A Review on Potential Electrochemical Point-of-Care Tests Targeting Pandemic Infectious Disease Detection: COVID-19 as a Reference

Biswas, Gokul Chandra; Choudhury, Swapnila; Rabbani, Mohammad Mahbub; Das, Jagotamoy

doi:10.3390/chemosensors10070269

Cited by 29 publications

(23 citation statements)

References 173 publications

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“…In deep learning algorithms, a CNN is a kind of network architecture that is used specifically for tasks such as image recognition [ 30 ]. The CNN is extensively used in disease diagnosis [ 31 ], speech recognition [ 32 ], object classification [ 33 ], and fault detection [ 34 ]. A CNN needs to simplify the images without removing important details in order to provide reliable results [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

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Due to the tremendous expectations placed on batteries to produce a reliable and secure product, fault detection has become a critical part of the manufacturing process. Manually, it takes much labor and effort to test each battery individually for manufacturing faults including burning, welding that is too high, missing welds, shifting, welding holes, and so forth. Additionally, manual battery fault detection takes too much time and is extremely expensive. We solved this issue by using image processing and machine learning techniques to automatically detect faults in the battery manufacturing process. Our approach will reduce the need for human intervention, save time, and be easy to implement. A CMOS camera was used to collect a large number of images belonging to eight common battery manufacturing faults. The welding area of the batteries’ positive and negative terminals was captured from different distances, between 40 and 50 cm. Before deploying the learning models, first, we used the CNN for feature extraction from the image data. To over-sample the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE) since the dataset was highly imbalanced, resulting in over-fitting of the learning model. Several machine learning and deep learning models were deployed on the CNN-extracted features and over-sampled data. Random forest achieved a significant 84% accuracy with our proposed approach. Additionally, we applied K-fold cross-validation with the proposed approach to validate the significance of the approach, and the logistic regression achieved an 81.897% mean accuracy score and a +/− 0.0255 standard deviation.

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Section: Methodsmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

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“…MNPs with proper surface functionalization are especially suitable for the development of lab-in-chip biosensors for easy, reliable, and cost-effective POC diagnostic facilities. In addition, MNPs are widely used in the molecular diagnosis of human viruses [ 128 ] and other infectious pathogens [ 129 ]; MNP-based biosensors are also an extremely promising form in highly accurate POC SARS-CoV-2 detection [ 130 , 131 , 132 ]. In this review, we briefly highlight the following biological objects, which have been recently analyzed using MNP-based POC devices: SARS-CoV-2, Human chorionic gonadotropin (hCG), the typical oral pathogen of Streptococcus, foodborne bacteria in food and environmental samples ( Listeria monocytogenes , Campylobacter jejuni , and Staphylococcus aureus ), pathogenic bacteria ( Escherichia coli ( E. coli ), and Staphylococcus aureus ( S. aureus ), the food pathogen Vibrio parahaemolyticus ), the bacteria Acinetobacter baumannii , and influenza A H1N1 virus ( Table 1 ).…”

Section: Diagnostic Application Of Mnpsmentioning

confidence: 99%

Magnetite-Based Biosensors and Molecular Logic Gates: From Magnetite Synthesis to Application

et al. 2023

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In the last few decades, point-of-care (POC) sensors have become increasingly important in the detection of various targets for the early diagnostics and treatment of diseases. Diverse nanomaterials are used as building blocks for the development of smart biosensors and magnetite nanoparticles (MNPs) are among them. The intrinsic properties of MNPs, such as their large surface area, chemical stability, ease of functionalization, high saturation magnetization, and more, mean they have great potential for use in biosensors. Moreover, the unique characteristics of MNPs, such as their response to external magnetic fields, allow them to be easily manipulated (concentrated and redispersed) in fluidic media. As they are functionalized with biomolecules, MNPs bear high sensitivity and selectivity towards the detection of target biomolecules, which means they are advantageous in biosensor development and lead to a more sensitive, rapid, and accurate identification and quantification of target analytes. Due to the abovementioned properties of functionalized MNPs and their unique magnetic characteristics, they could be employed in the creation of new POC devices, molecular logic gates, and new biomolecular-based biocomputing interfaces, which would build on new ideas and principles. The current review outlines the synthesis, surface coverage, and functionalization of MNPs, as well as recent advancements in magnetite-based biosensors for POC diagnostics and some perspectives in molecular logic, and it also contains some of our own results regarding the topic, which include synthetic MNPs, their application for sample preparation, and the design of fluorescent-based molecular logic gates.

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“…Thus, it is necessary to regard each group of people as reachable nodes. The flow between groups can be described by Markov mobility [ 13 ]. In Figure 1 , nodes define the different populations in the spread of the disease, and arrows indicate the conversion probability between populations.…”

Section: Epidemiological Model Proposalmentioning

confidence: 99%

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

Liu

Yang

Fong

et al. 2022

IJERPH

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The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the “Delta” virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

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A Review on Potential Electrochemical Point-of-Care Tests Targeting Pandemic Infectious Disease Detection: COVID-19 as a Reference

Cited by 29 publications

References 173 publications

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Magnetite-Based Biosensors and Molecular Logic Gates: From Magnetite Synthesis to Application

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

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