Search

Studies examining the etiology of problematic sexual behavior (PSB) among pre-teen children often rely on identifying correlational relationships without examining potential causal mechanisms. This study describes an exploratory analysis of a potential mediational model where child sexual abuse (CSA) and child physical abuse (CPA) predict the onset of PSB through their impact on the emergence of posttraumatic stress (PTS) symptoms and self-dysregulation. The caregivers of 189 children between the ages of 3 and 11 years presenting for mental health treatment in the United States completed a battery of measures designed to assess each of the variables in the model. Cross-sectional, regression-based mediational analyses showed that the overall model performed adequately ( R = 0.33, R 2 = 0.11, F = 3.07, p = .004). CSA exerted a direct effect on PSB that was not mediated through either PTS or self-dysregulation. However, no direct effect for CPA was found. Rather, CPA exerted a significant effect on the display of self-dysregulation, which in turn was associated with PSB. These results are discussed in light of clinical implications and directions for further research.

Portal vein thrombus (PVT) in cirrhotic patients is associated with worsening portal hypertension, leads to increased complexity of necessary interventions such as transjugular liver portosystemic shunt or liver transplantation, and is associated with worse outcomes after liver transplantation. Additionally, there are no established consensus guidelines for management of bland PVT in cirrhotic patients, which currently exists on a spectrum and is patient and provider dependent.The aim of this study was to determine whether there are associations between key clinical and imaging variables and bland PVT burden over time.This exploratory, retrospective, single-center study included patients who underwent two or more multiphase CT or MRI examinations between 1/1/2013 and 12/31/2019 and had a diagnosis of PVT. Three readers independently evaluated all index and follow-up examinations for PVT burden using a proposed 8-point scale and the established Yerdel score. Key clinical factors were collected from the electronic medical record. The PVT burden over time and the association of this burden with key clinical and imaging variables were assessed using logistic regression models.138 patients with cirrhosis and bland PVT were included in the analyses. Median age was 60 years (interquartile range 55-67; 90 men) and median follow-up time was 19.6 months. At baseline, the mean score was 2.31 (± 1.44) and at the final follow-up examination, the mean score was 2.52 (± 1.99). Baseline occlusion level was the only statistically significant association with worsening PVT burden in all four vascular territories (p-value 0.0001). Anticoagulation status did not have a statistically significant association with change in thrombus burden in any vascular territory or cumulative thrombus burden across all territories (p-value 0.11-0.43).In conclusion, our study shows that in cirrhotic patients with bland PVT, the thrombus burden did not change significantly over time and baseline thrombus burden is the only clinical factor significantly associated with increasing burden over time.

Recognizing and organizing different series in an MRI examination is important both for clinical review and research, but it is poorly addressed by the current generation of picture archiving and communication systems (PACSs) and post-processing workstations. In this paper, we study the problem of using deep convolutional neural networks for automatic classification of abdominal MRI series to one of many series types. Our contributions are three-fold. First, we created a large abdominal MRI dataset containing 3717 MRI series including 188,665 individual images, derived from liver examinations. 30 different series types are represented in this dataset. The dataset was annotated by consensus readings from two radiologists. Both the MRIs and the annotations were made publicly available. Second, we proposed a 3D pyramid pooling network, which can elegantly handle abdominal MRI series with varied sizes of each dimension, and achieved state-of-the-art classification performance. Third, we performed the first ever comparison between the algorithm and the radiologists on an additional dataset and had several meaningful findings.