甲状腺功能减退性肌病误诊3例并文献复习

目的提高风湿免疫科临床医师对甲状腺功能减退性肌病的认识及诊断水平。方法分析3例误诊为结缔组织病的甲状腺功能减退性肌病病例,参考国内外文献探讨误诊的原因,并对甲状腺功能减退性肌病进行复习。结果山西医科大学第二医院风湿免疫科近半年收治了3例以"肌无力、肌酶谱升高"为主要表现的患者,初步诊断均为结缔组织病,其中2例给予糖皮质激素治疗,行甲状腺功能检查后均诊断为甲状腺功能减退症,结合患者病史、症状、体征及肌酶谱、肌电图等结果确诊为甲状腺功能减退性肌病,给予左旋甲状腺素钠片治疗,2例患者症状、体征及心肌酶谱、甲状腺功能均恢复正常。第3例患者在治疗中,临床表现及心肌酶谱也明显恢复。结论对临床出现肌无力、肌酶谱升高,但查体肌力、肌张力正常的患者,应考虑甲状腺功能减退性肌病的可能,对此类患者应该常规行甲状腺功能检查,以减少误诊误治。     

流行性乙型脑炎发病率的气象影响因子分析

目的探讨山西省运城地区流行性乙型脑炎(乙脑)发病率与气象因子之间的关系。方法收集2000-2009年7-9月运城地区乙脑发病率及同期气象资料,用SPSS17.0软件分析两者相关性,并用逐步回归分析建立乙脑发病率的气象因子拟合模型。结果运城地区乙脑病例多在6月开始出现,7月增多,8月达到高峰,9月减少,10月偶有发生。乙脑发病率与月平均温度、月平均气压等相关,与月温差、月日照、相对湿度、月降雨量无关。逐步回归分析得出乙脑发病率的气压回归方程,ap1(提前1个月的平均气压)有良好的拟合效果。结论气象因子对乙脑发病有重要影响,可以利用气压拟合模型预测乙脑发病率变化趋势。     

高频超声观察运动对血管功能的影响

目的评价高频超声观察运动对健康成年人血管功能的影响。方法选取49例健康志愿者,分别于运动基础、运动峰值及恢复期,应用超声射频信号血管僵硬度（QAS）技术获取右侧颈动脉顺应性（CC）、扩张性（Dc）、硬度参数（α、β）及脉搏波传播速度（PWV）,应用二维斑点追踪（X-strain）技术获取右侧颈动脉内膜环向应变（EN-CS）、外膜环向应变（EP-CS）、内膜环向应变率（EN-CSR）、外膜环向应变率（EP-CSR）,分析EN-CS、EP-CS、EN42SR、EP-CSR与CC、DC、α、β、PWV的相关性。结果在运动基础、运动峰值及恢复期,CC分别为（1．436±0．448）mm^2/KPa、（1．041±0．432）mm^2/KPa、（1．191±0．467）mm^2／kPa,DC分别为（0．040±0．016）1／kPa、（0．031±0．013）1／kPa、（0．034±0．013）1／kPa,CC、DC随时间改变呈先降低后增高的趋势（F=12．99、7．65,P均〈0．01）：PwV分别为（5．037±0．798）m／s、（5．845±1．165）m／s、（5．683±1．367）m／s,α分别为2．298±0．633、3．303±1．697、3．0924±1．533,β分别为4．762±1．284、6．794±2．515、6．158±3．089,PWV、α、β随时间改变呈先增高后降低的趋势（F=9．21、13．24、12．33,P均〈0．01）。在运动基础、运动峰值及恢复期,EN．CS分别为（7．825±2．445）％、（9．105±4．234）％、（6．271±2．527）％,EN-CSR分别为（0．831±0．276）S-。、（1．219±0．556）s^-1、（0．765±0．282）S^-1,EP。CS分别为（6．894±2．465）％、（7．936±3．628）％、（5．489±2．306）％,EP．CSR分别为（0．714±0．247）s^-1、（1．019±0．414）s^-1、（0．656±0．237）s^-1,EN-CS、EN-CSR、EP-CS、EP-CSR随时间改变呈先增高后降低的趋势（F=25．92、43．76、21．22、47．98,P均〈O．01）。相关性分析表明：EN．CS、EN．CSR、EP。CS、EP．CSR与Pwv、α、β呈正相关（r=0．253～0．494,P均〈0．05）,与CC、DC呈负相关（r=-0．234- -0．562,P均〈0．05）。结论血管功能参数CC、DC、α、β、PWV、EN-CS、EN-CSR、E-CS、E-CSR均可从不同角度反映血管功能的变化,适当的运动能够改善血管弹性。     

Periprosthetic knee joint infection caused by Sneathia sanguinegens

Sneathia sanguinegens is a fastidious, Gram-negative, rod-shape organism rarely isolated from human specimens. In the present report, we describe a case of periprosthetic knee joint infection due to the organism, which occurred in a female patient receiving immunosuppressants for underlying lupus nephritis. The causative organism was isolated from the synovial fluid in the affected knee joint through inoculating the material on chocolate agar and incubation for 15 days under 5% CO₂. Moreover, the organism was capable to be subcultured on chocolate agar with incubation for a few days under 5% CO₂, demonstrating that this uncommon organism, although generally considered as a strict anaerobe, is culturable in aerobic condition if appropriate media and a sufficient incubation time are given. The patient was treated with intravenous cefepime, an antibiotic highly active to the isolated organism in an in vitro study, in addition to intraarticular debridement and exchanging a polyethylene insert in the affected joint. The antimicrobial therapy with cefepime was given for 19 days and, thereafter, changed with oral levofloxacin. Although the patient showed full recovery after administration of levofloxacin for 100 days, an in vitro study conducted later revealed that levofloxacin was inactive to the isolated organism.     

Staphylococcus pettenkoferi bacteremia in a tertiary care hospital in Japan: Report of three cases

Staphylococcus pettenkoferi is a coagulase-negative staphylococci (CoNS) species first isolated in 2002. Human infections caused by S. pettenkoferi are rare. We herein report three cases of S. pettenkoferi bacteremia in a tertiary care hospital in Japan. Staphylococcus pettenkoferi can be a causative pathogen of catheter related blood stream infection including complicated infection, and unknown source of bacteremia. All of the patients presented with fever and shaking chills, and good clinical outcome. Further research is needed to determine the role of this organism as a pathogen and frequency.     

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNN^AM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.     

Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs)

Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.     

Interaction Between AT1 Receptor and NF-κB in Hypothalamic Paraventricular Nucleus Contributes to Oxidative Stress and Sympathoexcitation by Modulating Neurotransmitters in Heart Failure

Angiotensin II type 1 receptor (AT1-R) and nuclear factor-kappaB (NF-κB) in the paraventricular nucleus (PVN) play important roles in heart failure (HF); however, the central mechanisms by which AT1-R and NF-κB contribute to sympathoexcitation in HF are yet unclear. In this study, we determined whether interaction between AT1-R and NF-κB in the PVN modulates neurotransmitters and contributes to NAD(P)H oxidase-dependent oxidative stress and sympathoexcitation in HF. Rats were implanted with bilateral PVN cannulae and subjected to coronary artery ligation or sham surgery (SHAM). Subsequently, animals were treated for 4 weeks through bilateral PVN infusion with either vehicle or losartan (LOS, 10 μg/h), an AT1-R antagonist; or pyrrolidine dithiocarbamate (PDTC, 5 μg/h), a NF-κB inhibitor via osmotic minipump. Myocardial infarction (MI) rats had higher levels of glutamate (Glu), norepinephrine (NE) and NF-κB p65 activity, lower levels of gamma-aminobutyric acid (GABA), and more positive neurons for phosphorylated IKKβ and gp91^phox (a subunit of NAD(P)H oxidase) in the PVN when compared to SHAM rats. MI rats also had higher levels of renal sympathetic nerve activity (RSNA) and plasma proinflammatory cytokines (PICs), NE and epinephrine. PVN infusions of LOS or PDTC attenuated the decreases in GABA and the increases in gp91^phox, NF-κB activity, Glu and NE, in the PVN of HF rats. PVN infusions of LOS or PDTC also attenuated the increases in RSNA and plasma PICs, NE and epinephrine in MI rats. These findings suggest that interaction between AT1 receptor and NF-κB in the PVN contributes to oxidative stress and sympathoexcitation by modulating neurotransmitters in heart failure.     

2型糖尿病发生冠心病相关危险因素的研究

目的探讨2型糖尿病患者发生冠心病的相关危险因素。方法选择2型糖尿病患者150例,根据是否合并冠心病分为2组,单纯2型糖尿病患者组(A组)82例和2型糖尿病合并冠心病患者组(B组)68例。另选择同期健康体检者60例作为对照组,测定各组的同型半胱氨酸、高敏C反应蛋白、凝血酶原时间、活化部分凝血酶时间、纤维蛋白原和D-二聚体。结果与对照组比较,A组和B组患者同型半胱氨酸、高敏C反应蛋白和D-二聚体差异有统计学意义(P<0.05);与A组比较,B组同型半胱氨酸、高敏C反应蛋白和D-二聚体差异有统计学意义(P<0.05)。多因素logistic回归分析显示,同型半胱氨酸、高敏C反应蛋白和D-二聚体均为冠心病的危险因素。3组凝血酶原时间、活化部分凝血酶时间、纤维蛋白原比较差异无统计学意义(P>0.05)。结论同型半胱氨酸、高敏C反应蛋白和D-二聚体均是2型糖尿病患者发生冠心病的高风险因素。