Case Series of Right-Hemisphere Nonfluent Variant of Primary Progressive Aphasia.

Background And Purpose: Nonfluent variant primary progressive aphasia (nfvPPA) is a neurodegenerative disorder characterized by the progressive deterioration of language functions that typically appears with atrophy predominating in the left peri-insular region (left-nfvPPA) on imaging. While both left-dominant and right-dominant presentations have been reported in semantic variant primary progressive aphasia, the other language presentation of frontotemporal dementia, no case series of nfvPPA with predominantly right-sided atrophy of the peri-insular region (right-nfvPPA) have been reported previously. This study explored whether such entities exist and what their clinical features might be.

Pharmacovigilance in Australia: how do adverse event reports from clinicians contribute to medicine and vaccine safety?

Reporting adverse events (adverse drug reactions) associated with medicines and vaccines assists with identifying previously unrecognised side effects and other safety concerns. Reporting adverse events to the Therapeutic Goods Administration is mandatory for sponsors (pharmaceutical companies), and strongly encouraged but voluntary for healthcare professionals and consumers. Adverse events should be reported even when causality is uncertain, as reports may contribute to identification of a safety signal for new or uncommon events.

Detection of Clinically Relevant Monogenic Copy-Number Variants by a Comprehensive Genome-Wide Microarray with Exonic Coverage.

Background: Disease-causing copy-number variants (CNVs) often encompass contiguous genes and can be detected using chromosomal microarray analysis (CMA). Conversely, CNVs affecting single disease-causing genes have historically been challenging to detect due to their small sizes.

Methods: A custom comprehensive CMA (Baylor College of Medicine - BCM v11.

Differentiation of histological calcification classifications in breast cancer using ultrashort echo time and chemical shift-encoded imaging MRI.

Introduction: Ductal carcinoma (DCIS) accounts for 25% of newly diagnosed breast cancer cases with only 14%-53% developing into invasive ductal carcinoma (IDC), but currently overtreated due to inadequate accuracy of mammography. Subtypes of calcification, discernible from histology, has been suggested to have prognostic value in DCIS, while the lipid composition of saturated and unsaturated fatty acids may be altered in synthesis with potential sensitivity to the difference between DCIS and IDC. We therefore set out to examine calcification using ultra short echo time (UTE) MRI and lipid composition using chemical shift-encoded imaging (CSEI), as markers for histological calcification classification, in the initial step towards application.

A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.

Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In this study, we propose a novel application of machine learning in the automated screening of microinfarcts and microhemorrhages.