Intracranial injection of AAV expressing NEP but not IDE reduces amyloid pathology in APP+PS1 transgenic mice

The accumulation of β-amyloid peptides in the brain has been recognized as an essential factor in Alzheimer\u27s disease pathology. Several proteases, including Neprilysin (NEP), endothelin converting enzyme (ECE), and insulin degrading enzyme (IDE), have been shown to cleave β-amyloid peptides (Aβ). We have previously reported reductions in amyloid in APP+PS1 mice with increased expression of ECE. In this study we compared the vector-induced increased expression of NEP and IDE. We used recombinant adeno-associated viral vectors expressing either native forms of NEP (NEP-n) or IDE (IDE-n), or engineered secreted forms of NEP (NEP-s) or IDE (IDE-s). In a six-week study, immunohistochemistry staining for total Aβ was significantly decreased in animals receiving the NEP-n and NEP-s but not for IDE-n or IDE-s in either the hippocampus or cortex. Congo red staining followed a similar trend revealing significant decreases in the hippocampus and the cortex for NEP-n and NEP-s treatment groups. Our results indicate that while rAAV-IDE does not have the same therapeutic potential as rAAV-NEP, rAAV-NEP-s and NEP-n are effective at reducing amyloid loads, and both of these vectors continue to have significant effects nine months post-injection. As such, they may be considered reasonable candidates for gene therapy trials in AD

LI-RADS: A Conceptual and Historical Review from Its Beginning to Its Recent Integration into AASLD Clinical Practice Guidance

The Liver Imaging Reporting and Data System (LI-RADS®) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver observations in individuals at high risk for hepatocellular carcinoma (HCC). LI-RADS is supported and endorsed by the American College of Radiology (ACR). Upon its initial release in 2011, LI-RADS applied only to liver observations identified at CT or MRI. It has since been refined and expanded over multiple updates to now also address ultrasound-based surveillance, contrast-enhanced ultrasound for HCC diagnosis, and CT/MRI for assessing treatment response after locoregional therapy. The LI-RADS 2018 version was integrated into the HCC diagnosis, staging, and management practice guidance of the American Association for the Study of Liver Diseases (AASLD). This article reviews the major LI-RADS updates since its 2011 inception and provides an overview of the currently published LI-RADS algorithms

A multicenter assessment of interreader reliability of LI-RADS version 2018 for MRI and CT

Background: Various limitations have impacted research evaluating reader agreement for Liver Imaging-Reporting and Data System (LI-RADS). Purpose: To assess reader agreement of LI-RADS in an international multi-center, multireader setting using scrollable images. Materials and Methods: This retrospective study used de-identified clinical multiphase CT and MRI examinations and reports with at least one untreated observation from six institutions and three countries; only qualifying examinations were submitted. Examination dates were October 2017 – August 2018 at the coordinating center. One untreated observation per examination was randomly selected using observation identifiers, and its clinically assigned features were extracted from the report. The corresponding LI-RADS v2018 category was computed as a re-scored clinical read. Each examination was randomly assigned to two of 43 research readers who independently scored the observation. Agreement for an ordinal modified four-category LI-RADS scale (LR-1/2, LR-3, LR-4, LR-5/M/tumor in vein) was computed using intra-class correlation coefficients (ICC). Agreement was also computed for dichotomized malignancy (LR-4/LR5/LR-M/LR-tumor in vein), LR-5, and LR-M. Agreement was compared between researchversus-research reads and research-versus-clinical reads. Results: 484 patients (mean age, 62 years ±10 [SD]; 156 women; 93 CT, 391 MRI) were included. ICCs for ordinal LI-RADS, dichotomized malignancy, LR-5, and LR-M were 0.68 (95% CI: 0.62, 0.74), 0.63 (95% CI: 0.56, 0.71), 0.58 (95% CI: 0.50, 0.66), and 0.46 (95% CI: 0.31, 0.61) respectively. Research-versus-research reader agreement was higher than research-versus-clinical agreement for modified four-category LI-RADS (ICC, 0.68 vs. 0.62, P = .03) and for dichotomized malignancy (ICC, 0.63 vs. 0.53, P = .005), but not for LR-5 (P = .14) or LR-M (P = .94). Conclusion: There was moderate agreement for Liver Imaging-Reporting and Data System v2018 overall. For some comparisons, research-versus-research reader agreement was higher than research-versus-clinical reader agreement, indicating differences between the clinical and research environments that warrant further study

Hepatocellular carcinoma imaging systems: why they exist, how they have evolved, and how they differ.

Over the past 16 years, several scientific organizations have proposed systems that incorporate imaging for surveillance, diagnosis, staging, treatment, and monitoring of treatment response of hepatocellular carcinoma (HCC). These systems are needed to standardize the acquisition, interpretation, and reporting of liver imaging examinations; help differentiate benign from malignant observations; improve consistency between radiologists; and provide guidance for management of HCC. This review article discusses the historical evolution of HCC imaging systems. We indicate the features differentiating these systems, including target population, screening and surveillance algorithm, diagnostic imaging modalities, diagnostic scope, expertise and technical requirements, terminology, major and ancillary imaging features, staging and transplant eligibility, and assessment of treatment response. We highlight the potential benefits of unifying the systems, which we anticipate will enable sharing, pooling, and meta-analysis of data; facilitate multi-center trials; and accelerate dissemination of knowledge

Hepatocellular carcinoma imaging systems: why they exist, how they have evolved, and how they differ.

Over the past 16 years, several scientific organizations have proposed systems that incorporate imaging for surveillance, diagnosis, staging, treatment, and monitoring of treatment response of hepatocellular carcinoma (HCC). These systems are needed to standardize the acquisition, interpretation, and reporting of liver imaging examinations; help differentiate benign from malignant observations; improve consistency between radiologists; and provide guidance for management of HCC. This review article discusses the historical evolution of HCC imaging systems. We indicate the features differentiating these systems, including target population, screening and surveillance algorithm, diagnostic imaging modalities, diagnostic scope, expertise and technical requirements, terminology, major and ancillary imaging features, staging and transplant eligibility, and assessment of treatment response. We highlight the potential benefits of unifying the systems, which we anticipate will enable sharing, pooling, and meta-analysis of data; facilitate multi-center trials; and accelerate dissemination of knowledge

Understanding LI-RADS: a primer for practical use.

The Liver Imaging-Reporting and Data System (LI-RADS) is a comprehensive system for standardized interpretation and reporting of computed tomography and magnetic resonance examinations performed in patients at risk for hepatocellular carcinoma. LI-RADS includes a diagnostic algorithm, lexicon, and atlas as well as suggestions for reporting, management, and imaging techniques. This primer provides an introduction to LI-RADS for radiologists including an explanation of the diagnostic algorithm, descriptions of the categories, and definitions of the major imaging features used to categorize observations with case examples