Biology subjects

Au, R.

Publications and source records attributed to Au, R..

3 recordsLinked to original sources

bioRxiv2022

Analyzing the covariance structure of plasma signaling proteins in relation to the diagnosis of dementia

Numerous studies have shown that individuals with dementia have exhibited activation of inflammatory pathways in their brains. Typically, these studies use traditional and well-established regression methods for data analysis. In this paper, a new approach is introduced that utilizes the analysis of the covariance structure using methods related to the principal component analysis (PCA) theory. Eleven biomarkers related to neuroinflammation were used to determine the association with the onset of dementia. Various demographic covariates were adjusted to account for possible confounding effects of the covariance structure. Three hypothesis testing methods were considered to discern differences between partial covariance matrices for comparing power and Type I errors through simulation studies. Application of hypothesis testing methods using data from Framingham Heart Study (FHS) found significant differences in covariance matrices between the non-dementia and dementia groups.

neuroscience↗

bioRxiv2019

Analysis of polyclonal vector integration sites using Nanopore sequencing as a scalable, cost-effective platform

Vector integration site analysis can be important in the follow-up of patients who received gene-modified cells, but current platforms based on next-generation sequencing are expensive and relatively inaccessible. We analyzed polyclonal T cells transduced by a gammaretroviral vector, SFG.iCasp9.2A.{Delta}CD19, from a clinical trial. Following restriction enzyme digestion, the unknown flanking genomic sequences were amplified by inverse polymerase chain reaction (PCR) or cassette ligation PCR. Nanopore sequencing could identify thousands of unique integration sites within polyclonal samples, with cassette ligation PCR showing less bias. The assay is scalable and requires minimum capital, which together enable cost-effective and timely analysis.

genomics↗

bioRxiv2019

Development and validation of a deep learning framework for Alzheimers disease classification

Alzheimers disease (AD) is the primary cause of dementia worldwide (1), with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and magnetic resonance imaging (MRI) to identify likely cases, yet effective practices remain variably-applied and lacking in sensitivity and specificity (2). Here we report an explainable deep learning strategy that delineates unique AD signatures from multimodal inputs of MRI, age, gender, and mini-mental state examination (MMSE) score. Our framework linked a fully convolutional network (FCN) to a multilayer perceptron (MLP) to construct high resolution maps of disease probability from local brain structure. This enabled precise, intuitive visualization of individual AD risk en route to accurate diagnosis. The model was trained using clinically-diagnosed AD and cognitively normal (NC) subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=417) (3), and validated on three independent cohorts: the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=382) (4), the Framingham Heart Study (FHS, n=102) (5), and the National Alzheimers Coordinating Center (NACC, n=582) (6). Model performance was consistent across datasets, with mean accuracy values of 0.966, 0.948, 0.815, and 0.916 for ADNI, AIBL, FHS and NACC, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n=11), and high-risk cerebral regions predicted by the model closely tracked postmortem histopathological findings. This framework provides a clinically-adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for AD diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

neuroscience↗