The Result of Revision Total Hip Arthroplasty in Patients with Metallosis Following a Catastrophic Failure of a Polyethylene Liner

BackgroundWear cannot be completely prevented after total hip arthroplasty. If severe polyethylene (PE) liner wear develops, the so-called catastrophic failure occurs and metallosis develops. We postulated that longevity of the new implant may be affected after revision surgery for metallosis following a catastrophic failure of a PE liner due to the substantial amount of PE wear particles and infiltration of the metal particles in this catastrophic condition.MethodsTwenty-three hips of 23 patients were identified because they showed metallosis during revision total hip arthroplasties performed in Seoul National University Hospital between January 1996 and August 2004. They were followed for at least 6.5 years after the index revision total hip arthroplasty. The clinical and radiological results of revision total hip arthroplasties in these patients were evaluated.ResultsThe median Harris hip score increased from 60 points before revision total hip arthroplasties to 90 points at the final follow-up. Osteolysis was detected at an average of 9.3 years after revision total hip arthroplasties in 13 hips and acetabular cup loosening at average 9.8 years after revision total hip arthroplasties in 9 hips. With radiographic evidence of osteolysis and loosening as the end points, the 15-year survival rates were 28.2% and 56.0%, respectively.ConclusionsThe survival rate of revision total hip arthroplasty in patients with metallosis following a catastrophic failure of a PE liner was low.     

The enigmatic function of IgD: some answers at last

IgD emerged soon after IgM at the time of inception of the adaptive immune system. Despite its evolutionary conservation from fish to humans, the specific functions of IgD have only recently begun to be elucidated. Mature B cells undergo alternative mRNA splicing to express IgD and IgM receptors with identical antigenic specificity. The enigma of dual IgD and IgM expression has been tackled by several recent studies showing that IgD helps peripheral accumulation of physiologically autoreactive B cells through its functional unresponsiveness to self‐antigens but prompt readiness against foreign antigens. IgD achieves this balance by attenuating IgM‐mediated anergy while promoting specific responses to multimeric non‐self‐antigens. Additional research has clarified how and why certain mucosal B cells become plasmablasts or plasma cells specializing in IgD secretion. In particular, the microbiota has been shown to play an important role in driving class switch‐mediated replacement of IgM with IgD. Secreted IgD appears to enhance mucosal homeostasis and immune surveillance by “arming” myeloid effector cells such as basophils and mast cells with IgD antibodies reactive against mucosal antigens, including commensal and pathogenic microbes. Here we will review these advances and discuss their implications in humoral immunity in human and mice.     

组织学成绩与学习压力的关系

目的 探讨北京协和医学院临床医学8年制学生组织学学习成绩与压力的关系。 方法 对91名学生的组织学成绩进行教育测量学计算,采用半结构式问卷调查其心理压力和躯体症状,对三者的关系进行统计学分析。 结果 担心不及格的学生成绩中位数显著低于其余学生（P<0.001）,感到很焦虑的学生成绩中位数显著低于其余学生（P<0.01）。担心不及格的学生易于出现考前多次失眠症状（P<0.05）;感到很焦虑的学生既更易于考前多次失眠（P<0.001）,也更易于考后发烧感冒（P<0.01）。考前1周多次失眠的学生成绩中位数显著低于其余学生（P<0.05）。 结论 有较大压力感的学生成绩相对不够理想,且更容易出现感冒、发烧等躯体症状。老师应对学生的学习成绩、心理压力和躯体症状3个方面进行全方位和具体化的指导和关心,引导学生正确面对学习压力,帮助学生实现专业知识和生理心理的健康成长。     

Biomechanical evaluation of opening-wedge high tibial osteotomy with composite materials using finite-element analysis

Background

Medial opening-wedge high tibial osteotomy (HTO) has been used to treat osteoarthritis of the medial compartment of the knee. However, this makes the proximal tibia a highly unstable structure and causes the plate to be a potential source of mechanical failure. Consequently, proper design and material use of the fixation device are essential in HTO, especially for overweight or full-weight-bearing patients.

Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.Well-designed transportation systems are a key element in the economic welfare of major cities. Design and planning of these systems requires a quantitative understanding of traffic patterns and relies on the ability to predict the effects of disruptions to such patterns, both planned and unplanned (1).There is a long history of analytic and modeling approaches to the study of traffic patterns (2), for example using simulated scenarios in simple transportation systems (3), and analysis of real traffic data in complex systems, either focusing on a small samples (4) or using more aggregate data (5, 6). Here we take this approach to the next level by making use of smart-card data and incident logs to (i) predict traffic patterns and (ii) estimate the effect of unplanned disruptions on these patterns. We analyzed 70 d of smart-card transactions from the London transportation network, composed of ∼10 million unique IDs and 6 million transactions per day on average, resulting in one of the largest statistical analyses of transportation systems to date.A related literature deals with various aspects of dynamics in complex networks and complex systems in general (7–9), using a variety of data sources, from emails (10) to the circulation of bank notes (11) to online experiments on Amazon Turk (12). More recently, a number of analyses have leveraged mobile phone data as proxies for mobility (4, 13–15).However, smart-card technology allows us to obtain large samples of passenger location and movements without requiring noisy and potentially unreliable proxies such as mobile Global Positioning System traces (16), while also leveraging a more structured environment that imposes hard constraints on patterns of urban mobility (17). In particular, these constraints of the system allow us to identify a global model of passenger behavior under local line and station closures.