Reduced Heart Rate Variability in Somatic Symptom Disorder: Associations with Alexithymia / 정신신체의학

Objectives@#：We investigated heart rate variability (HRV) patterns in patients with somatic symptom disorder (SSD) and the relationships of these patterns with alexithymia. @*Methods@#：In total, 42 patients with SSD and 33 healthy controls were enrolled in this study. Demographic, psychological, and HRV data were assessed at baseline, and 24 patients with SSD were reassessed after 6 months of treatment. The psychological data included somatic symptoms and levels of depression, anxiety, and alexithymia as indicated by the somatic symptom subscale of the Symptom Checklist 90-Revision (SCL-12), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and the Toronto Alexithymia Scale 20 (TAS-20), respectively. @*Results@#：Patients with SSD had a lower standard deviation of normal-to-normal R-R intervals (SDNN) and lower proportions of adjacent R-R intervals greater than 50 milliseconds (pNN50) compared with controls. These HRV parameters were negatively correlated with alexithymia severity. After treatment, patients exhibited significantly decreased levels of somatic symptoms and reduced anxiety and depression, but there were no significant differences in the HRV parameters. In patients with alexithymia, a high baseline SDNN and pNN50 were associated with a decrease in somatic symptoms. @*Conclusions@#：Patients with SSD have different HRV patterns, and several HRV parameters are associated with alexithymia severity. These findings suggest that ANS regulation is involved in the pathophysiology of SSD, mediated by alexithymia. Furthermore, these results suggest that certain HRV parameters may be associated with clinical outcomes of SSD.

FusionScan: accurate prediction of fusion genes from RNA-Seq data

Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.

Diagnostic Neuroimaging in Headache Patients: A Systematic Review and Meta-Analysis

OBJECTIVE: Neuroimaging in headache patients identifies clinically significant neurological abnormalities and plays an important role in excluding secondary headache diagnoses. We performed a systematic review and meta-analysis of the existing guidelines and studies surrounding neuroimaging in headache patients. METHODS: The research question involved determining the prevalence of detecting clinically significant neurological abnormalities using neuroimaging in patients suspected of primary headache. Searches of the PubMed and Embase databases were conducted on English-language studies published from 1991 to 2016, and the reference lists of the retrieved articles were also checked manually. All headache subtypes and patients aged ≥15 years were included in the analysis. RESULTS: Ten studies met the selection criteria. The pooled prevalence of detecting clinically significant abnormalities in the neuroimaging of headache patients was 8.86% (95% confidence interval: 5.12–15.33%). Subsequently, diverse subgroup analyses were performed based on the detection method, headache type, study type, study region, age group, and disease type. CONCLUSION: The present findings indicate that limited neuroimaging methods should be carefully considered for headache diagnostic purposes when there are red flag symptoms. Limitations and suggested directions for future studies on neuroimaging in headache patients are described.