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Familial Mediterranean fever (FMF) is a monogenic autoinflammatory disorder with recurrent fever, abdominal pain, serositis, articular manifestations, erysipelas-like erythema, and renal complications as its main features. Caused by the mutations in the MEditerranean FeVer (MEFV) gene, it mainly affects people of Mediterranean descent with a higher incidence in the Turkish, Jewish, Arabic, and Armenian populations. As our understanding of FMF improves, it becomes clearer that we are facing with a more complex picture of FMF with respect to its pathogenesis, penetrance, variant type (gain-of-function vs. loss-of-function), and inheritance. In this study, MEFV gene analysis results and clinical findings of 27,504 patients from 35 universities and institutions in Turkey and Northern Cyprus are combined in an effort to provide a better insight into the genotype-phenotype correlation and how a specific variant contributes to certain clinical findings in FMF patients. Our results may help better understand this complex disease and how the genotype may sometimes contribute to phenotype. Unlike many studies in the literature, our study investigated a broader symptomatic spectrum and the relationship between the genotype and phenotype data. In this sense, we aimed to guide all clinicians and academicians who work in this field to better establish a comprehensive data set for the patients. One of the biggest messages of our study is that lack of uniformity in some clinical and demographic data of participants may become an obstacle in approaching FMF patients and understanding this complex disease., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Gender identity development is complex and involves several key processes. Transgender people experience incongruence between their biological and identified gender. This incongruence can cause significant impairment in overall functioning and lead to gender dysphoria (GD). The pathophysiology of GD is complex and is poorly understood. A PubMed search based on predetermined eligibility criteria was conducted to review neuropsychiatric articles focused on neurological, biological and neuroimaging aspects of gender development, transgender identity and GD. The information obtained from the literature was then used to formulize a GD model. Distinct gray matter volume and brain activation and connectivity differences were found in individuals with GD compared to controls, suggesting a neurobiological basis of GD; which leads to the concept of brain gender. Individuals with GD encounter a recurrent conflict between their brain gender and the societal feedback; which causes recurrent and ongoing cognitive dissonance, finally leading to GD and functional connectivity and activation changes in the transgender brain. GD has neurobiological basis, but it is closely associated with the individuals' interaction with the external world, their self-perception and the feedback received in return. We propose a novel model where the development of GD includes cognitive dissonance, involving anterior cingulate cortex and ventral striatum as the key brain structures. This model can be used to generate testable hypotheses using behavioral and neuroimaging techniques to understand the neuropsychobiology of GD.

We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.