Analytical and Computational Systems in Biosensing

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "(Bio)chemical Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10244

Special Issue Editors


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Guest Editor
Group of Applied Analytical Chemistry, Campus da Zapateira s/n, University of A Coruña, 15071 A Coruña, Spain
Interests: infrared analysis; chemometrics; environmental analysis; petrochemistry; quality control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science and Information Technologies, University of A Coruña, Campus Elviña, 15071 A Coruña, Spain
Interests: evolutionary computation; artificial neural networks; artificial intelligence; feature selection; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this SARS-CoV-2 pandemic era health and wellbeing are major desires of all humankind. Thus, research on both analytical and computational approaches that can be used in biosensing constitute scientific and social hot topics nowadays.

This special issue aims to collect ongoing, cutting-edge studies that deal with the development of sensors or sensing systems that can be applied in the biological (consider this term broadly) world. As a matter of example: Analytical Chemistry, Computational Science, imaging, PCR and qPCR, miniaturization, etc. Applications presenting in-silico, in-vitro and in-vivo research in the analytical, medical, biological and/or point-of-care diagnosis fields will be welcomed as well.

This new section in Chemosensors is a good opportunity to present advances in analytical instrumentation, and/or its applications, as well as computational approaches, like machine learning, artificial neural networks, data mining, pattern recognition, classification and modelling techniques and feature selection, among others. Multidisciplinary approaches will also be appreciated.

We kindly invite researchers and investigators to contribute their original research or review articles to this Special Issue.

Dr. Jose Manuel Andrade
Dr. Marcos Gestal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Chemosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Analytical Chemistry Analytical Instrumentation
  • Artificial Intelligence
  • Biosensors
  • Chemometrics
  • Computer Science
  • Data Science
  • Health and wellbeing Monitoring
  • Machine Learning
  • Point of care diagnostics
  • Sensor systems

Published Papers (5 papers)

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Research

15 pages, 3595 KiB  
Article
Design of a Decision Support System to Operate a NO2 Gas Sensor Using Machine Learning, Sensitive Analysis and Conceptual Control Process Modelling
by Mohammad Gheibi, Hadi Taghavian, Reza Moezzi, Stanislaw Waclawek, Jindrich Cyrus, Anna Dawiec-Lisniewska, Jan Koci and Masoud Khaleghiabbasabadi
Chemosensors 2023, 11(2), 126; https://doi.org/10.3390/chemosensors11020126 - 08 Feb 2023
Cited by 3 | Viewed by 2094
Abstract
The most advantageous method for detecting dangerous gases and reducing the risk of potential environmental toxicity effects is the use of innovative gas sensing systems. However, designing effective sensors requires a complex process of synthesizing functional nanoparticles, which is a costly process. Additionally, [...] Read more.
The most advantageous method for detecting dangerous gases and reducing the risk of potential environmental toxicity effects is the use of innovative gas sensing systems. However, designing effective sensors requires a complex process of synthesizing functional nanoparticles, which is a costly process. Additionally, practical operation of the toxic gas sensors always carries a significant cost along with a considerable risk of hazardous gas emissions. Machine learning algorithms may be used to accurately automate the behavior of the sensors to eliminate the abovementioned deficiencies. In the present research, there are three different factors involved in the optimization of NO2 sensing by means of the response surface methodology (RSM). Two main functions of sensor efficiency, namely sensitivity and response time, are predicted according to the Fe3O4 additive (%), input NO2 (ppm), and response time/sensitivity, and moreover, the execution of a controlling system of the sensor network using the Jacobson model is proposed. The machine learning computations are implemented by Meta.RegressionByDiscretization, M5.Rules, Lazy KStar, and Gaussian Processes algorithms. The outcomes illustrate that the best gas sensor efficiency predictions are related to M5.Rules and Lazy KStar, with a correlation coefficient of more than 96%. The best performance of machine learning computations can be found in the range of 8–10-fold in training and testing arrangements. Meanwhile, the ANOVA assessment confirmed that the most important features in the prediction of response time and sensitivity are NO2 concentration and response time, respectively, with the lowest p-value recorded. The outcomes illustrated that with combinations of RSM, machine learning, and the Jacobson model as a controller, a decision support system can be presented for the NO2 gas sensor system. Full article
(This article belongs to the Special Issue Analytical and Computational Systems in Biosensing)
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12 pages, 2673 KiB  
Article
Identification of Distinct and Common Subpopulations of Myxoid Liposarcoma and Ewing Sarcoma Cells Using Self-Organizing Maps
by Amin Forootan, Daniel Andersson, Soheila Dolatabadi, David Svec, José Andrade and Anders Ståhlberg
Chemosensors 2023, 11(1), 67; https://doi.org/10.3390/chemosensors11010067 - 14 Jan 2023
Viewed by 1685
Abstract
Myxoid liposarcoma and Ewing sarcoma are the two most common tumor types that are characterized by the FET (FUS, EWSR1 and TAF15) fusion oncogenes. These FET fusion oncogenes are considered to have the same pathological mechanism. However, the cellular similarities [...] Read more.
Myxoid liposarcoma and Ewing sarcoma are the two most common tumor types that are characterized by the FET (FUS, EWSR1 and TAF15) fusion oncogenes. These FET fusion oncogenes are considered to have the same pathological mechanism. However, the cellular similarities between cells from the different tumor entities remain unknown. Here, we profiled individual myxoid liposarcoma and Ewing sarcoma cells to determine common gene expression signatures. Five cell lines were analyzed, targeting 76 different genes. We employed unsupervised clustering, focusing on self-organizing maps, to identify biologically relevant subpopulations of tumor cells. In addition, we outlined the basic concepts of self-organizing maps. Principal component analysis and a t-distributed stochastic neighbor embedding plot showed gradual differences among all cells. However, we identified five distinct and robust subpopulations using self-organizing maps. Most cells were similar to other cells within the same tumor entity, but four out of five groups contained both myxoid liposarcoma and Ewing sarcoma cells. The major difference between the groups was the overall transcriptional activity, which could be linked to cell cycle regulation. We conclude that self-organizing maps are useful tools to define biologically relevant subpopulations and that myxoid liposarcoma and Ewing sarcoma exhibit cells with similar gene expression signatures. Full article
(This article belongs to the Special Issue Analytical and Computational Systems in Biosensing)
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12 pages, 4656 KiB  
Article
Machine Learning-Based Radon Monitoring System
by Diego Valcarce, Alberto Alvarellos, Juan Ramón Rabuñal, Julián Dorado and Marcos Gestal
Chemosensors 2022, 10(7), 239; https://doi.org/10.3390/chemosensors10070239 - 24 Jun 2022
Cited by 3 | Viewed by 1708
Abstract
Radon (Rn) is a biological threat to cells due to its radioactivity. It is capable of penetrating the human body and damaging cellular DNA, causing mutations and interfering with cellular dynamics. Human exposure to high concentrations of Rn should, therefore, be minimized. The [...] Read more.
Radon (Rn) is a biological threat to cells due to its radioactivity. It is capable of penetrating the human body and damaging cellular DNA, causing mutations and interfering with cellular dynamics. Human exposure to high concentrations of Rn should, therefore, be minimized. The concentration of radon in a room depends on numerous factors, such as room temperature, humidity level, existence of air currents, natural grounds of the buildings, building structure, etc. It is not always possible to change these factors. In this paper we propose a corrective measure for reducing indoor radon concentrations by introducing clean air into the room through forced ventilation. This cannot be maintained continuously because it generates excessive noise (and costs). Therefore, a system for predicting radon concentrations based on Machine Learning has been developed. Its output activates the fan control system when certain thresholds are reached. Full article
(This article belongs to the Special Issue Analytical and Computational Systems in Biosensing)
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19 pages, 1714 KiB  
Article
Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach
by Kateryna Tkachenko, María Espinosa, Isabel Esteban-Díez, José M. González-Sáiz and Consuelo Pizarro
Chemosensors 2022, 10(6), 229; https://doi.org/10.3390/chemosensors10060229 - 16 Jun 2022
Cited by 1 | Viewed by 1686
Abstract
An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification [...] Read more.
An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes. Full article
(This article belongs to the Special Issue Analytical and Computational Systems in Biosensing)
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13 pages, 2579 KiB  
Article
Application of a Fluorescent Biosensor in Determining the Binding of 5-HT to Calmodulin
by L. X. Vásquez-Bochm, Isabel Velázquez-López, Rachel Mata, Alejandro Sosa-Peinado, Patricia Cano-Sánchez and Martin González-Andrade
Chemosensors 2021, 9(9), 250; https://doi.org/10.3390/chemosensors9090250 - 05 Sep 2021
Cited by 2 | Viewed by 2076
Abstract
Here, we show the utility of the fluorescent biosensor hCaM-M124C-mBBr in detecting and determining the affinity of serotonin (5-HT). We obtained a Kd of 5-HT (0.71 μm) for the first time, the same order of magnitude as most anti-CaM drugs. [...] Read more.
Here, we show the utility of the fluorescent biosensor hCaM-M124C-mBBr in detecting and determining the affinity of serotonin (5-HT). We obtained a Kd of 5-HT (0.71 μm) for the first time, the same order of magnitude as most anti-CaM drugs. This data can contribute to understanding the direct and indirect modulation of CaM on its binding proteins when the 5-HT concentration varies in different tissues or explain some of the side effects of anti-CaM drugs. On the other hand, molecular modeling tools help the rational design of biosensors and adequately complement the experimental results. For example, the docking study indicates that 5-HT binds at the same site as chlorpromazine (site 1) with a theoretical Ki of 2.84 μM; while the molecular dynamics simulations indicate a stability of the CaM–5-HT complex with a theoretical ΔG of −4.85 kcal mol−1, where the enthalpy contribution is greater. Thus, the combination of biotechnology and bioinformatics helps in the design and construction of more robust biosensors. Full article
(This article belongs to the Special Issue Analytical and Computational Systems in Biosensing)
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