Electrochemical point-of-care devices for monitoring waterborne pathogens: Protozoa, bacteria, and viruses – An overview

Silva, Alexsandra D. da; Paschoalino, Waldemir J.; Neto, Romeu Cantúsio; Kubota, Lauro T.

doi:10.1016/j.cscee.2022.100182

Cited by 9 publications

(5 citation statements)

References 68 publications

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“…Microscopes and other imaging devices are widely used for the classification of protozoa in the methods in the literature [13,16,33]. However, there are no real-time and fully automatic protozoa detectors and classifiers.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm

Kahraman,

Karaş,

Turan

2024

Applied Sciences

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Protozoa detection and classification from freshwaters and microscopic imaging are critical components in environmental monitoring, parasitology, science, biological processes, and scientific research. Bacterial and parasitic contamination of water plays an important role in society health. Conventional methods often rely on manual identification, resulting in time-consuming analyses and limited scalability. In this study, we propose a real-time protozoa detection framework using the YOLOv4 algorithm, a state-of-the-art deep learning model known for its exceptional speed and accuracy. Our dataset consists of objects of the protozoa species, such as Bdelloid Rotifera, Stylonychia Pustulata, Paramecium, Hypotrich Ciliate, Colpoda, Lepocinclis Acus, and Clathrulina Elegans, which are in freshwaters and have different shapes, sizes, and movements. One of the major properties of our work is to create a dataset by forming different cultures from various water sources like rainwater and puddles. Our network architecture is carefully tailored to optimize the detection of protozoa, ensuring precise localization and classification of individual organisms. To validate our approach, extensive experiments are conducted using real-world microscopic image datasets. The results demonstrate that the YOLOv4-based model achieves outstanding detection accuracy and significantly outperforms traditional methods in terms of speed and precision. The real-time capabilities of our framework enable rapid analysis of large-scale datasets, making it highly suitable for dynamic environments and time-sensitive applications. Furthermore, we introduce a user-friendly interface that allows researchers and environmental professionals to effortlessly deploy our YOLOv4-based protozoa detection tool. We conducted f1-score 0.95, precision 0.92, sensitivity 0.98, and mAP 0.9752 as evaluating metrics. The proposed model achieved 97% accuracy. After reaching high efficiency, a desktop application was developed to provide testing of the model. The proposed framework’s speed and accuracy have significant implications for various fields, ranging from a support tool for paramesiology/parasitology studies to water quality assessments, offering a powerful tool to enhance our understanding and preservation of ecosystems.

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Section: Introductionmentioning

confidence: 99%

Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm

Kahraman,

Karaş,

Turan

2024

Applied Sciences

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“…There has been an increasing demand for the inexpensive, rapid, and portable monitoring of pathogenic and indicator bacteria in medical and environmental samples [1,2]. The conventional methods of detecting bacteria include immunological assays, molecular tests, and cell culturing, which are labor-intensive, time-consuming, and expensive while requiring trained personnel and complex equipment [3].…”

Section: Introductionmentioning

confidence: 99%

“…PoC devices use various transduction techniques such as electrochemiluminescence [6,7], surface plasmon resonance (SPR) [8], fluorescence [9], colorimetry [10], mass spectrometry using quartz crystal microbalance (QCM) [11], and electrochemistry [12]. Electrochemical Biosensors 2023, 13, 943 2 of 13 detection has multiple advantages in terms of high sensitivity and specificity, rapid measurement, and being inexpensive and suitable for the miniaturization for on-site testing [2,13]. Numerous electrochemical bacteria sensors with various bio-recognition elements such as antibodies, aptamers, and bacteriophages have been developed to date [14,15].…”

Section: Introductionmentioning

confidence: 99%

Microfluidic Sensor Based on Cell-Imprinted Polymer-Coated Microwires for Conductometric Detection of Bacteria in Water

Akhtarian,

Doostmohammadi,

Archonta

et al. 2023

Biosensors

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The rapid, inexpensive, and on-site detection of bacterial contaminants using highly sensitive and specific microfluidic sensors is attracting substantial attention in water quality monitoring applications. Cell-imprinted polymers (CIPs) have emerged as robust, cost-effective, and versatile recognition materials with selective binding sites for capturing whole bacteria. However, electrochemical transduction of the binding event to a measurable signal within a microfluidic device to develop easy-to-use, compact, portable, durable, and affordable sensors remains a challenge. For this paper, we employed CIP-functionalized microwires (CIP-MWs) with an affinity towards E. coli and integrated them into a low-cost microfluidic sensor to measure the conductometric transduction of CIP–bacteria binding events. The sensor comprised two CIP-MWs suspended perpendicularly to a PDMS microchannel. The inter-wire electrical resistance of the microchannel was measured before, during, and after exposure of CIP-MWs to bacteria. A decline in the inter-wire resistance of the sensor after 30 min of incubation with bacteria was detected. Resistance change normalization and the subsequent analysis of the sensor's dose-response curve between 0 to 109 CFU/mL bacteria revealed the limits of detection and quantification of 2.1 × 105 CFU/mL and 7.3 × 105 CFU/mL, respectively. The dynamic range of the sensor was 104 to 107 CFU/mL where the bacteria counts were statistically distinguishable from each other. A linear fit in this range resulted in a sensitivity of 7.35 μS per CFU/mL. Experiments using competing Sarcina or Listeria cells showed specificity of the sensor towards the imprinted E. coli cells. The reported CIP-MW-based conductometric microfluidic sensor can provide a cost-effective, durable, portable, and real-time solution for the detection of pathogens in water.

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“…Challenges and prospects were provided to develop sample-to-result POC tools for the detection of pathogens. Silva et al 39 comprehensively elucidated electrochemical POC techniques for the detection of waterborne pathogens. Nordin et al 40 developed a portable and simple electrochemical biosensor to monitor the Vibrio parahaemolyticus pathogen in real samples.…”

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confidence: 99%

Review—Emerging Trends in the Development of Electrochemical Devices for the On-Site Detection of Food Contaminants

et al. 2022

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Traditional laboratory-based sensing strategies for food contaminant detection are often limited because they are time consuming and expensive and require trained personnel, which makes them unsuitable for routine sensing. Therefore, the scientific and industrial community is showing enormous interest in the design and development of portable sensing devices for the on-site and point-of-care detection of food contaminants. Portability is one of the chief characteristic features of designing contemporary analytical devices. Portable devices have received tremendous attention, as these novel devices have advanced the field of sensing. Various sensing strategies have been utilized for on-site detection of food contaminants. Among these, portable electrochemical devices have emerged vigorously in the past few years. Scientists and industrialists have worked tirelessly to develop portable electrochemical devices for a minute amount of food contaminant detection in water bodies and food products. The current work aims to demonstrate recent research progress related to the design, development, and improvement of portable electrochemical devices for detection of food contaminants.

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Electrochemical point-of-care devices for monitoring waterborne pathogens: Protozoa, bacteria, and viruses – An overview

Cited by 9 publications

References 68 publications

Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm

Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm

Microfluidic Sensor Based on Cell-Imprinted Polymer-Coated Microwires for Conductometric Detection of Bacteria in Water

Review—Emerging Trends in the Development of Electrochemical Devices for the On-Site Detection of Food Contaminants

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