Works (2)

Updated: July 5th, 2023 14:41

2021 review

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

[Review of ]. CHEMICAL REVIEWS, 121(4), 2545–2647.

By: P. Nguyen*, A. Ramamoorthy, B. Sahoo, J. Zheng, P. Faller, J. Straub, L. Dominguez, J. Shea ...

MeSH headings : Alzheimer Disease / metabolism; Alzheimer Disease / pathology; Amyloid / chemistry; Amyloid / metabolism; Amyloid beta-Peptides / chemistry; Amyloid beta-Peptides / metabolism; Amyotrophic Lateral Sclerosis / genetics; Amyotrophic Lateral Sclerosis / metabolism; Amyotrophic Lateral Sclerosis / pathology; Animals; Diabetes Mellitus, Type 2 / metabolism; Diabetes Mellitus, Type 2 / pathology; Humans; Islet Amyloid Polypeptide / chemistry; Islet Amyloid Polypeptide / metabolism; Models, Molecular; Neurodegenerative Diseases / metabolism; Neurodegenerative Diseases / pathology; Parkinson Disease / metabolism; Parkinson Disease / pathology; Protein Aggregation, Pathological; Proteostasis Deficiencies / metabolism; Superoxide Dismutase-1 / chemistry; Superoxide Dismutase-1 / metabolism; alpha-Synuclein / chemistry; alpha-Synuclein / metabolism; tau Proteins / chemistry; tau Proteins / metabolism
TL;DR: What computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease, Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, are reviewed. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: March 29, 2021

2020 review

A tutorial review of mathematical techniques for quantifying tumor heterogeneity


By: R. Everett*, K. Flores*, N. Henscheid, J. Lagergren*, K. Larripa, D. Li, J. Nardini*, P. Nguyen*, E. Pitman, E. Rutter n

author keywords: cancer heterogeneity; mathematical oncology; tumor growth; glioblastoma multiforme; virtual populations; nonlinear mixed effects; spatiotemporal data; Bayesian estimation; generative; adversarial networks; non-parametric estimation; variational autoencoders; machine learning
MeSH headings : Bayes Theorem; Humans; Machine Learning; Models, Theoretical; Neoplasms; Precision Medicine
TL;DR: Several techniques that can be used to aid the mathematical modeller in inferring and quantifying both sources of heterogeneity from patient data are reviewed, including virtual populations, nonlinear mixed effects modeling, non-parametric estimation, Bayesian techniques, and machine learning. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 3, 2020

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