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Open AccessEditorial

Preface: The 4th International Electronic Conference on Biosensors

by Giovanna Marrazza and Sara Tombelli

Eng. Proc. 2024, 73(1), 11; https://doi.org/10.3390/engproc2024073011 - 21 Nov 2024

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Abstract

With the success of the past 3 editions, the 4th International Electronic Conference on Biosensors (IECC 2024) was held on 20–22 May 2021 with an online mode [...] Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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9 pages, 2889 KB

Open AccessProceeding Paper

An Electrochemical Sensing Platform Based on a Carbon Paste Electrode Modified with a Graphene Oxide/TiO₂ Nanocomposite for Atenolol Determination

by Ergi Hoxha, Nevila Broli, Majlinda Vasjari and Sadik Cenolli

Eng. Proc. 2024, 73(1), 1; https://doi.org/10.3390/engproc2024073001 - 19 Aug 2024

Cited by 2 | Viewed by 1538

Abstract

Atenolol is a medication belonging to the class of drugs known as beta-blockers, used to treat high blood pressure (hypertension) and irregular heartbeats (arrhythmia). Their presence in the environment has serious impacts on humans, animals, and the water ecosystem. In this context, the [...] Read more.

Atenolol is a medication belonging to the class of drugs known as beta-blockers, used to treat high blood pressure (hypertension) and irregular heartbeats (arrhythmia). Their presence in the environment has serious impacts on humans, animals, and the water ecosystem. In this context, the aim of this study was to develop a simple voltammetric method for the determination of atenolol (ATN) using carbon paste electrodes modified with the nanomaterials TiO₂ and rGO/TiO₂. The analytical performance of the modified sensor was evaluated using square wave voltammetry and cyclic voltammetry in 0.1 mol L⁻¹ acid sulfuric solution (H₂SO₄), pH 2. The nanocomposite electrode CPE/rGO/TiO₂ exhibited excellent electrocatalytic activity towards ATN oxidations at 0.1 mol L⁻¹ H₂SO₄ compared with unmodified carbon paste electrodes CPEs and those modified with titanium oxide, CPE/TiO₂. Different experimental and conditional parameters were optimized, such as supporting electrolytes, pH, amplitude, frequency, etc. Under optimal conditions, linear calibration curves were obtained, ranging from 1.7 to 23.2 µmol L⁻¹ for ATN with detection limits of 0.05 μmol L⁻¹. The modified nanocomposite CPE/rGO/TiO₂ sensor showed good sensitivity and good repeatability (RSD ≤ 0.61%) for ATN determination. The proposed sensor is mechanically robust and presented reproducible results and a long useful life. In order to verify the usefulness of the developed methods, the nanocomposite sensor CPE/rGO/TiO₂ was applied for the detection of atenolol in real samples (pharmaceutical tablets without any pre-treatment). The excipients present in the tablets did not interfere in the assay. Recoveries ranging from 97.7% to 106% were obtained. The results showed that the CPE/rGO/TiO₂ voltammetric sensor could be successfully applied in the routine quality control of ATN in complex matrices. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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9 pages, 2729 KB

Open AccessProceeding Paper

Development of a Flexible Piezoelectric Biosensor That Integrates BaTiO₃–Poly(Dimethylsiloxane) for Posture Correction Applications

by Menduh Furkan Aslan, Cem Özbek, Gökhan Yiğit, Mehmet Tosun and Seda Demirel Topel

Eng. Proc. 2024, 73(1), 2; https://doi.org/10.3390/engproc2024073002 - 20 Sep 2024

Cited by 2 | Viewed by 2529

Abstract

The prolonged issue of poor posture due to desk work has led to innovative technological remedies. This study shows the development of a flexible piezoelectric biosensor integrating BaTiO₃ nanoparticles within a Polydimethylsiloxane (PDMS) matrix for practical posture correction. The biosensor is capable [...] Read more.

The prolonged issue of poor posture due to desk work has led to innovative technological remedies. This study shows the development of a flexible piezoelectric biosensor integrating BaTiO₃ nanoparticles within a Polydimethylsiloxane (PDMS) matrix for practical posture correction. The biosensor is capable of real-time posture monitoring and correction by leveraging the piezoelectric properties of BaTiO₃. Comprehensive synthesis and characterization using X-ray diffraction analysis (XRD) and transmission electron microscopy (TEM) validated the ideal particle size and crystalline structure of the composite. COMSOL Multiphysics simulations showed a peak potential of 0.87 volts under mechanical stress, which further confirmed the sensor’s efficiency. Electrical testing revealed that the sensor with 35 wt.% BaTiO₃ exhibited a higher output voltage of 0.87 V compared to 0.34 V for the sensor with 30 wt.% BaTiO₃, emphasizing its exceptional potential for addressing posture-related issues. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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5 pages, 928 KB

Open AccessProceeding Paper

ZnO Functional Nanomaterial in Green Microalgae Growth Advancement

by Praskoviya Boltovets, Sergii Kravchenko and Viktoriya Petlovana

Eng. Proc. 2024, 73(1), 3; https://doi.org/10.3390/engproc2024073003 - 8 Oct 2024

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Abstract

Nanomaterials are substances with unique properties due to the irintrinsic confinement effect and high surface area that have allowed their use in biology and medicine for sensor application. The key feature of nanomaterials in such applications is their ability to providesensitivity enhancement for [...] Read more.

Nanomaterials are substances with unique properties due to the irintrinsic confinement effect and high surface area that have allowed their use in biology and medicine for sensor application. The key feature of nanomaterials in such applications is their ability to providesensitivity enhancement for sensors. On the other hand, nanomaterials possess the ability to change the biological function in cells or tissues; therefore, it is from this point of view that nanomaterials can be considered as functional. As far as biosensor application is concerned, it is important to optimize the determination of target molecules in spatial and temporal modes. The purpose of the presented work was to study the effect of functional nanomaterials on the growth (the temporal component) and morphology (the spatial component) of cell culture. The aim was to provide a culture condition where an increase in both the spatial and temporal components of configuration could be achieved in order to optimize sensor needs. Since microalgae have a wide range of possibilities for practical use in medicine, pharmacology and various industries, the study of the effect of nanomaterials on their growth and development is very important. It was found that ZnO nanomaterial, which was obtained by volumetric electrospark dispersion, revealed the concentration-dependent effect on both the grown rate and the color intensity interior of Chlamydomonas monadina microalgae culture. Therefore, ZnO functional nanomaterial achieved the optimization of target molecule formation for biosensor application. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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5 pages, 554 KB

Open AccessProceeding Paper

Detection of Alzheimer’s and Parkinson’s Diseases Using Deep Learning-Based Various Transformers Models

by Mesut Güven

Eng. Proc. 2024, 73(1), 4; https://doi.org/10.3390/engproc2024073004 - 11 Oct 2024

Cited by 2 | Viewed by 3034

Abstract

Alzheimer’s disease is a neurodegenerative condition primarily attributed to environmental factors, abnormal protein deposits, immune system dysregulation, and the consequential death of nerve cells in the brain. On the other hand, Parkinson’s disease manifests as a neurological disorder featuring primary motor, secondary motor, [...] Read more.

Alzheimer’s disease is a neurodegenerative condition primarily attributed to environmental factors, abnormal protein deposits, immune system dysregulation, and the consequential death of nerve cells in the brain. On the other hand, Parkinson’s disease manifests as a neurological disorder featuring primary motor, secondary motor, and non-motor symptoms, accompanied by the rapid demise of cells in the brain’s dopamine-producing region. Utilizing brain images for accurate diagnosis and treatment is integral to addressing both conditions. This study harnessed the power of artificial intelligence for classification processes, employing state-of-the-art transformer models such as Swin transformer, vision transformer (ViT), and bidirectional encoder representation from image transformers (BEiT). The investigation utilized an open-source dataset comprising 450 images, evenly distributed among healthy, Alzheimer’s, and Parkinson’s classes. The dataset was meticulously divided, with 80% allocated to the training set (390 images) and 20% to the validation set (90 images). Impressively, the classification accuracy surpassed 80%, showcasing the efficacy of transformer-based models in disease detection. Looking ahead, this study recommends delving into hybrid and ensemble models and leveraging the strengths of multiple transformer-based deep learning architectures. Beyond contributing crucial insights at the intersection of artificial intelligence and neurology, this research emphasizes the transformative potential of advanced models for enhancing diagnostic precision and treatment strategies in Alzheimer’s and Parkinson’s diseases. It signifies a significant step towards integrating cutting-edge technology into mainstream medical practices for improved patient outcomes. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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11 pages, 702 KB

Open AccessProceeding Paper

AI-Driven Improvements in Electrochemical Biosensors for Effective Pathogen Detection at Point-of-Care

by Inderpreet Singh, Asmita Gupta, Chansi Gupta, Ashish Mani and Tinku Basu

Eng. Proc. 2024, 73(1), 5; https://doi.org/10.3390/engproc2024073005 - 14 Oct 2024

Cited by 8 | Viewed by 2720

Abstract

The rapid and accurate detection of pathogens is vital for effective disease management and control. This paper introduces a novel framework for integrating artificial intelligence (AI) into electrochemical biosensors for pathogen detection. Real-world samples often present unwanted noise in the signal, particularly when [...] Read more.

The rapid and accurate detection of pathogens is vital for effective disease management and control. This paper introduces a novel framework for integrating artificial intelligence (AI) into electrochemical biosensors for pathogen detection. Real-world samples often present unwanted noise in the signal, particularly when utilizing portable point-of-care devices. To overcome this challenge, a framework using AI for noise reduction from a portable potentiostat is proposed in this work. This approach involves employing a denoising autoencoder (DAE) to effectively remove noise from the electrochemical signals generated from a portable potentiostat by utilizing training datasets generated from benchtop potentiostat for training the DAE, bringing the performance of portable devices on par with their benchtop counterparts. This enhancement is crucial for point-of-care applications where environmental and operational factors often compromise data quality. Smartphones are often used as interfaces for portable electrochemical devices, the proposed framework can leverage the computational capabilities of smartphones to run the DAE model for processing electrochemical signals in real-time, thus making it compatible with fully point-of-care solution. The proposed system has been validated using COVID-19 and dengue DPV data, demonstrating its potential as a powerful tool in the rapid and accurate detection of SARS-CoV-2 and other pathogens. The integration of AI into electrochemical biosensing offers a more reliable and accessible option for healthcare professionals and researchers. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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8 pages, 1124 KB

Open AccessProceeding Paper

A Fog Computing-Based Cost-Effective Smart Health Monitoring Device for Infectious Disease Applications

by Saranya Govindakumar, Vijayalakshmi Sankaran, Paramasivam Alagumariappan, Bhaskar Kosuru Bojji Raju and Daniel Ford

Eng. Proc. 2024, 73(1), 6; https://doi.org/10.3390/engproc2024073006 - 17 Oct 2024

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Abstract

The COVID-19 epidemic has raised awareness of exactly how crucial it is to continuously observe issues and diagnose respiratory problems early. Although the respiratory system is the primary objective of the disease’s acute phase, subsequent complications of SARS-CoV-2 infection might trigger enduring respiratory [...] Read more.

The COVID-19 epidemic has raised awareness of exactly how crucial it is to continuously observe issues and diagnose respiratory problems early. Although the respiratory system is the primary objective of the disease’s acute phase, subsequent complications of SARS-CoV-2 infection might trigger enduring respiratory problems and symptoms, according to new research. These signs and symptoms, which collectively inflict considerable strain on healthcare systems and people’s quality of life, comprise, but are not restricted to, congestion, shortage of breath, tightness in the chest, and a decrease in lung function. Wearable technology offers a promising remedy to this persistent issue by offering continuous respiratory parameter monitoring, facilitating the early control and intervention of post-COVID-19 issues with respiration. In an effort to enhance patient outcomes and reduce expenses related to healthcare, this paper examines the possibility of using wearable technology to provide remote surveillance and the early diagnosis of respiratory problems in individuals suffering from COVID-19. In this work, a fog computing-based cost-effective smart health monitoring device is proposed for infectious disease applications. Further, the proposed device consists of three different biosensor modules, namely a MAX90614 infrared temperature sensor, a MAX30100 pulse oximeter, and a microphone sensor. All these sensor modules are connected to a fog computing device, namely a Raspberry PI microcontroller. Also, three different sensor modules were integrated with the Raspberry PI microcontroller and individuals’ physiological parameters, such as oxygen saturation (SPO2), heartbeat rate, and cough sounds, were recorded by the computing device. Additionally, a convolutional neural network (CNN)-based deep learning algorithm was coded inside the Raspberry PI and was trained with normal and COVID-19 cough sounds from the KAGGLE database. This work appears to be of high clinical significance since the developed fog computing-based smart health monitoring device is capable of identifying the presence of infectious disease through individual physiological parameters. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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6 pages, 4681 KB

Open AccessProceeding Paper

Field-Effect Transistor-Based Biosensor for pH Sensing and IgG Detection

by Beatriz Sequeira-Antunes, Ana S. Viana, Ana Francisca Martins, Nuno Marujo, Susana Cardoso and Hugo Alexandre Ferreira

Eng. Proc. 2024, 73(1), 7; https://doi.org/10.3390/engproc2024073007 - 29 Oct 2024

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Abstract

Measuring urine pH and metabolite concentrations is crucial for detecting potential health issues like urinary tract infection, kidney failure, and metabolic disorders. To address this issue, we are working on the development of a Bio-FET system, a biosensor based on field-effect transistors. This [...] Read more.

Measuring urine pH and metabolite concentrations is crucial for detecting potential health issues like urinary tract infection, kidney failure, and metabolic disorders. To address this issue, we are working on the development of a Bio-FET system, a biosensor based on field-effect transistors. This system utilizes a microfabricated gold electrode to detect not only urine pH but also specific biomarkers. As a pH sensor, the developed system shows a good response, with a corresponding sensitivity of 2.20 µA/pH. For biomarker detection, the system successfully detected immunoglobulin G, with antigen–antibody binding causing a measurable change of approximately 4 µA. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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8 pages, 1907 KB

Open AccessProceeding Paper

An In-Depth Analysis of Peritoneal Dialysate Effluent Composition with a Deep-UV-LED-Based Affordable Optical Chromatographic Sensor

by Nikolay Ovsyannikov, Georgii Konoplev, Artur Kuznetsov, Alar Sünter, Vadim Korsakov, Oksana Stepanova, Milana Mikhailis, Roman Gerasimchuk, Alina Isachkina, Zarina Rustamova and Aleksandr Frorip

Eng. Proc. 2024, 73(1), 8; https://doi.org/10.3390/engproc2024073008 - 7 Nov 2024

Cited by 1 | Viewed by 1592

Abstract

It was shown earlier that the use of fast protein and metabolites liquid chromatography (FPMLC) and low-cost deep UV–LED-based optical chromatographic sensors with PD-10 desalting columns as a separation element can facilitate the monitoring of patients on chronic peritoneal dialysis (PD). Previously, we [...] Read more.

It was shown earlier that the use of fast protein and metabolites liquid chromatography (FPMLC) and low-cost deep UV–LED-based optical chromatographic sensors with PD-10 desalting columns as a separation element can facilitate the monitoring of patients on chronic peritoneal dialysis (PD). Previously, we established that the first peak in the FPMLC chromatograms of effluent dialysate is mainly responsible for proteins and could be used for the assessment of peritoneal protein loss in patients on PD, while the origin and clinical significance of the other two peaks still remain unclear. Optical absorption and fluorescence spectroscopy in the UV and visible regions of 240…720 nm were used for the analysis of PD effluent chromatographic fractions collected from a drainpipe of the sensor with photometric detection at 280 nm; chromatograms of twenty dialysate samples were processed. The absorption and fluorescence spectra of the first fraction demonstrated peaks at 270 nm and 330 nm, respectively, which is typical for proteins. The absorption spectra of the third fraction revealed the characteristic maxima of creatinine and uric acid, while the fluorescence spectra showed the characteristic peak of indoxyl sulfate 375 nm at 270 nm excitation. The second fraction had a single, extremely wide absorption band, strong fluorescence was observed at 440–450 nm while excited at 370 nm. Such spectral characteristics are typical for advanced glycation end products (AGE). Thus, it was demonstrated that deep UV–LED-based affordable chromatographic sensors could provide significantly more information about the composition of PD effluent dialysate than just the total protein concentration, including the contents of clinically significant metabolites, e.g., indoxyl sulfate and AGE. Moreover, the introduction of optical fluorescence detection could significantly improve the capabilities of such devices. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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10 pages, 2002 KB

Open AccessProceeding Paper

Silver Nanostructures for Determination of FKBP12 Protein

by Cosimo Bartolini, Martina Tozzetti, Stefano Menichetti and Gabriella Caminati

Eng. Proc. 2024, 73(1), 9; https://doi.org/10.3390/engproc2024073009 - 12 Nov 2024

Cited by 3 | Viewed by 1105

Abstract

FKBP12 is a peptidyl––prolyl cis–trans isomerase that was recently proposed as a candidate biomarker for cancer, for neurodegenerations and for anti–rejection therapy after organ transplant. We designed and fabricated a nanosensor platform for the rapid and efficient determination of FKBP12 concentration in biological [...] Read more.

FKBP12 is a peptidyl––prolyl cis–trans isomerase that was recently proposed as a candidate biomarker for cancer, for neurodegenerations and for anti–rejection therapy after organ transplant. We designed and fabricated a nanosensor platform for the rapid and efficient determination of FKBP12 concentration in biological fluids exploiting anisotropic silver nanoparticles (AgNps) to enhance the capabilities of Quartz Crystal Microbalance (QCM) detection. To this end, the QCM sensor was coated with a compact array of AgNPs that were further functionalized with a Self–Assembled–Monolayer containing a synthetic receptor, GPS–SH1, designed and synthesized specifically to selectively bind FKBP12. Silver nanoflowers, AgNFs, and silver dendrites, AgNDs, were prepared by electrodeposition and characterized by means of UV–Vis spectroscopy, Scanning Electron Microscopy (SEM), QCM and water contact angle (CA). The AgNPs@Au/GPS–SH1–functionalized QCM sensors were used to detect increasing concentrations of FKBP12 in solution; the results showed that the use of AgNDs significantly enhanced the sensitivity of the sensor with respect to flat Ag sensor chips, allowing the detection of FKBP12 at sub–picomolar concentrations. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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7 pages, 426 KB

Open AccessProceeding Paper

Integration of Multiple Biosensors for Emotion Classification with Artificial Intelligence

by Cintia Ricaele Ferreira da Silva, Marcus Vinicius Costa Alves, Maria José Nunes Gadelha and Edgard Morya

Eng. Proc. 2024, 73(1), 10; https://doi.org/10.3390/engproc2024073010 - 21 Nov 2024

Cited by 1 | Viewed by 1218

Abstract

The objective of this study is to integrate and classify electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) signals from a participant exposed to emotional stimuli—happiness, anger, fear, and sadness. We used the LazyPredict library to identify the most effective classification model, [...] Read more.

The objective of this study is to integrate and classify electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) signals from a participant exposed to emotional stimuli—happiness, anger, fear, and sadness. We used the LazyPredict library to identify the most effective classification model, leveraging its simplified implementation and wide range of models and performance metrics. The signals were processed in Python following a detailed workflow: (1) normalization, (2) band-pass filtering, (3) epoch extraction and selection, and (4) relative energy extraction using Discrete Wavelet Transform (DWT). After preprocessing, the data were input into LazyPredict, where the Extra Trees model consistently demonstrated the best performance for binary emotion classification. Our experience with LazyPredict proved to be practical and efficient, facilitating the exploration of high-performing models for emotion classification. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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