Gold nanoparticles from magnetite for the detection of amyloid proteins in neurodegenerative diseases

Introduction. Currently, neurodegenerative diseases (ND) are the fourth leading cause of death worldwide that pose a great challenge in the development of tools for early diagnosis. Thus, advances in science seek sensitive and selective detection systems and this manuscript will highlight the importance of nanotechnology. Material and methods. A literature review was conducted on the representative findings of NPs technologies in neurodegenerative diseases. Articles written in both English and Spanish were included. References between 2015-2021 were also taken into account. Results. One of the most representative techniques, AuNP was specifically implemented, together with a magnetic center composed of magnetite, which has as a specific ligand with a C-terminal cysteine domain present in the B-amyloid protein, which adhere directly to the surface of the NPs, characterizing the anomalous protein. Subsequently, by means of nanosensors capable of detecting and measuring different concentrations, these pathologies are identified at an early stage. Conclusions. Today, along with the advent of biotechnology, it has been possible to design techniques with NPs that allow the identification of specific mutations and provide diagnosis in individuals. In the investigative models of AuNP, it is possible to infer that the capabilities that make them representative focus on their magnetism and biofunctionality, by specifically binding to amyloid peptides and other ligands present in the protein, which are the major components of amyloid plaques used in these studies.

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Naked-eye colorimetric detection of HCV RNA mediated by a 5′ UTR-targeted antisense oligonucleotide and plasmonic gold nanoparticles

The Analyst ◽

10.1039/d0an02481c ◽

2021 ◽

Author(s):

Almas Shamaila Mohammed ◽

Aniket Balapure ◽

Mahammad Nanne Khaja ◽

Ramakrishnan Ganesan ◽

Jayati Ray Dutta

Keyword(s):

Gold Nanoparticles ◽

Antisense Oligonucleotide ◽

Early Stage ◽

Colorimetric Detection ◽

Sensitive Detection ◽

Clinical Samples ◽

Hcv Rna ◽

Naked Eye

An Au NP based facile strategy for the rapid, early-stage, and sensitive detection of HCV RNA in clinical samples which avoids thiol tagging to the antisense oligonucleotide and expensive infrastructure is presented.

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Curcumin Inhibits the Primary Nucleation of Amyloid-Beta Peptide: A Molecular Dynamics Study

Biomolecules ◽

10.3390/biom10091323 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1323

Author(s):

Irini Doytchinova ◽

Mariyana Atanasova ◽

Evdokiya Salamanova ◽

Stefan Ivanov ◽

Ivan Dimitrov

Keyword(s):

Molecular Dynamics ◽

Hydrogen Bonds ◽

Amyloid Beta ◽

Amyloid Plaques ◽

Amyloid Beta Peptide ◽

Aβ Peptides ◽

Amyloid Peptides ◽

Primary Nucleation ◽

Beta Peptide ◽

Good Agreement

The amyloid plaques are a key hallmark of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. Amyloidogenesis is a complex long-lasting multiphase process starting with the formation of nuclei of amyloid peptides: a process assigned as a primary nucleation. Curcumin (CU) is a well-known inhibitor of the aggregation of amyloid-beta (Aβ) peptides. Even more, CU is able to disintegrate preformed Aβ firbils and amyloid plaques. Here, we simulate by molecular dynamics the primary nucleation process of 12 Aβ peptides and investigate the effects of CU on the process. We found that CU molecules intercalate among the Aβ chains and bind tightly to them by hydrogen bonds, hydrophobic, π–π, and cation–π interactions. In the presence of CU, the Aβ peptides form a primary nucleus of a bigger size. The peptide chains in the nucleus become less flexible and more disordered, and the number of non-native contacts and hydrogen bonds between them decreases. For comparison, the effects of the weaker Aβ inhibitor ferulic acid (FA) on the primary nucleation are also examined. Our study is in good agreement with the observation that taken regularly, CU is able to prevent or at least delay the onset of neurodegenerative disorders.

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Nanosensor Composed of Nitrogen-Doped Carbon Dots and Gold Nanoparticles for Highly Selective Detection of Cysteine with Multiple Signals

Analytical Chemistry ◽

10.1021/ac503595y ◽

2015 ◽

Vol 87 (4) ◽

pp. 2195-2203 ◽

Cited By ~ 132

Author(s):

Jianhui Deng ◽

Qiujun Lu ◽

Yuxin Hou ◽

Meiling Liu ◽

Haitao Li ◽

...

Keyword(s):

Gold Nanoparticles ◽

Carbon Dots ◽

Selective Detection ◽

Multiple Signals ◽

Nitrogen Doped ◽

Doped Carbon Dots ◽

Nitrogen Doped Carbon

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Virus induced hepatocellular carcinoma (HCC) and protein biomarkers

Pakistan BioMedical Journal ◽

10.52229/pbmj.v4i2.72 ◽

2021 ◽

Author(s):

Hamza Abbas Jaffari ◽

Sumaira Mazhar

Keyword(s):

Hepatocellular Carcinoma ◽

Early Stage ◽

Imaging Techniques ◽

Disease Diagnosis ◽

Protein Biomarkers ◽

Early Disease ◽

Detection Systems ◽

Non Invasive ◽

Viral Hepatitis B ◽

Current Article

Hepatocellular carcinoma (HCC) is a standout amongst the most widely recognized cancers around the world, and just as the alcoholic liver disease it is also progressed by extreme viral hepatitis B or C. At the early stage of the disease, numerous patients are asymptomatic consequently late diagnosis of HCC occurs resulting in expensive surgical resection or transplantation. On the basis of the alpha fetoprotein (AFP) estimation, combined with the ultrasound and other sensitive imaging techniques used, the non-invasive detection systems are available. For early disease diagnosis and its use in the effective treatment of HCC patients, the identification of HCC biomarkers has provided a breakthrough utilizing the molecular genetics and proteomics. In the current article, most recent reports on the protein biomarkers of HBV or HCV-related HCC and their co-evolutionary association with liver cancer are reviewed.

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Sensitive and Selective Detection of Antibiotic D-Penicillamine Based on a Dual-Mode Probe of Fluorescent Carbon Dots and Gold Nanoparticles

Journal of Fluorescence ◽

10.1007/s10895-018-2307-3 ◽

2018 ◽

Vol 28 (6) ◽

pp. 1405-1412 ◽

Cited By ~ 7

Author(s):

Hongwei Ge ◽

Kui Zhang ◽

Huan Yu ◽

Ji Yue ◽

Long Yu ◽

...

Keyword(s):

Gold Nanoparticles ◽

Carbon Dots ◽

Selective Detection ◽

Dual Mode ◽

Fluorescent Carbon Dots ◽

Fluorescent Carbon

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Electrochemical detection of β-amyloid peptides on electrode covered with N-terminus-specific antibody based on electrocatalytic O2 reduction by Aβ(1–16)-heme-modified gold nanoparticles

Biosensors and Bioelectronics ◽

10.1016/j.bios.2013.05.028 ◽

2013 ◽

Vol 49 ◽

pp. 231-235 ◽

Cited By ~ 38

Author(s):

Lin Liu ◽

Feng Zhao ◽

Fengji Ma ◽

Liping Zhang ◽

Suling Yang ◽

...

Keyword(s):

Gold Nanoparticles ◽

Electrochemical Detection ◽

Specific Antibody ◽

Amyloid Peptides ◽

O2 Reduction ◽

N Terminus ◽

Β Amyloid

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Visual and fluorescent assays for selective detection of beta-amyloid oligomers based on the inner filter effect of gold nanoparticles on the fluorescence of CdTe quantum dots

Biosensors and Bioelectronics ◽

10.1016/j.bios.2016.05.066 ◽

2016 ◽

Vol 85 ◽

pp. 625-632 ◽

Cited By ~ 55

Author(s):

Ning Xia ◽

Binbin Zhou ◽

Nanbing Huang ◽

Mengsha Jiang ◽

Jiebing Zhang ◽

...

Keyword(s):

Quantum Dots ◽

Gold Nanoparticles ◽

Inner Filter Effect ◽

Beta Amyloid ◽

Cdte Quantum Dots ◽

Selective Detection ◽

Filter Effect ◽

Amyloid Oligomers

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Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data

Frontiers in Bioinformatics ◽

10.3389/fbinf.2021.764497 ◽

2021 ◽

Vol 1 ◽

Author(s):

Jianhu Zhang ◽

Xiuli Zhang ◽

Yuan Sh ◽

Benliang Liu ◽

Zhiyuan Hu

Keyword(s):

Machine Learning ◽

Neurodegenerative Diseases ◽

Early Stage ◽

Group Versus ◽

Blood Protein ◽

Diagnostic Model ◽

Feature Engineering ◽

Ann Model ◽

Test Set ◽

Upstream Regulators

Background: Parkinson’s disease (PD), Alzheimer’s disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them.Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model’s performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators.Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies.Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.

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