Over‐the‐scope clip system: A review of 1517 cases over 9 years

Rescue therapy for gastrointestinal (GI) refractory bleeding, perforation, and fistula has traditionally required surgical interventions owing to the limited performance of conventional endoscopic instruments and techniques. An innovative clipping system, the over‐the‐scope clip (OTSC), may play an important role in rescue therapy. This innovative device is proposed as the final option in endoscopic treatment. The device presents several advantages including having a powerful sewing force for closure of GI defects using a simple mechanism and also having an innovative feature, whereby a large defect and fistula can be sealed using accessory forceps. Consequently, it is able to provide outstanding clinical effects for rescue therapy. This review clarifies the current status and limitations of OTSC according to different indications of GI refractory disease, including refractory bleeding, perforation, fistula, and anastomotic dehiscence. An extensive literature search identified studies reported 10 or more cases in which the OTSC system was applied. A total of 1517 cases described in 30 articles between 2010 and 2018 were retrieved. The clinical success rates and complications were calculated overall and for each indication. The average clinical success rate was 78% (n = 1517) overall, 85% for bleeding (n = 559), 85% (n = 351) for perforation, 52% (n = 388) for fistula, 66% (n = 97) for anastomotic dehiscence, and 95% (n = 122) for other conditions, respectively. The overall and severe OTSC‐associated complications were 1.7% (n = 23) and 0.59% (n = 9), respectively. This review concludes that the OTSC system may serve as a safe and productive device for GI refractory diseases, albeit with limited success for fistula.     

Multi‐scale network regression for brain‐phenotype associations

Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051) , we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.