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.     

荧光定量PCR检测生殖道感染患者HPV6/11型结果分析

目的 了解生殖道感染人乳头状瘤病毒(HPV)6/11的感染情况,并分析与感染相关的影响因素.方法 采用实时荧光定量聚合酶链反应(RT-PCR)技术对我院2010年3月-2012年6月妇科和泌尿外科门诊以生殖道感染症状就诊的725例的生殖道分泌物进行HPV6/11 DNA检测.结果 725例中HPV6/11阳性146例,阳性率20.14％.男性阳性率11.36％ (40/352),女性28.43％(106/373),组间比较差异有统计学意义(P＜0.01);男、女性≤24岁、25～34岁、35 ～44岁年龄组阳性率组间比较差异均有统计学意义(P＜0.05).男性HPV6/11 DNA平均定量(4.62±2.13) copies/ml,女性(5.22±2.01) copies/ml.男性各年龄组HPV6/11 DNA平均定量比较差异无统计学意义(P＞0.05),女性各年龄组差异有统计学意义(P＜0.05).女性HPV6/11感染以≤34岁高发且病毒含量最高,男性感染则主要集中在25～ 34岁.结论 泌尿生殖道HPV感染不容忽视(尤其女性),RT-PCR定量检测HPV6/11 DNA可为临床判断感染程度及药物疗效提供依据.     

GRP78在肝硬化大鼠肠源性内毒素血症引发心脏病变中的作用

目的: 探讨葡萄糖调节蛋白78(GRP78)在肝硬化大鼠肠源性内毒素血症引发心脏病变中的作用。方法: 51只Wistar雄性大鼠随机分为肝硬化模型4周组、6周组、8周组和同期正常对照组。于第8周检测大鼠心功能;检测各组心肌组织匀浆中肿瘤坏死因子α(TNF-α)和丙二醛(MDA)水平;甲苯胺蓝染色和van Giesan染色分别观察心肌细胞数量的变化和计算心肌胶原容积分数(CVF);免疫组化法检测心肌组织GRP78和缺氧诱导因子1α(HIF-1α)蛋白表达情况。结果: 随肝硬化病程进展:(1)肝硬化8周组大鼠左室舒张末期压(LVEDP)及左室内压最大上升和下降速率(±LV dp/dt_max)均较对照组降低(P<0.05);(2)心肌组织中TNF-α、MDA、CVF、GRP78蛋白和HIF-1α蛋白水平均逐渐升高并显著高于正常对照组(P<0.05);(3)甲苯胺蓝染色显示模型各组心肌细胞数量逐渐减少并显著低于正常对照组(P<0.05);(4)血浆中内毒素水平分别与丙氨酸氨基转移酶(ALT)、同型半胱氨酸(Hcy)、TNF-α、GRP78和MDA呈显著正相关(P<0.05);(5)GRP78蛋白分别与HIF-1α、Hcy、MDA、ALT和CVF呈显著正相关(P<0.05)。结论: 肝硬化大鼠伴发的肠源性内毒素血症通过直接或间接作用引起内质网应激和GRP78表达增加,后者可能是引起心肌重构、导致心脏功能变化的关键分子。     

产超广谱β-内酰胺酶大肠埃希菌检出率及耐药性分析

目的了解2007～2009年产超广谱β-内酰胺酶(ESBLs)大肠埃希菌的检出率及对抗菌药物的耐药情况。方法细菌培养分离采用常规方法,细菌鉴定应用VTECK-2全自动细菌鉴定分析仪。ESBLs菌株采用双纸片确认法检测,药敏试验采用纸片扩散(K-B)法,按美国临床实验室标准化协会(CLSI)规定标准进行。结果 3年内从临床感染标本中共分离获得1510株大肠埃希菌,产ESBLs百分率为57.5%;各年度产ESBLs检出率分别为54.4%、55.5%和63.0%。产酶菌对头孢呋辛、头孢曲松、头孢他啶几乎全部耐药,对氨苄西林/舒巴坦、哌拉西林/他唑巴坦、头孢哌酮/舒巴坦耐药率分别为40.0%、10.0%和1.0%左右,对环丙沙星和阿米卡星耐药率分别为70.0%以上和20.0%左右,对亚胺培南和美罗培南全部敏感。结论 2007～2009年产ESBLs大肠埃希菌检出率呈上升趋势,产酶菌对含酶抑制剂复合抗菌药物的耐药性也呈上升趋势,这与近几年来头孢类抗菌药物在临床广泛、大量应用有关,应引起临床医生高度重视。对产酶菌引起的重度感染应首选碳青霉烯类抗菌药物治疗。     

大肠埃希菌AmpC酶的临床实验室检测与报告

目的探讨致病大肠埃希菌AmpC酶的临床实验室检测方法,为临床合理使用抗生素提供科学依据。方法将纸片扩散法药敏试验中对头孢西丁中介或耐药的菌株采用三维试验和聚合酶链反应(PCR)基因扩增对AmpC酶进行确证试验。结果临床分离筛选的10株耐头孢西丁大肠埃希菌中采用三维试验和PCR基因扩增后1株被确证为AmpC酶菌株。结论耐头孢西丁的革兰阴性菌需要作AmpC酶试验或PCR基因扩增进行确证,准确AmpC酶检测结果对临床合理使用抗生素具有积极意义。     

The clinical evaluation and treatment of female precocious puberty

One in 180 American girls has precocious puberty. Accordingly, as a primary care physician, the obstetrician/gynecologist must be knowledgeable about the clinical evaluation and management of this disorder. Pubertal precocity has numerous causes and may be classified broadly as being central or peripheral in etiology. A meticulous history and physical examination, the judicious choice and interpretation of laboratory tests, and the selective use of radiological studies are the cornerstones of the evaluation. The initial approach should focus on identifying life-threatening tumors of the brain, adrenal gland, or ovary. The management goals include reducing the gonadotropin secretion and sex steroid effects and maximizing the eventual adult height. Because the child and her parents are frequently extremely distressed, the treating physician’s sensitivity and reassurance are paramount. The obstetrician/gynecologist, as both primary care physician and consultant, is in an ideal position to investigate, diagnose, and treat female precocious puberty.