Endogenous networks and international cooperation

The rise of social network analyses in the social sciences has allowed empirical work to better account for interdependencies among actors and among their actions. However, this work has been, to a large extent, descriptive: it has treated these actions as exogenous and immutable. In many cases these networks describe actions like alliance formation or trade phenomena that are the outcome variables for programs of social scientific research. In this paper, I attempt to account for both interdependencies and the endogenous nature of networks by incorporating formal theory; helping answer the question of how these networks arise by looking at the incentives of actors to form links with each other. I discuss the appropriate solution concept for a network formation game, and present an algorithm for finding the equilibrium of these networks computationally as well as ways to compare the theoretical networks to observed ones in order to evaluate the fit of the theory. I apply these methods to the study of international cooperation a subject where both the interdependencies and purposive nature of actors must be accounted for. The theoretical network is able to reproduce a number of important observed characteristics. Still, there are more factors that must be accounted for if we want to understand how the network of international cooperation is formed

Increased Prevalence of Metabolic Syndrome in Patients with Acne Inversa

BACKGROUND: Acne inversa (AI; also designated as Hidradenitis suppurativa) is a common chronic inflammatory skin disease, localized in the axillary, inguinal and perianal skin areas that causes painful, fistulating sinuses with malodorous purulence and scars. Several chronic inflammatory diseases are associated with the metabolic syndrome and its consequences including arteriosclerosis, coronary heart disease, myocardial infraction, and stroke. So far, the association of AI with systemic metabolic alterations is largely unexplored. METHODS AND FINDINGS: A hospital-based case-control study in 80 AI patients and 100 age- and sex-matched control participants was carried out. The prevalence of central obesity (odds ratio 5.88), hypertriglyceridemia (odds ratio 2.24), hypo-HDL-cholesterolemia (odds ratio 4.56), and hyperglycemia (odds ratio 4.09) in AI patients was significantly higher than in controls. Furthermore, the metabolic syndrome, previously defined as the presence of at least three of the five alterations listed above, was more common in those patients compared to controls (40.0% versus 13.0%; odds ratio 4.46, 95% confidence interval 2.02 to 9.96; P<0.001). AI patients with metabolic syndrome also had more pronounced metabolic alterations than controls with metabolic syndrome. Interestingly, there was no correlation between the severity or duration of the disease and the levels of respective parameters or the number of criteria defining the metabolic syndrome. Rather, the metabolic syndrome was observed in a disproportionately high percentage of young AI patients. CONCLUSIONS: This study shows for the first time that AI patients have a high prevalence of the metabolic syndrome and all of its criteria. It further suggests that the inflammation present in AI patients does not have a major impact on the development of metabolic alterations. Instead, evidence is given for a role of metabolic alterations in the development of AI. We recommend monitoring of AI patients in order to correct their modifiable cardiovascular risk factors

Correlation of disease severity and duration with parameters of the metabolic syndrome for AI patients.

<p>The correlation of Sartorius scores and duration of AI with each plasma TG levels and the number of positive metabolic syndrome criteria was investigated by Spearman's rank correlation analysis. No significant correlation was found.</p

Percentages of AI patients and control participants that fulfilled the indicated number of metabolic syndrome criteria.

<p>The dashed vertical line marks the cutoff for the metabolic syndrome.</p

Correlation between age and metabolic syndrome for AI patients and control participants.

<p>(A) The correlation of age with the number of fulfilled metabolic syndrome criteria was investigated by Spearman's rank correlation analysis for AI patients and controls. Significant correlation was found only for controls (Spearman's rank correlation coefficient r_s = 0.363, ***<i>P</i><0.000). (B) The prevalence of the metabolic syndrome in AI patients and control participants in different age groups is given (≤34 years old AI patients: n = 22; ≤34 years old control participants: n = 36; 35 to 44 years old AI patients: n = 27, 35 to 44 years old control participants: n = 23; ≥45 years old AI patients: n = 31; ≥45 years old control participants: n = 41). Significance of differences was assessed by the Chi-square test (*<i>P</i><0.05, ***<i>P</i><0.001).</p

Demographic characteristics of AI patients and controls.

<p>The <i>P</i>-values calculated by the Chi-square test (sex distribution) or Mann–Whitney U test (age) are indicated.</p

Correlation of severity and duration of AI with parameters of metabolic syndrome.

<p>The correlation was investigated by Spearman's rank correlation analysis. For each field, the Spearman's rank correlation coefficient and, in parenthesis, the <i>P</i>-values are indicated.</p

Parameter levels and frequency of fulfilled criteria for metabolic syndrome in AI patients and control persons.

<p>(A) The average waist circumference, plasma HDL-cholesterol and TG levels, systolic and diastolic blood pressure, and fasting plasma glucose levels in AI patients and control participants are demonstrated as the mean ± SEM. Significance of differences was assessed by the Mann–Whitney U-test (*<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001). (B) The frequency of central obesity, hypo-HDL-cholesterolemia, hypertriglyceridemia, hypertension, hyperglycemia in AI patients and controls are given. Significance of differences was assessed by the Chi-square test (*<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001). (C) The percentages of AI patients and controls with metabolic syndrome are given. Significance of differences was assessed by the Chi-square test (**<i>P</i><0.01).</p