overview and meta-analysis of neuroimaging studies on motor conversion disorder

Background Conversion Disorders (CD) are prevalent functional disorders. Although the pathogenesis is still not completely understood, an interaction of genetic, neurobiological, and psychosocial factors is quite likely. The aim of this study is to provide a systematic overview on imaging studies on CDs and investigate neuronal areas involved in Motor Conversion Disorders (MCD). Methods A systematic literature search was conducted on CD. Subsequently a meta-analysis of functional neuroimaging studies on MCD was implemented using an Activation Likelihood Estimation (ALE). We calculated differences between patients and healthy controls as well as between affected versus unaffected sides in addition to an overall analysis in order to identify neuronal areas related to MCD. Results Patients with MCD differ from healthy controls in the amygdala, superior temporal lobe, retrosplenial area, primary motor cortex, insula, red nucleus, thalamus, anterior as well as dorsolateral prefrontal and frontal cortex. When comparing affected versus unaffected sides, temporal cortex, dorsal anterior cingulate cortex, supramarginal gyrus, dorsal temporal lobe, anterior insula, primary somatosensory cortex, superior frontal gyrus and anterior prefrontal as well as frontal cortex show significant differences. Conclusions Neuronal areas seem to be involved in the pathogenesis, maintenance or as a result of MCD. Areas that are important for motor-planning, motor-selection or autonomic response seem to be especially relevant. Our results support the emotional unawareness theory but also underline the need of more support by conduction imaging studies on both CD and MCD

Chronic Pain and Sleep Disorders in Primary Care.

BACKGROUND: Chronic pain (CP) and sleep disorders (SD) are highly prevalent in the general population. However, comprehensive data regarding the prevalence and characteristics of pain and SD in primary care are rare. METHODS: From N = 578 patients N = 570 were included within 8 weeks (mean age: 50.8 ± 18.7 years, females: 289). Sociodemographic data, Insomnia Severity Index (ISI), and parts of a self-report questionnaire for pain (Multidimensional German Pain Questionnaire) were recorded and additional medical information (pain medication, sleep medication) was gathered from the patient charts. RESULTS: Of the total sample, 33.2% (n = 189) suffer from CP (pain ≥ 6 months) and 29.1% (n = 166) from SD. 45.5% of the CP patients suffer from SD and 26.5% from clinical insomnia (ISI ≥ 15). SD (β = 0.872, SE = 0.191,  t = 4,572, p < 0.001, CI [0.497; 1.246]) and older age (β = 0.025, SE = 0.005, t = 5.135, p < 0.001, CI [0.015; 0.035]) were significantly associated with pain experience. CONCLUSION: About a quarter of CP patients suffer from clinical insomnia. The suggested bidirectional relation should be considered during comprehensive assessment and treatment of patients

Neural correlates of conversion disorder: overview and meta-analysis of neuroimaging studies on motor conversion disorder.

BACKGROUND: Conversion Disorders (CD) are prevalent functional disorders. Although the pathogenesis is still not completely understood, an interaction of genetic, neurobiological, and psychosocial factors is quite likely. The aim of this study is to provide a systematic overview on imaging studies on CDs and investigate neuronal areas involved in Motor Conversion Disorders (MCD). METHODS: A systematic literature search was conducted on CD. Subsequently a meta-analysis of functional neuroimaging studies on MCD was implemented using an Activation Likelihood Estimation (ALE). We calculated differences between patients and healthy controls as well as between affected versus unaffected sides in addition to an overall analysis in order to identify neuronal areas related to MCD. RESULTS: Patients with MCD differ from healthy controls in the amygdala, superior temporal lobe, retrosplenial area, primary motor cortex, insula, red nucleus, thalamus, anterior as well as dorsolateral prefrontal and frontal cortex. When comparing affected versus unaffected sides, temporal cortex, dorsal anterior cingulate cortex, supramarginal gyrus, dorsal temporal lobe, anterior insula, primary somatosensory cortex, superior frontal gyrus and anterior prefrontal as well as frontal cortex show significant differences. CONCLUSIONS: Neuronal areas seem to be involved in the pathogenesis, maintenance or as a result of MCD. Areas that are important for motor-planning, motor-selection or autonomic response seem to be especially relevant. Our results support the emotional unawareness theory but also underline the need of more support by conduction imaging studies on both CD and MCD

Program FFlexCom — High frequency flexible bendable electronics for wireless communication systems

Today, electronics are implemented on rigid substrates. However, many objects in daily-life are not rigid — they are bendable, stretchable and even foldable. Examples are paper, tapes, our body, our skin and textiles. Until today there is a big gap between electronics and bendable daily-life items. Concerning this matter, the DFG Priority Program FFlexCom aims at paving the way for a novel research area: Wireless communication systems fully integrated on an ultra-thin, bendable and flexible piece of plastic or paper. The Program encompasses 13 projects led by 25 professors. By flexibility we refer to mechanical flexibility, which can come in flavors of bendability, foldability and, stretchability. In the last years the speed of flexible devices has massively been improved. However, to enable functional flexible systems and operation frequencies up to the sub-GHz range, the speed of flexible devices must still be increased by several orders of magnitude requiring novel system and circuit architectures, component concepts, technologies and materials

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring