别是糖尿病并发症。血脂异常在糖尿病人群中患病率逐年上升，脂代谢异常在糖尿病并发症的发生及发展中占有重要的地位，逐渐被人们重视，尤其是三酰甘油（TG）/高密度脂蛋白胆固醇（HDL-C）比值，是目前临床研究热点问题，根据目前研究发现，TG/HDL-C比值在糖尿病并发症的发生、发展中起到重要作用。本文就TG/HDL-C比值与糖尿病并发症的关系情况做一综述，发现TG/HDL-C比值与糖尿病并发症有关，与微血管并发症及大血管并发症均有联系，但是，仅从目前研究中，TG/HDL-L比值与糖尿病具体并发症的切值没有统一的标准，还需进一步研究。     

Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals

The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task, by identifying the experience that a user has while interacting with an AAL technology under particular conditions. This may help designing better products and improve user engagement and adoption of AAL solutions. However, evaluating the UX of AAL technologies is a difficult task, due to the inherent limitations of their subjects and of the evaluation methods. In this study, we validated the feasibility of assessing the UX of older adults while they use a cognitive stimulation application using a neural network trained to recognize pleasant and unpleasant emotions from electroencephalography (EEG) signals by contrasting our results with those of additional self-report and qualitative analysis UX evaluations. Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%. As future work we will conduct additional UX evaluation studies using the three different methods, in order to appropriately validate these results.     

High-speed digital imaging and electroglottography of tracheoesophageal phonation by Amatsu's method

Background. Our previous findings have indicated that the thyropharyngeal muscles form a retropharyngeal prominence during alaryngeal phonation via the TE fistula. This prominence forms a so-called ‘neoglottis’, which is thought to function as the vibratory source. To better understand the mechanism of TE phonation, we analyzed the vibration of the neoglottis using electroglottography (EGG) and a high-speed digital imaging system. Patients and methods. Two volunteers who use TE phonation for their daily speech communication were subjected to this study. The vibrations of the neoglottis were recorded simultaneously as EGG and high-speed imaging with acoustic signals. Results. The vibrations of the neoglottis, recorded by means of high-speed digital imaging, were exactly synchronized with the waveforms of the acoustic signals and EGG. Conclusions. These results further confirm the neoglottis as the source of vibration during tracheoesophageal (TE) phonation.     

Dermatoglyphic patterns as possible predictor of treatment resistance in schizophrenia

IntroductionDermatoglyphic patterns have been studied in schizophrenia and there is now evidence relating aspects of the dermatoglyphic profiles of the hands to schizophrenia.^1,2 But there has been no study on whether treatment resistance can be predicted from the dermatoglyphic profile of an individual. This study was done to determine whether abnormal neurodevelopmental markers were more frequent in schizophrenics non-responsive to routine treatment and whether treatment resistance can be predicted by studying the dermatoglyphic profile of an individual.MethodsFinger and palm prints of 144 schizophrenic patients responsive to treatment and 44 schizophrenic patients non-responsive were taken. Finger print patterns, palmar patterns, palmar flexion creases, total ridge counts and atd angles were studied. The analysis was done using SPSS 11.0.ResultsThe results showed there was increased pattern frequency in the I3 area of the left hand of non-responders and in the I4 area of the right hand of responders. atd angle was decreased in the left hand of non-responders. There was increased frequency of the Simian type I crease in male non-responders and Sydney line in female non-responders.DiscussionCertain dermatoglyphic abnormalities occur more frequently in non-responders and with the help of these abnormalities, a schizophrenic patient likely to develop treatment resistance can be detected.