Two Sides of One Coin: A Comparison of Clinical and Neurobiological Characteristics of Convicted and Non-Convicted Pedophilic Child Sexual Offenders

High prevalence of child sexual offending stand in contradiction to low conviction rates (one-tenth at most) of child sexual offenders (CSOs). Little is known about possible differences between convicted and non-convicted pedophilic CSOs and why only some become known to the judicial system. This investigation takes a closer look at the two sides of "child sexual offending" by focusing on clinical and neurobiological characteristics of convicted and non-convicted pedophilic CSOs as presented in the Neural Mechanisms Underlying Pedophilia and sexual offending against children (NeMUP)*-study. Seventy-nine male pedophilic CSOs were examined, 48 of them convicted. All participants received a thorough clinical examination including the structured clinical interview (SCID), intelligence, empathy, impulsivity, and criminal history. Sixty-one participants (38 convicted) underwent an inhibition performance task (Go/No-go paradigm) combined with functional magnetic resonance imaging (fMRI). Convicted and non-convicted pedophilic CSOs revealed similar clinical characteristics, inhibition performances, and neuronal activation. However, convicted subjects' age preference was lower (i.e., higher interest in prepubescent children) and they had committed a significantly higher number of sexual offenses against children compared to non-convicted subjects. In conclusion, sexual age preference may represent one of the major driving forces for elevated rates of sexual offenses against children in this sample, and careful clinical assessment thereof should be incorporated in every preventive approach

The AP-1-BATF and -BATF3 module is essential for growth, survival and TH17/ILC3 skewing of anaplastic large cell lymphoma.

Transcription factor AP-1 is constitutively activated and IRF4 drives growth and survival in ALK+ and ALK- anaplastic large cell lymphoma (ALCL). Here we demonstrate high-level BATF and BATF3 expression in ALCL. Both BATFs bind classical AP-1 motifs and interact with in ALCL deregulated AP-1 factors. Together with IRF4, they co-occupy AP-1-IRF composite elements, differentiating ALCL from non-ALCL. Gene-specific inactivation of BATFs, or global AP-1 inhibition results in ALCL growth retardation and/or cell death in vitro and in vivo. Furthermore, the AP-1-BATF module establishes TH17/group 3 innate lymphoid cells (ILC3)-associated gene expression in ALCL cells, including marker genes such as AHR, IL17F, IL22, IL26, IL23R and RORγt. Elevated IL-17A and IL-17F levels were detected in a subset of children and adolescents with ALK+ ALCL. Furthermore, a comprehensive analysis of primary lymphoma data confirms TH17-, and in particular ILC3-skewing in ALCL compared with PTCL. Finally, pharmacological inhibition of RORC as single treatment leads to cell death in ALCL cell lines and, in combination with the ALK inhibitor crizotinib, enforces death induction in ALK+ ALCL. Our data highlight the crucial role of AP-1/BATFs in ALCL and lead to the concept that some ALCL might originate from ILC3

Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography

Purpose: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA). Materials and Methods: In this Institutional Review Board-approved study, we analyzed the data of 65 patients (57 female, mean age: 67.4 y) who underwent both DEXA and chest CT (mean time between scans: 1.31 y). From the DEXA studies,T-scores for L1-L4 (lumbar vertebrae 1 to 4) were recorded. Patients were then divided on the basis of theirT-scores into normal control, osteopenic, or osteoporotic groups. An AI algorithm based on wavelet features, AdaBoost, and local geometry constraints independently localized thoracic vertebrae from chest CT studies and automatically computed average Hounsfield Unit (HU) values with kVp-dependent spectral correction. The Pearson correlation evaluated the correlation between theT-scores and HU values. Mann-WhitneyUtest was implemented to compare the HU values of normal control versus osteoporotic patients. Results: Overall, the DEXA-determinedT-scores and AI-derived HU values showed a moderate correlation (r=0.55;