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Scientific Papers
Publications citing the applications of openQCM (by Novaetech S.r.l.) instruments and accessories in scientific research.
The list of scientific papers published on the most important journals showing the usage of openQCM in several scientific fields, such as thin film deposition, chemical sensors, biological research and biosensors.
Because of the large number of publications, we are reorganizing everything by subject areas. This will take some time. Thank you for your patience
2024
Dattilo, Marco; Patitucci, Francesco; Motta, Marisa Francesca; Prete, Sabrina; Galeazzi, Roberta; Franzè, Silvia; Perrotta, Ida; Cavarelli, Mariangela; Parisi, Ortensia Ilaria; Puoci, Francesco
In: Colloids and Surfaces B: Biointerfaces, pp. 114408, 2024, ISSN: 0927-7765.
Abstract | Links | BibTeX | Tags: Emulsion Polymerization, Molecular Recognition, Molecularly Imprinted Polymers (MIPs), Omicron Variant, openQCM NEXT, QCM, Quartz Crystal Microbalance, SARS-CoV-2, Spike Protein Receptor-Binding Domain (RBD)
@article{DATTILO2024114408,
title = {Molecularly Imprinted Polymers (MIPs) for SARS-CoV-2 Omicron variant inhibition: an alternative approach to address the challenge of emerging zoonoses},
author = {Marco Dattilo and Francesco Patitucci and Marisa Francesca Motta and Sabrina Prete and Roberta Galeazzi and Silvia Franzè and Ida Perrotta and Mariangela Cavarelli and Ortensia Ilaria Parisi and Francesco Puoci},
url = {https://www.sciencedirect.com/science/article/pii/S0927776524006672},
doi = {https://doi.org/10.1016/j.colsurfb.2024.114408},
issn = {0927-7765},
year = {2024},
date = {2024-11-26},
urldate = {2024-01-01},
journal = {Colloids and Surfaces B: Biointerfaces},
pages = {114408},
abstract = {ABSTRACT
Emerging zoonoses pose significant public health risks and necessitate rapid and effective treatment responses. This study enhances the technology for preparing Molecularly Imprinted Polymers (MIPs), which function as synthetic nanoparticles targeting SARS-CoV-2 receptor-binding domain (RBD), specifically the Omicron variant, thereby inhibiting its function. This study builds on previous findings by introducing precise adjustments in the formulation and process conditions to enhance particle stability and ensure better control over size and distribution, thereby overcoming the issues identified in earlier research. Following docking studies, imprinted nanoparticles were synthesized via inverse microemulsion polymerization and characterized in terms of size, morphology and surface charge. The selective recognition properties and ability of MIPs to obstruct the interaction between ACE2 and the RBD of SARS-CoV-2 were assessed in vitro, using Non-Imprinted Polymers (NIPs) as controls, and rebinding studies were conducted utilizing a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D). The synthesized nanoparticles exhibited uniform dispersion and had a consistent diameter within the nanoscale range. MIPs demonstrated significant recognition properties and exhibited a concentration-dependent ability to reduce RBD binding to ACE2 without cytotoxic or sensitizing effects. MIPs-based platforms offer a promising alternative to natural antibodies for treating SARS-CoV-2 infections, therefore representing a versatile platform for managing emerging zoonoses.},
keywords = {Emulsion Polymerization, Molecular Recognition, Molecularly Imprinted Polymers (MIPs), Omicron Variant, openQCM NEXT, QCM, Quartz Crystal Microbalance, SARS-CoV-2, Spike Protein Receptor-Binding Domain (RBD)},
pubstate = {published},
tppubtype = {article}
}
ABSTRACT
Emerging zoonoses pose significant public health risks and necessitate rapid and effective treatment responses. This study enhances the technology for preparing Molecularly Imprinted Polymers (MIPs), which function as synthetic nanoparticles targeting SARS-CoV-2 receptor-binding domain (RBD), specifically the Omicron variant, thereby inhibiting its function. This study builds on previous findings by introducing precise adjustments in the formulation and process conditions to enhance particle stability and ensure better control over size and distribution, thereby overcoming the issues identified in earlier research. Following docking studies, imprinted nanoparticles were synthesized via inverse microemulsion polymerization and characterized in terms of size, morphology and surface charge. The selective recognition properties and ability of MIPs to obstruct the interaction between ACE2 and the RBD of SARS-CoV-2 were assessed in vitro, using Non-Imprinted Polymers (NIPs) as controls, and rebinding studies were conducted utilizing a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D). The synthesized nanoparticles exhibited uniform dispersion and had a consistent diameter within the nanoscale range. MIPs demonstrated significant recognition properties and exhibited a concentration-dependent ability to reduce RBD binding to ACE2 without cytotoxic or sensitizing effects. MIPs-based platforms offer a promising alternative to natural antibodies for treating SARS-CoV-2 infections, therefore representing a versatile platform for managing emerging zoonoses.
Emerging zoonoses pose significant public health risks and necessitate rapid and effective treatment responses. This study enhances the technology for preparing Molecularly Imprinted Polymers (MIPs), which function as synthetic nanoparticles targeting SARS-CoV-2 receptor-binding domain (RBD), specifically the Omicron variant, thereby inhibiting its function. This study builds on previous findings by introducing precise adjustments in the formulation and process conditions to enhance particle stability and ensure better control over size and distribution, thereby overcoming the issues identified in earlier research. Following docking studies, imprinted nanoparticles were synthesized via inverse microemulsion polymerization and characterized in terms of size, morphology and surface charge. The selective recognition properties and ability of MIPs to obstruct the interaction between ACE2 and the RBD of SARS-CoV-2 were assessed in vitro, using Non-Imprinted Polymers (NIPs) as controls, and rebinding studies were conducted utilizing a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D). The synthesized nanoparticles exhibited uniform dispersion and had a consistent diameter within the nanoscale range. MIPs demonstrated significant recognition properties and exhibited a concentration-dependent ability to reduce RBD binding to ACE2 without cytotoxic or sensitizing effects. MIPs-based platforms offer a promising alternative to natural antibodies for treating SARS-CoV-2 infections, therefore representing a versatile platform for managing emerging zoonoses.
Kunčák, Jakub; Forinová, Michala; Pilipenco, Alina; Procházka, Viktor; Horák, Petr; Sycheva, Sofya Dmitrievna; Deyneka, Ivan Gennadievich; Vaisocherová-Lísalová, Hana
Automating data classification for label-free point-of-care biosensing in real complex samples Journal Article
In: Sensors and Actuators A: Physical, pp. 115501, 2024, ISSN: 0924-4247.
Abstract | Links | BibTeX | Tags: . O157:H7, automatic data classification, Detection of pathogens, openQCM Q-1, Point-of-care biosensors, QCM-D, Quartz Crystal Microbalance, SARS-CoV-2
@article{KUNCAK2024115501,
title = {Automating data classification for label-free point-of-care biosensing in real complex samples},
author = {Jakub Kunčák and Michala Forinová and Alina Pilipenco and Viktor Procházka and Petr Horák and Sofya Dmitrievna Sycheva and Ivan Gennadievich Deyneka and Hana Vaisocherová-Lísalová},
url = {https://www.sciencedirect.com/science/article/pii/S0924424724004953},
doi = {https://doi.org/10.1016/j.sna.2024.115501},
issn = {0924-4247},
year = {2024},
date = {2024-05-19},
urldate = {2024-01-01},
journal = {Sensors and Actuators A: Physical},
pages = {115501},
abstract = {Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. While laboratory-based devices commonly employ various techniques to mitigate noise, signal drifts, fluidic artifacts, and other system imperfections, their simple cost-effective POC counterparts designed for field use frequently lack such capabilities. This paper addresses this gap by introducing a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. The procedure integrates classical analytical tools such as filtering, data selection, baseline de-drifting, and result calculation in tailored successive steps, considering the nature of the sensor signal and the challenges posed by real-world media. We show that the developed procedure exhibits exceptional robustness across different biosensing assays and complex real-world media. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8% and 90.9% for E. coli and SARS-CoV-2, respectively, without extensive machine learning. Furthermore, we analyse the sensitivity of the procedure to variations of input parameters and with examples discuss key factors influencing overall procedure accuracy. Our results suggest that this exceptionally robust method holds potential as a straightforward tool for automating sample classification in point-of-care diagnostics, underpinning its promising broader applicability.},
keywords = {. O157:H7, automatic data classification, Detection of pathogens, openQCM Q-1, Point-of-care biosensors, QCM-D, Quartz Crystal Microbalance, SARS-CoV-2},
pubstate = {published},
tppubtype = {article}
}
Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. While laboratory-based devices commonly employ various techniques to mitigate noise, signal drifts, fluidic artifacts, and other system imperfections, their simple cost-effective POC counterparts designed for field use frequently lack such capabilities. This paper addresses this gap by introducing a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. The procedure integrates classical analytical tools such as filtering, data selection, baseline de-drifting, and result calculation in tailored successive steps, considering the nature of the sensor signal and the challenges posed by real-world media. We show that the developed procedure exhibits exceptional robustness across different biosensing assays and complex real-world media. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8% and 90.9% for E. coli and SARS-CoV-2, respectively, without extensive machine learning. Furthermore, we analyse the sensitivity of the procedure to variations of input parameters and with examples discuss key factors influencing overall procedure accuracy. Our results suggest that this exceptionally robust method holds potential as a straightforward tool for automating sample classification in point-of-care diagnostics, underpinning its promising broader applicability.
Kunčák, Jakub; Forinová, Michala; Pilipenco, Alina; Procházka, Viktor; Horák, Petr; Dmitrievna, Sycheva Sofya; Deyneka, Ivan Gennadievich; Vaisocherová-L'isalová, Hana
In: Available at SSRN 4756321, 2024.
Abstract | Links | BibTeX | Tags: automatic data classification, Detection of pathogens, E. coli O157:H7, openQCM, openQCM Q-1, Point-of-care biosensors, Quartz Crystal Microbalance, SARS-CoV-2
@article{kunvcak2024automating,
title = {Automating Data Analysis for Point-of-Care Label-Free Surface-Based Affinity Biosensors Dealing with Complex Biological Samples: Escherichia Coli O157: H7 and Sars-Cov-2 Case Studies},
author = {Jakub Kunčák and Michala Forinová and Alina Pilipenco and Viktor Procházka and Petr Horák and Sycheva Sofya Dmitrievna and Ivan Gennadievich Deyneka and Hana Vaisocherová-L'isalová},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4756321},
year = {2024},
date = {2024-03-20},
urldate = {2024-03-20},
journal = {Available at SSRN 4756321},
abstract = {Surface-based affinity biosensors offer a promising avenue for point-of-care (POC) detection of pathogens in real-world samples. While laboratory-based devices commonly employ various techniques to compensate for noise, signal drifts, fluidic artifacts, and other system imperfections, their POC counterparts aiming at providing simple cost-effective detection platforms for field use, often lack these qualities. This paper addresses this gap by introducing a procedure for automatic classification of pathogen presence in unprocessed liquids from direct detection data measured by a simple POC-relevant quartz crystal microbalance sensor device. By considering the nature of the sensor signal and the sources of its imperfections in real-world media, a straightforward procedure integrates “classical” analytical tools (filtering, data selection, baseline de-drifting, and result calculation) in successive steps to automate sample classification without the need for extensive machine learning. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8% and 90.9% for E. coli and SARS-CoV-2, respectively. Furthermore, we analyse the sensitivity of the routine to variations of input parameters and with examples discuss the key factors influencing the accuracy of the overall procedure. The results show that the developed method exhibits exceptional robustness across different biosensing assays and complex real-world media, highlighting its promising broader applicability in point-of-care diagnostics.},
keywords = {automatic data classification, Detection of pathogens, E. coli O157:H7, openQCM, openQCM Q-1, Point-of-care biosensors, Quartz Crystal Microbalance, SARS-CoV-2},
pubstate = {published},
tppubtype = {article}
}
Surface-based affinity biosensors offer a promising avenue for point-of-care (POC) detection of pathogens in real-world samples. While laboratory-based devices commonly employ various techniques to compensate for noise, signal drifts, fluidic artifacts, and other system imperfections, their POC counterparts aiming at providing simple cost-effective detection platforms for field use, often lack these qualities. This paper addresses this gap by introducing a procedure for automatic classification of pathogen presence in unprocessed liquids from direct detection data measured by a simple POC-relevant quartz crystal microbalance sensor device. By considering the nature of the sensor signal and the sources of its imperfections in real-world media, a straightforward procedure integrates “classical” analytical tools (filtering, data selection, baseline de-drifting, and result calculation) in successive steps to automate sample classification without the need for extensive machine learning. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8% and 90.9% for E. coli and SARS-CoV-2, respectively. Furthermore, we analyse the sensitivity of the routine to variations of input parameters and with examples discuss the key factors influencing the accuracy of the overall procedure. The results show that the developed method exhibits exceptional robustness across different biosensing assays and complex real-world media, highlighting its promising broader applicability in point-of-care diagnostics.
2023
Stuart, Daniel David
Advancing Label Free Detection Techniques Through Surface Based Sensing and Machine Learning PhD Thesis
University of California, Riverside, 2023.
Abstract | Links | BibTeX | Tags: openQCM, openQCM Q-1, QCM, QCM sensor, QCM-D, SARS-CoV-2
@phdthesis{stuart2023advancing,
title = {Advancing Label Free Detection Techniques Through Surface Based Sensing and Machine Learning},
author = {Daniel David Stuart},
url = {https://escholarship.org/uc/item/2cr290xf},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
school = {University of California, Riverside},
abstract = {High-performing sensors have played a pivotal role in expanding our understanding of biological systems, disease diagnosis, environmental monitoring, and national security. The technical capability they provide has enabled us to obtain in-depth information and insights towards improving human health. One area of sensing that exemplifies this progress is the development of label free sensors which allow direct analysis of molecular interactions. Among these methods surface plasmon resonance (SPR) has emerged as a powerful, real-time detection technique for studies of biological interactions, drug discovery, and other important aspects that lead to new disease diagnosis. Through the implementation of new materials and methods SPR and other label-free sensors have expanded the range of analytes tested. This Dissertation aims to establish improvements in materials and methodologies through technology advancement for solving current sensor limitations. The work focuses on enhancing sensing signal while limiting the impact of nonspecific interactions on label-free methods, providing expanded molecular identity information, and overcoming challenges encountered when detecting small molecules. Chapters 2, 3, and 4 demonstrate advancements in unique biomimetic surfaces to enable the exploration of new biological systems as well as block nonspecific interactions. Chapter 2 focuses on a tethered membrane system to promote incorporation of relevant constituents into lipid bilayers without compromising membrane mobility property and drug delivery interactions. Chapter 3 employs a charged membrane to suppress nonspecific interactions and explores the working mechanism. Chapter 4 expands the capabilities of label-free sensing systems through development of curved membrane platforms that mitigate the decay limits through modeling of lipid distribution in vesicles. Chapter 5 exploits the plasmonic properties of SPR chips to enhance signals in matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) , which is further facilitated with development of machine learning models to identify bacterial species. In Chapter 6, the limitation of small molecule analysis with SPR is tackled by taking advantage of pressure effects to provide specific gas sensing. Each of these Chapters provides novel advancements in sensing capabilities by addressing performance-impairing limitations in label-free sensors. Research goals are achieved both from improvements to SPR systems and incorporation of other methodologies to augment SPR results.},
keywords = {openQCM, openQCM Q-1, QCM, QCM sensor, QCM-D, SARS-CoV-2},
pubstate = {published},
tppubtype = {phdthesis}
}
High-performing sensors have played a pivotal role in expanding our understanding of biological systems, disease diagnosis, environmental monitoring, and national security. The technical capability they provide has enabled us to obtain in-depth information and insights towards improving human health. One area of sensing that exemplifies this progress is the development of label free sensors which allow direct analysis of molecular interactions. Among these methods surface plasmon resonance (SPR) has emerged as a powerful, real-time detection technique for studies of biological interactions, drug discovery, and other important aspects that lead to new disease diagnosis. Through the implementation of new materials and methods SPR and other label-free sensors have expanded the range of analytes tested. This Dissertation aims to establish improvements in materials and methodologies through technology advancement for solving current sensor limitations. The work focuses on enhancing sensing signal while limiting the impact of nonspecific interactions on label-free methods, providing expanded molecular identity information, and overcoming challenges encountered when detecting small molecules. Chapters 2, 3, and 4 demonstrate advancements in unique biomimetic surfaces to enable the exploration of new biological systems as well as block nonspecific interactions. Chapter 2 focuses on a tethered membrane system to promote incorporation of relevant constituents into lipid bilayers without compromising membrane mobility property and drug delivery interactions. Chapter 3 employs a charged membrane to suppress nonspecific interactions and explores the working mechanism. Chapter 4 expands the capabilities of label-free sensing systems through development of curved membrane platforms that mitigate the decay limits through modeling of lipid distribution in vesicles. Chapter 5 exploits the plasmonic properties of SPR chips to enhance signals in matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) , which is further facilitated with development of machine learning models to identify bacterial species. In Chapter 6, the limitation of small molecule analysis with SPR is tackled by taking advantage of pressure effects to provide specific gas sensing. Each of these Chapters provides novel advancements in sensing capabilities by addressing performance-impairing limitations in label-free sensors. Research goals are achieved both from improvements to SPR systems and incorporation of other methodologies to augment SPR results.
Qi, Shaojun; Kiratzis, Ioannis; Adoni, Pavan; Tuekprakhon, Aekkachai; Hill, Harriet James; Stamataki, Zania; Nabi, Aneesa; Waugh, David; Rodriguez, Javier Rodriguez; Clarke, Stuart Matthew; others,
Porous Cellulose Thin Films as Sustainable and Effective Antimicrobial Surface Coatings Journal Article
In: ACS Applied Materials & Interfaces, 2023.
Abstract | Links | BibTeX | Tags: antimicrobial, cellulose, evaporation, film, openQCM NEXT, QCM, QCM-D, Quartz Crystal Microbalance, robustness, SARS-CoV-2
@article{qi2023porous,
title = {Porous Cellulose Thin Films as Sustainable and Effective Antimicrobial Surface Coatings},
author = {Shaojun Qi and Ioannis Kiratzis and Pavan Adoni and Aekkachai Tuekprakhon and Harriet James Hill and Zania Stamataki and Aneesa Nabi and David Waugh and Javier Rodriguez Rodriguez and Stuart Matthew Clarke and others},
url = {https://pubs.acs.org/doi/full/10.1021/acsami.2c23251},
doi = {https://doi.org/10.1021/acsami.2c23251},
year = {2023},
date = {2023-03-29},
urldate = {2023-01-01},
journal = {ACS Applied Materials & Interfaces},
publisher = {ACS Publications},
abstract = {In the present work, we developed an effective antimicrobial surface film based on sustainable microfibrillated cellulose. The resulting porous cellulose thin film is barely noticeable to human eyes due to its submicrometer thickness, of which the surface coverage, porosity, and microstructure can be modulated by the formulations and the coating process. Using goniometers and a quartz crystal microbalance, we observed a threefold reduction in water contact angles and accelerated water evaporation kinetics on the cellulose film (more than 50% faster than that on a flat glass surface). The porous cellulose film exhibits a rapid inactivation effect against SARS-CoV-2 in 5 min, following deposition of virus-loaded droplets, and an exceptional ability to reduce contact transfer of liquid, e.g., respiratory droplets, to surfaces such as an artificial skin by 90% less than that from a planar glass substrate. It also shows excellent antimicrobial performance in inhibiting the growth of both Gram-negative and Gram-positive bacteria (Escherichia coli and Staphylococcus epidermidis) due to the intrinsic porosity and hydrophilicity. Additionally, the cellulose film shows nearly 100% resistance to scraping in dry conditions due to its strong affinity to the supporting substrate but with good removability once wetted with water, suggesting its practical suitability for daily use. Importantly, the coating can be formed on solid substrates readily by spraying, which requires solely a simple formulation of a plant-based cellulose material with no chemical additives, rendering it a scalable, affordable, and green solution as antimicrobial surface coating. Implementing such cellulose films could thus play a significant role in controlling future pan- and epidemics, particularly during the initial phase when suitable medical intervention needs to be developed and deployed.},
key = {cellulose, film, antimicrobial, evaporation, SARS-CoV-2, robustness},
keywords = {antimicrobial, cellulose, evaporation, film, openQCM NEXT, QCM, QCM-D, Quartz Crystal Microbalance, robustness, SARS-CoV-2},
pubstate = {published},
tppubtype = {article}
}
In the present work, we developed an effective antimicrobial surface film based on sustainable microfibrillated cellulose. The resulting porous cellulose thin film is barely noticeable to human eyes due to its submicrometer thickness, of which the surface coverage, porosity, and microstructure can be modulated by the formulations and the coating process. Using goniometers and a quartz crystal microbalance, we observed a threefold reduction in water contact angles and accelerated water evaporation kinetics on the cellulose film (more than 50% faster than that on a flat glass surface). The porous cellulose film exhibits a rapid inactivation effect against SARS-CoV-2 in 5 min, following deposition of virus-loaded droplets, and an exceptional ability to reduce contact transfer of liquid, e.g., respiratory droplets, to surfaces such as an artificial skin by 90% less than that from a planar glass substrate. It also shows excellent antimicrobial performance in inhibiting the growth of both Gram-negative and Gram-positive bacteria (Escherichia coli and Staphylococcus epidermidis) due to the intrinsic porosity and hydrophilicity. Additionally, the cellulose film shows nearly 100% resistance to scraping in dry conditions due to its strong affinity to the supporting substrate but with good removability once wetted with water, suggesting its practical suitability for daily use. Importantly, the coating can be formed on solid substrates readily by spraying, which requires solely a simple formulation of a plant-based cellulose material with no chemical additives, rendering it a scalable, affordable, and green solution as antimicrobial surface coating. Implementing such cellulose films could thus play a significant role in controlling future pan- and epidemics, particularly during the initial phase when suitable medical intervention needs to be developed and deployed.
Bulut, Aliye; Temur, Betul Z; Kirimli, Ceyhun E; Gok, Ozgul; Balcioglu, Bertan K; Ozturk, Hasan U; Uyar, Neval Y; Kanlidere, Zeynep; Kocagoz, Tanil; Can, Ozge
A Novel Peptide-Based Detection of SARS-CoV-2 Antibodies Journal Article
In: Biomimetics, vol. 8, no. 1, pp. 89, 2023.
Abstract | Links | BibTeX | Tags: antibody detection, biosensors, peptide mimetics, QCM, Quartz Crystal Microbalance, SARS-CoV-2
@article{bulut2023novel,
title = {A Novel Peptide-Based Detection of SARS-CoV-2 Antibodies},
author = {Aliye Bulut and Betul Z Temur and Ceyhun E Kirimli and Ozgul Gok and Bertan K Balcioglu and Hasan U Ozturk and Neval Y Uyar and Zeynep Kanlidere and Tanil Kocagoz and Ozge Can},
url = {https://www.mdpi.com/2313-7673/8/1/89},
doi = {https://doi.org/10.3390/biomimetics8010089},
year = {2023},
date = {2023-02-22},
urldate = {2023-02-22},
journal = {Biomimetics},
volume = {8},
number = {1},
pages = {89},
publisher = {MDPI},
abstract = {The need for rapidly developed diagnostic tests has gained significant attention after the recent pandemic. Production of neutralizing antibodies for vaccine development or antibodies to be used in diagnostic tests usually require the usage of recombinant proteins representing the infectious agent. However, peptides that can mimic these recombinant proteins may be rapidly utilized, especially in emergencies such as the recent outbreak. Here, we report two peptides that mimic the receptor binding domain of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and investigate their binding behavior against the corresponding human immunoglobulin G and immunoglobulin M (IgG and IgM) antibodies in a clinical sample using a quartz crystal microbalance (QCM) sensor. These peptides were immobilized on a QCM sensor surface, and their binding behavior was studied against a clinical serum sample that was previously determined to be IgG and IgM-positive. It was determined that designed peptides bind to SARS-CoV-2 antibodies in a clinical sample. These peptides might be useful for the detection of SARS-CoV-2 antibodies using different methods such as enzyme-linked immunosorbent assay (ELISA) or lateral flow assays. A similar platform might prove to be useful for the detection and development of antibodies in other infections.},
key = {peptide mimetics, SARS-CoV-2, biosensor, quartz crystal microbalance, antibody detection},
keywords = {antibody detection, biosensors, peptide mimetics, QCM, Quartz Crystal Microbalance, SARS-CoV-2},
pubstate = {published},
tppubtype = {article}
}
The need for rapidly developed diagnostic tests has gained significant attention after the recent pandemic. Production of neutralizing antibodies for vaccine development or antibodies to be used in diagnostic tests usually require the usage of recombinant proteins representing the infectious agent. However, peptides that can mimic these recombinant proteins may be rapidly utilized, especially in emergencies such as the recent outbreak. Here, we report two peptides that mimic the receptor binding domain of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and investigate their binding behavior against the corresponding human immunoglobulin G and immunoglobulin M (IgG and IgM) antibodies in a clinical sample using a quartz crystal microbalance (QCM) sensor. These peptides were immobilized on a QCM sensor surface, and their binding behavior was studied against a clinical serum sample that was previously determined to be IgG and IgM-positive. It was determined that designed peptides bind to SARS-CoV-2 antibodies in a clinical sample. These peptides might be useful for the detection of SARS-CoV-2 antibodies using different methods such as enzyme-linked immunosorbent assay (ELISA) or lateral flow assays. A similar platform might prove to be useful for the detection and development of antibodies in other infections.
