A New Strategy for Microbial Taxonomic Identification through Micro‐Biosynthetic Gold Nanoparticles and Machine Learning

2022 ◽  
pp. 2109365
Author(s):  
Ting Yu ◽  
Shixuan Su ◽  
Jing Hu ◽  
Jun Zhang ◽  
Yunlei Xianyu
Keyword(s):  
Machine Learning ◽  
Gold Nanoparticles ◽  
Taxonomic Identification ◽  
New Strategy

New strategy to prepare enriched and small gold nanoparticles by sonoelectrochemical pulse methods

2011 ◽  
Vol 58 ◽  
pp. 497-502 ◽  
Author(s):  
Keng-Liang Ou ◽  
Kuang-Hsuan Yang ◽  
Yu-Chuan Liu ◽  
Ting-Chu Hsu ◽  
Qing-Ye Chen
Keyword(s):  
Gold Nanoparticles ◽  
New Strategy

Modification of gold nanoparticles with a hole-transferring cocatalyst: A new strategy for plasmonic water splitting under irradiation of visible light

2021 ◽  
Author(s):  
Eri Fudo ◽  
Atsuhiro Tanaka ◽  
Shoji Iguchi ◽  
Hiroshi Kominami
Keyword(s):  
Gold Nanoparticles ◽  
Metal Oxide ◽  
Visible Light ◽  
Water Splitting ◽  
Organic Compounds ◽  
New Strategy

Plasmonic water splitting (H2O → H2 + 1/2O2) over a metal-loaded metal oxide under irradiation of visible light is still difficult, although conversion of organic compounds over plasmonic photocatalysts has...

Chondroitin sulfate coated gold nanoparticles: a new strategy to resolve multidrug resistance and thromboinflammation

2016 ◽  
Vol 52 (5) ◽  
pp. 966-969 ◽  
Author(s):  
Deepanjali Gurav ◽  
Oommen P. Varghese ◽  
Osama A. Hamad ◽  
Bo Nilsson ◽  
Jöns Hilborn ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Multidrug Resistance ◽  
Chondroitin Sulfate ◽  
Cancer Cells ◽  
Chemotherapeutic Drug ◽  
New Strategy

We have developed the first chondroitin sulfate polymer coated gold nanoparticles that can simultaneously overcome mulidrug resistance in cancer cells and suppress thromboinflammation triggered by the chemotherapeutic drug.

scholarly journals New Strategy for Creating TiO2 Thin Films with Embedded Au Nanoparticles

Coatings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1525
Author(s):  
Sofia Rubtsov ◽  
Albina Musin ◽  
Michael Zinigrad ◽  
Alexander Kalashnikov ◽  
Viktor Danchuk
Keyword(s):  
Thin Films ◽  
Gold Nanoparticles ◽  
Ethylene Glycol ◽  
Inkjet Printing ◽  
Au Nanoparticles ◽  
Localized Surface Plasmon ◽  
Tio2 Thin Films ◽  
Solar Cell Application ◽  
New Strategy ◽  
Printing Ink

This paper proposes a new strategy for producing thin films of TiO2 with embedded gold nanoparticles (TiO2/AuNP). One of the main tasks was the synthesis of a stable dispersion of TiO2 and gold nanoparticles in an aqueous solution of ethylene glycol, suitable for inkjet printing—ink with complex gold nanoparticles (AuCNP ink). The AuCNP were synthesized by a reduction from tetrachloroauric acid in the presence of TiO2 nanoparticles and ethylene glycol (EG). The final formation of TiO2/AuNP films occurred during the annealing of AuCNP layers, inkjet printed on a glass substrate. The TiO2/AuNP films demonstrate absorbance in the yellow-green range due to the localized surface plasmon resonance (LSPR) and are promising for solar cell application.

scholarly journals Proteomic fingerprint identification of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library

2020 ◽  
Author(s):  
Rolando A. Gittens ◽  
Alejandro Almanza ◽  
Eric Álvarez ◽  
Kelly L. Bennett ◽  
Luis C. Mejía ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Species Identification ◽  
Tick Species ◽  
Mass Spectra ◽  
Ixodid Ticks ◽  
Hard Tick ◽  
Taxonomic Identification ◽  
Reference Library ◽  
Hard Ticks

AbstractMatrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used as biomarkers for taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both principal component analysis (PCA) and linear discriminant analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.Author SummaryHard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to four years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers greater accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding.

scholarly journals A new strategy for the green synthesis of chondroitin sulfate-reduced gold nanoparticles; in vitro evaluation of synthesized nanoparticles

Bioimpacts ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 217-226
Author(s):  
Maryam Asariha ◽  
Azam Chahardoli ◽  
Farshad Qalekhani ◽  
Mahnaz Ghowsi ◽  
Mehdi Fouladi ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Antibacterial Activity ◽  
Green Synthesis ◽  
Chondroitin Sulfate ◽  
Blood Compatibility ◽  
Spherical Shape ◽  
Hemolysis Assay ◽  
Wide Range ◽  
New Strategy

introduction: The application of gold nanoparticles (GNPs) in medicine is expanding as an effective therapeutic and diagnostic compound. Different polysaccharides with high biocompatibility and hydrophilic properties have been used for synthesis and capping of GNPs. Chondroitin sulfate (CHS) as a polysaccharide possesses a wide range of biological functions e.g. anti-oxidant, anti-inflammation, anti-coagulation, anti-atherosclerosis, anti-thrombosis with insignificant immunogenicity and has not been used for the green synthesis of GNPs. Methods: GNPs were synthesized using CHS, and their physicochemical properties were evaluated. The antibacterial activity of CHS-GNPs was estimated against both gram-positive and gram-negative bacteria. The cytotoxicity of CHS and CHS-GNPs was obtained by MTT (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) test, and the electrocatalytic activity of CHS-GNPs was investigated. The blood compatibility was evaluated by the in vitro hemolysis assay. Results: The absorption band at 527 nm reveals the reduction of Au3+ into GNPs. The transmission electron microscopy (TEM) image displays the spherical shape of GNPs in the range of 5.8–31.4 nm. The CHS and CHS-GNPs at 300 µg/mL revealed a maximum DPPH (1, 1-diphenyl-2-picrylhydrazyl) scavenging activity of 73% and 65%, respectively. CHS-GNPs showed antibacterial activity against Bacillus subtilis, while CHS has no antibacterial activity. CHS-GNPs exhibited a cytotoxicity effect against MDA-MB-468 and βTC3 cancer cell lines, and the electrochemical study indicated a significant increase in electrocatalytic properties of CHS-GNPs coated electrode compared by the bare electrode. The hemolysis test proved the blood compatibility of CHS-GNPs. Conclusion: The results indicate the advantages of using CHS to produce blood-compatible GNPs with antioxidant, cytotoxic, and electrochemical properties.

scholarly journals Quantification of cristae architecture reveals time-dependent characteristics of individual mitochondria

2020 ◽  
Vol 3 (7) ◽  
pp. e201900620
Author(s):  
Mayuko Segawa ◽  
Dane M Wolf ◽  
Nan W Hultgren ◽  
David S Williams ◽  
Alexander M van der Bliek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mitochondrial Fission ◽  
Live Cell ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Fusion Event ◽  
New Strategy ◽  
Function Relationship ◽  
Relationship Of ◽  
Standard Techniques

Recent breakthroughs in live-cell imaging have enabled visualization of cristae, making it feasible to investigate the structure–function relationship of cristae in real time. However, quantifying live-cell images of cristae in an unbiased way remains challenging. Here, we present a novel, semi-automated approach to quantify cristae, using the machine-learning Trainable Weka Segmentation tool. Compared with standard techniques, our approach not only avoids the bias associated with manual thresholding but more efficiently segments cristae from Airyscan and structured illumination microscopy images. Using a cardiolipin-deficient cell line, as well as FCCP, we show that our approach is sufficiently sensitive to detect perturbations in cristae density, size, and shape. This approach, moreover, reveals that cristae are not uniformly distributed within the mitochondrion, and sites of mitochondrial fission are localized to areas of decreased cristae density. After a fusion event, individual cristae from the two mitochondria, at the site of fusion, merge into one object with distinct architectural values. Overall, our study shows that machine learning represents a compelling new strategy for quantifying cristae in living cells.

Abstract MP10: Microbiome-based Diagnostic Screening Of Cardiovascular Disease Using A Machine Learning Approach

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Sachin Aryal ◽  
Ahmad Alimadadi ◽  
Ishan Manandhar ◽  
Bina Joe ◽  
Xi Cheng
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Characteristic Curve ◽  
Feature Space ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Diagnostic Screening ◽  
New Strategy ◽  
Metagenomics Data

In recent years, the microbiome has been recognized as an important factor associated with cardiovascular disease (CVD), which is the leading cause of human mortality worldwide. Disparities in gut microbial compositions between individuals with and without CVD were reported, whereby, we hypothesized that utilizing such microbiome-based data for training with supervised machine learning (ML) models could be exploited as a new strategy for evaluation of cardiovascular health. To test our hypothesis, we analyzed the metagenomics data extracted from the American Gut Project. Specifically, 16S rRNA reads from stool samples of 478 CVD and 473 non-CVD control samples were analyzed using five supervised ML algorithms: random forest (RF), support vector machine with radial kernel (svmRadial), decision tree (DT), elastic net (ENet) and neural networks (NN). Thirty-nine differential bacterial taxa (LEfSe: LDA > 2) were identified between CVD and non-CVD groups. ML classifications, using these taxonomic features, achieved an AUC (area under the receiver operating characteristic curve) of ~0.58 (RF). However, by choosing the top 500 high-variance features of operational taxonomic units (OTUs) for training ML models, an improved AUC of ~0.65 (RF) was achieved. Further, by limiting the selection to only the top 25 highly contributing OTU features to reduce the dimensionality of feature space, the AUC was further significantly enhanced to ~0.70 (RF). In summary, this study is the first to demonstrate the successful development of a ML model using microbiome-based datasets for a systematic diagnostic screening of CVD.

Phosphorylation-regulated crosslinking of gold nanoparticles: a new strategy for colorimetric detection of protein kinase activity

The Analyst ◽  
2015 ◽  
Vol 140 (16) ◽  
pp. 5685-5691 ◽  
Author(s):  
Sujuan Sun ◽  
Haixia Shen ◽  
Chenghui Liu ◽  
Zhengping Li
Keyword(s):  
Gold Nanoparticles ◽  
Protein Kinase ◽  
Kinase Activity ◽  
Colorimetric Detection ◽  
Kinase Assay ◽  
Protein Kinase Activity ◽  
New Strategy ◽  
Peptide Phosphorylation

A facile colorimetric protein kinase assay has been developed based on the peptide phosphorylation-tuned crosslinking and aggregation of gold nanoparticles.

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