To facilitate clinical trials of disease-modifying therapies for Alzheimer's disease, which are expected to be most efficacious at the earliest and mildest stages of the disease, supportive biomarker information is necessary. The only validated methods for identifying amyloid-β deposition in the brain-the earliest pathological signature of Alzheimer's disease-are amyloid-β positron-emission tomography (PET) imaging or measurement of amyloid-β in cerebrospinal fluid. Therefore, a minimally invasive, cost-effective blood-based biomarker is desirable. Despite much effort, to our knowledge, no study has validated the clinical utility of blood-based amyloid-β markers. Here we demonstrate the measurement of high-performance plasma amyloid-β biomarkers by immunoprecipitation coupled with mass spectrometry. The ability of amyloid-β precursor protein (APP)/amyloid-β (Aβ) and Aβ/Aβ ratios, and their composites, to predict individual brain amyloid-β-positive or -negative status was determined by amyloid-β-PET imaging and tested using two independent data sets: a discovery data set (Japan, n = 121) and a validation data set (Australia, n = 252 including 111 individuals diagnosed using C-labelled Pittsburgh compound-B (PIB)-PET and 141 using other ligands). Both data sets included cognitively normal individuals, individuals with mild cognitive impairment and individuals with Alzheimer's disease. All test biomarkers showed high performance when predicting brain amyloid-β burden. In particular, the composite biomarker showed very high areas under the receiver operating characteristic curves (AUCs) in both data sets (discovery, 96.7%, n = 121 and validation, 94.1%, n = 111) with an accuracy approximately equal to 90% when using PIB-PET as a standard of truth. Furthermore, test biomarkers were correlated with amyloid-β-PET burden and levels of Aβ in cerebrospinal fluid. These results demonstrate the potential clinical utility of plasma biomarkers in predicting brain amyloid-β burden at an individual level. These plasma biomarkers also have cost-benefit and scalability advantages over current techniques, potentially enabling broader clinical access and efficient population screening.

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There is no consensus for a blood-based test for the early diagnosis of Alzheimer's disease (AD). Expression profiling of small non-coding RNA's, microRNA (miRNA), has revealed diagnostic potential in human diseases. Circulating miRNA are found in small vesicles known as exosomes within biological fluids such as human serum. The aim of this work was to determine a set of differential exosomal miRNA biomarkers between healthy and AD patients, which may aid in diagnosis. Using next-generation deep sequencing, we profiled exosomal miRNA from serum (N=49) collected from the Australian Imaging, Biomarkers and Lifestyle Flagship Study (AIBL). Sequencing results were validated using quantitative reverse transcription PCR (qRT-PCR; N=60), with predictions performed using the Random Forest method. Additional risk factors collected during the 4.5-year AIBL Study including clinical, medical and cognitive assessments, and amyloid neuroimaging with positron emission tomography were assessed. An AD-specific 16-miRNA signature was selected and adding established risk factors including age, sex and apolipoprotein ɛ4 (APOE ɛ4) allele status to the panel of deregulated miRNA resulted in a sensitivity and specificity of 87% and 77%, respectively, for predicting AD. Furthermore, amyloid neuroimaging information for those healthy control subjects incorrectly classified with AD-suggested progression in these participants towards AD. These data suggest that an exosomal miRNA signature may have potential to be developed as a suitable peripheral screening tool for AD.

The aim of this paper was to build upon Dalgarno and Lee's model or framework of learning in three-dimensional (3-D) virtual learning environments (VLEs) and to extend their road map for further research in this area. The enhanced model shares the common goal with Dalgarno and Lee of identifying the learning benefits from using 3-D VLEs. The approach adopted here is to attempt a more pedagogical description using the concept of pedagogical immersion as derived from Mayes and Fowler's framework for mapping stages of learning onto types of learning environment. The paper adopts a "design for learning" perspective and in doing so hopes the combined framework will prove useful to those designing learning activities in 3-D VLEs.