抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

计量经济分析 = Econometric analysis

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

In-group favoritism is a central aspect of human behavior. People often help members of their own group more than members of other groups. Here we propose a mathematical framework for the evolution of in-group favoritism from a continuum of strategies. Unlike previous models, we do not pre-suppose that players never cooperate with out-group members. Instead, we determine the conditions under which preferential in-group cooperation emerges, and also explore situations where preferential out-group helping could evolve. Our approach is not based on explicit intergroup conflict, but instead uses evolutionary set theory. People can move between sets. Successful sets attract members, and successful strategies gain imitators. Individuals can employ different strategies when interacting with in-group versus out-group members. Our framework also allows us to implement different games for these two types of interactions. We prove general results and derive specific conditions for the evolution of cooperation based on in-group favoritism.

/pdf/evolution-of-in-group-favoritism-32aw5zkoqu.pdf

Evolution of in-group favoritism

Biases in how physicians choose to withdraw life support

It is well known that humans tend to associate with other humans who have similar characteristics, but it is unclear whether this tendency has consequences for the distribution of genotypes in a population. Although geneticists have shown that populations tend to stratify genetically, this process results from geographic sorting or assortative mating, and it is unknown whether genotypes may be correlated as a consequence of nonreproductive associations or other processes. Here, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends. Maps of the friendship networks show clustering of genotypes and, after we apply strict controls for population stratification, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study in an independent sample from the Framingham Heart Study verifies that DRD2 exhibits significant homophily and that CYP2A6 exhibits significant heterophily. These unique results show that homophily and heterophily obtain on a genetic (indeed, an allelic) level, which has implications for the study of population genetics and social behavior. In particular, the results suggest that association tests should include friends’ genes and that theories of evolution should take into account the fact that humans might, in some sense, be metagenomic with respect to the humans around them.

http://fowler.ucsd.edu/correlated_genotypes_in_friendship_networks.pdf

Correlated genotypes in friendship networks

Relationships between health care providers are essential to a functioning health care system. Physicians rely on their relationships with physician colleagues for patient referrals (Gonzalez and Rizzo 1991), clinical advice (Keating, Zaslavsky, and Ayanian 1998), and information about the latest clinical advances (Gabbay and le May 2004). Given their importance, understanding the nature of such relationships could yield valuable knowledge about the emergence of local practice patterns and the diffusion of health care practices. This understanding could in turn inform health policy decisions aimed at modifying physicians' behavior.

Since every doctor has a range of interactions with an array of other doctors, physicians are embedded within a network of relationships, or ties, with their physician colleagues. Using tools from the emerging field of complex network analysis (Newman 2003), physician professional networks defined by such formal or informal relationships can be analyzed at a deeper level than previously possible. Some studies have already begun using these methods to analyze health care networks, addressing topics such as the exchange of clinical advice, the diffusion of pharmaceutical use, or organizational performance and cost-efficiency (Keating et al. 2007; Christakis and Fowler 2010; Iyengar, Van den Bulte, and Valente 2010).

A major hurdle to studying physician networks is the lack of data on physician relationships which limits the scope of many studies. For instance, Keating and colleagues studied information seeking among primary care physicians (PCPs) about issues related to women's health, but they studied only 38 doctors whom they surveyed personally. Such de novo survey work requires laborious ascertainment of each relationship among pairs of doctors to map a relationship network, which is a formidable barrier to replication across multiple hospitals or practices.

One potential way to identify physician relationships would be to use records regarding patients shared between physicians, as identified in administrative databases. Furthermore, using shared patients to define relationships is clinically intuitive. Physicians often email, phone, or “curbside” a colleague with specific clinical questions or cases, and these informal requests for information are formalized when the patient is actually referred for care (Keating, Zaslavsky, and Ayanian 1998). Thus, the presence of shared patients in administrative data—arising because of referral, patient self-selection, administrative rules (e.g., insurer policies regarding second opinions), or even chance—may represent an important source of information about physician relationships that could be useful for large-scale studies using the tools of network science.

In this study, we sought to validate the use of patient sharing identified in administrative data as a source of information on physician networks. To do so, we first identified physicians connected via shared patients in Medicare data. We then surveyed physicians in a large physicians' organization and asked them about their referral and information-sharing relationships with other physicians and evaluated the correspondence between those relationships and patient sharing measured by Medicare claims.

/pdf/mapping-physician-networks-with-self-reported-and-2cfyhha0eu.pdf

Mapping Physician Networks with Self‐Reported and Administrative Data

Predicting survival and disclosing the prediction to patients with advanced
disease, particularly cancer, is among the most difficult tasks that physicians
face. With the de-emphasis of prognosis in favor
of diagnosis and therapeutics in
the medical literature, physicians may have difficulty finding the survival
information they need to make appropriate estimates of survival for patients
who develop cancer. Quite separate from the challenge of estimating survival
accurately, physicians may also find the process of disclosing the prognosis
to their patients difficult. Using the vignette of a real patient with advanced
cancer who far outlived her physician's prognostic estimate, we discuss clinical
issues related to the science of prognosis in advanced cancer and the art
of its disclosure.

/pdf/complexities-in-prognostication-in-advanced-cancer-to-help-2qw0oenaig.pdf

Nicholas A. Christakis

Papers

Evolution of in-group favoritism

Biases in how physicians choose to withdraw life support

Correlated genotypes in friendship networks

Mapping Physician Networks with Self‐Reported and Administrative Data

Complexities in prognostication in advanced cancer: "to help them live their lives the way they want to".