scholarly journals Stochastic biosensors based on N- and S-doped graphene for the enantioanalysis of aspartic acid in biological samples

RSC Advances ◽  
2021 ◽  
Vol 11 (38) ◽  
pp. 23301-23309
Author(s):  
Raluca-Ioana Stefan-van Staden ◽  
Damaris-Cristina Gheorghe ◽  
Ruxandra-Maria Ilie-Mihai ◽  
Lucian-Barbu Tudoran ◽  
Stela Maria Pruneanu
Keyword(s):  
Gastric Cancer ◽  
Aspartic Acid ◽  
Biological Samples ◽  
Doped Graphene

Stochastic biosensors based on N- and S-doped graphene modified with hemin or α-hemolysin contributed to establishing the metabolomics of gastric cancer by performing the enantioanalysis of aspartic acid in different biological samples.

scholarly journals Fast Screening Method of Biological Samples Based on Needle Stochastic Sensors for Early Detection of Gastric Cancer

2020 ◽  
Vol 72 (4) ◽  
pp. 22-34
Author(s):  
Ruxandra-Maria Ilie-Mihai ◽  
Damaris-Cristina Gheorghe ◽  
Raluca-Ioana Stefan-Van Staden ◽  
Alexandrina Lungu-Moscalu ◽  
Maria Coros ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Detection ◽  
Biological Samples ◽  
Early Detection Of Cancer ◽  
Tumor Tissue ◽  
Screening Method ◽  
Protoporphyrin Ix ◽  
Fast Screening ◽  
Doped Graphene ◽  
Linear Concentration

Early detection of cancer is essential for saving the life of patients. Needle stochastic sensors were proposed as new tools for the fast screening of biological samples. The design of the stochastic sensors was based on the immobilization of protoporphyrin IX in pastes of S-doped graphene. The selected biomarkers were CEA and CA19-9. High sensitivities, and wide linear concentration ranges as well as low limits of quantification were achieved. The proposed sensors were validated; student t-test shown that the proposed needle stochastic sensors can be reliably used for the pattern recognition and quantification of CEA and CA19-9 in whole blood, gastric tumor tissue, saliva, and urine.

(Invited) Fast Screening Tests for Early Diagnosis of Gastric Cancer, Based on Molecular Recognition and Assay of Maspin in Biological Samples

2021 ◽  
Vol MA2021-02 (56) ◽  
pp. 1655-1655
Author(s):  
Raluca Van Staden ◽  
Ruxandra Maria Ilie-Mihai ◽  
Damaris Cristina Ghrorghe ◽  
Iuliana Mihaela Bogea
Keyword(s):  
Gastric Cancer ◽  
Molecular Recognition ◽  
Early Diagnosis ◽  
Biological Samples ◽  
Screening Tests ◽  
Fast Screening

scholarly journals The Fast Screening Method of Biological Samples for Early Diagnosis of Gastric Cancer

2020 ◽  
Vol 4 (3) ◽  
pp. 25-30
Author(s):  
Raluca Ioana Stefan-van Staden ◽  
Ruxandra Maria Ilie-Mihai ◽  
Simona Gurzu ◽  
◽  
◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Biological Samples ◽  
Screening Method ◽  
Fast Screening

Graphene-based stochastic sensors for pattern recognition of gastric cancer biomarkers in biological fluids

2019 ◽  
Vol 23 (11n12) ◽  
pp. 1365-1370 ◽  
Author(s):  
Raluca-Ioana Stefan-van Staden ◽  
Ruxandra-Maria Ilie-Mihai ◽  
Florina Pogacean ◽  
Stela Pruneanu
Keyword(s):  
Gastric Cancer ◽  
Pattern Recognition ◽  
Biological Samples ◽  
Whole Blood ◽  
Biological Fluids ◽  
Protoporphyrin Ix ◽  
Cancer Biomarkers ◽  
Exfoliated Graphene ◽  
Blood And Urine Samples

This paper proposes pattern recognition of gastric cancer biomarkers CEA, CA19-9 and p53 in whole blood and urine samples using a stochastic sensor based on exfoliated graphene (E-NGr) paste modified with protoporphyrin IX. The proposed sensor covered large ranges of concentrations: 1 × 10[Formula: see text]–1 × 10[Formula: see text]g/mL for CEA, 1 × 10[Formula: see text]–1 × 102 U/mL for CA19-9, and 0.2–5.0 [Formula: see text]g/mL for p53. These ranges allowed the determination of the three biomarkers from early to latest stages of gastric cancer. Validation of the pattern recognition of gastric cancer biomarkers was accomplished using biological samples: whole blood and urine.

A dopamine-modulated nitrogen-doped graphene quantum dot fluorescence sensor for the detection of glutathione in biological samples

2016 ◽  
Vol 40 (10) ◽  
pp. 8911-8917 ◽  
Author(s):  
Zhenzhen Liu ◽  
Yan Gong ◽  
Zhefeng Fan
Keyword(s):  
Quantum Dot ◽  
Biological Samples ◽  
Fluorescent Sensor ◽  
Fluorescence Sensor ◽  
Selective Detection ◽  
Graphene Quantum Dot ◽  
Redox Mechanism ◽  
Nitrogen Doped ◽  
Nitrogen Doped Graphene ◽  
Doped Graphene

A dopamine-modulated nitrogen-doped graphene quantum dot (N-GQD) system was explored to develop a fluorescent sensor based on a chemical redox mechanism for the facile, sensitive and selective detection of glutathione (GSH) in biological samples.

Needle stochastic sensors for on-site fast recognition and quantification of biomarkers for gastric cancer in biological samples

2020 ◽  
Vol 44 (46) ◽  
pp. 20203-20211
Author(s):  
Raluca-Ioana Stefan-van Staden ◽  
Ruxandra-Maria Ilie-Mihai ◽  
Florina Pogacean ◽  
Stela Maria Pruneanu
Keyword(s):  
Gastric Cancer ◽  
Biological Samples ◽  
Fast Recognition

N-Doped graphenes recognised CA19-9, CEA, and p53 in biological samples.

An Efficient Statistic for Determining the Diameter of Thin Sectioned Uniform Spheres from Measurements of Biological Samples

1974 ◽  
Vol 32 ◽  
pp. 114-115
Author(s):  
W. R. Schucany ◽  
G. H. Kelsoe ◽  
V. F. Allison
Keyword(s):  
Biological Samples ◽  
Traditional Method ◽  
Cell Types ◽  
Accurate Estimation ◽  
Morphological Identification ◽  
Sectioned Material ◽  
Maximal Diameter ◽  
Similar Cell

Accurate estimation of the size of spheroid organelles from thin sectioned material is often necessary, as uniquely homogenous populations of organelles such as vessicles, granules, or nuclei often are critically important in the morphological identification of similar cell types. However, the difficulty in obtaining accurate diameter measurements of thin sectioned organelles is well known. This difficulty is due to the extreme tenuity of the sectioned material as compared to the size of the intact organelle. In populations where low variance is suspected the traditional method of diameter estimation has been to measure literally hundreds of profiles and to describe the “largest” as representative of the “approximate maximal diameter”.

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