scholarly journals Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Masateru Taniguchi ◽  
Shohei Minami ◽  
Chikako Ono ◽  
Rina Hamajima ◽  
Ayumi Morimura ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Speed ◽  
Simple Procedure ◽  
Rna Extraction ◽  
Disease Outbreaks ◽  
High Sensitivity ◽  
Cost Effective ◽  
Rt Pcr ◽  
Emerging Viruses

AbstractHigh-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

scholarly journals An artificial intelligence nanopore platform for SARS-CoV-2 virus detection

2020 ◽  
Author(s):  
Masateru Taniguchi ◽  
Shohei Minami ◽  
Chikako Ono ◽  
Rina Hamajima ◽  
Ayumi Morimura ◽  
...  
Keyword(s):  
High Throughput ◽  
High Speed ◽  
Simple Procedure ◽  
Rna Extraction ◽  
High Sensitivity ◽  
Viral Rna ◽  
Rt Pcr ◽  
Emerging Viruses ◽  
Accurate Identification ◽  
Artificial Intelligent

Abstract High-throughput, high-accuracy detection of emerging viruses allows for pandemic prevention and control. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is used to diagnose the presence of SARS-CoV-2. The principle of the test is to detect RNA in the virus using a pair of primers that specifically binds to the base sequence of the viral RNA. However, RT-PCR is a sophisticated technique requiring a time-consuming pretreatment procedure for extracting viral RNA from clinical specimens and to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity using artificial intelligent nanopores utilizing a simple procedure that does not require RNA extraction. Artificial intelligent nanopore platform consists of machine learning software on the servers, portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. Here we show that the artificial intelligent nanopores are successful in accurate identification of four types of coronaviruses, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2, which are usually extremely difficult to detect. The positive/negative diagnostics of the new coronavirus is achieved with a sensitivity of 95 % and specificity of 92 % with a 5-minute diagnosis. The platform enables high throughput diagnostics with low false negatives for the novel coronavirus.

scholarly journals Evaluation of RNA extraction free method for detection of SARS-COV-2 in salivary samples for mass screening for COVID-19

2021 ◽  
Author(s):  
Sally A. Mahmoud ◽  
Esra Ibrahim ◽  
Subhashini Ganesan ◽  
Bhagyashree Thakre ◽  
Juliet G Teddy ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Gold Standard ◽  
Rna Extraction ◽  
High Sensitivity ◽  
High Specificity ◽  
Cost Effective ◽  
Kappa Coefficient ◽  
Nasopharyngeal Swab ◽  
Rt Pcr ◽  
Good Percentage

AbstractIn this current COVID - 19 pandemic, there is a dire need for cost effective and less time-consuming alternatives for SARS-COV-2 testing. The RNA extraction free method for detecting SARS-COV-2 in saliva is a promising option, this study found that it has high sensitivity (85.34%), specificity (95.04%) and was comparable to the gold standard nasopharyngeal swab. The method showed good percentage of agreement (kappa coefficient) 0.797 between salivary and NPS samples. However, there are variations in the sensitivity and specificity based on the RT-PCR kit used. The Thermo Fischer-Applied biosystems showed high sensitivity, PPV and NPV but also showed higher percentage of invalid reports. Whereas the BGI kit showed high specificity, better agreement (kappa coefficient) between the results of saliva and NPS samples and higher correlation between the Ct values of saliva and NPS samples. Thus, the RNA extraction free method for salivary sample serves as an effective alternative for SARS-CoV 2-testing.

scholarly journals Evaluation of RNA Extraction-Free Method for Detection of SARS-CoV-2 in Salivary Samples for Mass Screening for COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Sally A. Mahmoud ◽  
Subhashini Ganesan ◽  
Esra Ibrahim ◽  
Bhagyashree Thakre ◽  
Juliet G. Teddy ◽  
...  
Keyword(s):  
Predictive Value ◽  
Rna Extraction ◽  
Screening Method ◽  
High Sensitivity ◽  
High Specificity ◽  
Cost Effective ◽  
Kappa Coefficient ◽  
Nasopharyngeal Swab ◽  
Rt Pcr ◽  
Good Agreement

In this COVID-19 pandemic, there is a dire need for cost-effective and less time-consuming alternatives for SARS-CoV-2 testing. The RNA extraction-free method for detecting SARS-CoV-2 in saliva is a promising option. This study found that it has high sensitivity (85.34%), specificity (95.04%), and was comparable to the gold standard nasopharyngeal swab (NPS) sample tests. The method showed good agreement between salivary and NPS samples, with a kappa coefficient of 0.797. However, there are variations in the sensitivity and specificity based on the RT-PCR kit used. The Thermo Fisher Applied Biosystems showed high sensitivity, positive predictive value (PPV), and negative predictive value (NPV) but also showed a higher percentage of invalid reports. On the other hand, the BGI kit showed high specificity, better agreement (kappa coefficient) between the results of saliva and NPS samples, and higher correlation between the Ct values of saliva and NPS samples. Thus, the RNA extraction-free method for salivary sample serves as an effective alternative screening method for COVID-19.

scholarly journals Multiplexed detection of SARS-CoV-2 and other respiratory infections in high throughput by SARSeq

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ramesh Yelagandula ◽  
◽  
Aleksandr Bykov ◽  
Alexander Vogt ◽  
Robert Heinen ◽  
...  
Keyword(s):  
Influenza A ◽  
Respiratory Infections ◽  
High Sensitivity ◽  
Cost Effective ◽  
Massively Parallel ◽  
Rt Pcr ◽  
Viral Spread ◽  
Saliva Analysis ◽  
Double Blinded ◽  
Generation Sequencing

AbstractThe COVID-19 pandemic has demonstrated the need for massively-parallel, cost-effective tests monitoring viral spread. Here we present SARSeq, saliva analysis by RNA sequencing, a method to detect SARS-CoV-2 and other respiratory viruses on tens of thousands of samples in parallel. SARSeq relies on next generation sequencing of multiple amplicons generated in a multiplexed RT-PCR reaction. Two-dimensional, unique dual indexing, using four indices per sample, enables unambiguous and scalable assignment of reads to individual samples. We calibrate SARSeq on SARS-CoV-2 synthetic RNA, virions, and hundreds of human samples of various types. Robustness and sensitivity were virtually identical to quantitative RT-PCR. Double-blinded benchmarking to gold standard quantitative-RT-PCR performed by human diagnostics laboratories confirms this high sensitivity. SARSeq can be used to detect Influenza A and B viruses and human rhinovirus in parallel, and can be expanded for detection of other pathogens. Thus, SARSeq is ideally suited for differential diagnostic of infections during a pandemic.

scholarly journals Are Artificial Intelligence (AI) And Machine Learning (ML) Having An Effective Role In Helping Humanity Address The New Coronavirus Pandemic?

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Outbreaks ◽  
Hospital Capacity ◽  
Data Engineering ◽  
Clinical Presentations ◽  
The Face ◽  
Effective Role ◽  
Using Data ◽  
The Right

COVID-19 has become a pandemic affecting the most of countries in the world. One of the most difficult decisions doctors face during the Covid-19 epidemic is determining which patients will stay in hospital, and which are safe to recover at home. In the face of overcrowded hospital capacity and an entirely new disease with little data-based evidence for diagnosis and treatment, the old rules for determining which patients should be admitted have proven ineffective. But machine learning can help make the right decision early, save lives and lower healthcare costs. So, there is therefore an urgent and imperative need to collect data describing clinical presentations, risks, epidemiology and outcomes. On the other side, artificial intelligence(AI) and machine learning(ML) are considered a strong firewall against outbreaks of diseases and epidemics due to its ability to quickly detect, examine and diagnose these diseases and epidemics.AI is being used as a tool to support the fight against the epidemic that swept the entire world since the beginning of 2020.. This paper presents the potential for using data engineering, ML and AI to confront the Coronavirus, predict the evolution of disease outbreaks, and conduct research in order to develop a vaccine or effective treatment that protects humanity from these deadly diseases.

scholarly journals Direct-RT-qPCR Detection of SARS-CoV-2 without RNA Extraction as Part of a COVID-19 Testing Strategy: From Sample to Result in One Hour

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 605 ◽  
Author(s):  
Eva Kriegova ◽  
Regina Fillerova ◽  
Petr Kvapil
Keyword(s):  
High Throughput Screening ◽  
High Speed ◽  
Limit Of Detection ◽  
Point Of Care ◽  
Rna Extraction ◽  
Rna Isolation ◽  
High Sensitivity ◽  
Ease Of Use ◽  
Diagnostic Tools ◽  
Heat Inactivation

Due to the lack of protective immunity in the general population and the absence of effective antivirals and vaccines, the Coronavirus disease 2019 (COVID-19) pandemic continues in some countries, with local epicentres emerging in others. Due to the great demand for effective COVID-19 testing programmes to control the spread of the disease, we have suggested such a testing programme that includes a rapid RT-qPCR approach without RNA extraction. The Direct-One-Step-RT-qPCR (DIOS-RT-qPCR) assay detects severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in less than one hour while maintaining the high sensitivity and specificity required of diagnostic tools. This optimised protocol allows for the direct use of swab transfer media (14 μL) without the need for RNA extraction, achieving comparable sensitivity to the standard method that requires the time-consuming and costly step of RNA isolation. The limit of detection for DIOS-RT-qPCR was lower than seven copies/reaction, which translates to 550 virus copies/mL of swab. The speed, ease of use and low price of this assay make it suitable for high-throughput screening programmes. The use of fast enzymes allows RT-qPCR to be performed under standard laboratory conditions within one hour, making it a potential point-of-care solution on high-speed cycling instruments. This protocol also implements the heat inactivation of SARS-CoV-2 (75 °C for 10 min), which renders samples non-infectious, enabling testing in BSL-2 facilities. Moreover, we discuss the critical steps involved in developing tests for the rapid detection of COVID-19. Implementing rapid, easy, cost-effective methods can help control the worldwide spread of the COVID-19 infection.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

scholarly journals A faster and less costly alternative for RNA extraction of SARS-CoV-2 using proteinase k treatment followed by thermal shock

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248885
Author(s):  
Adolfo Marcelo Ñique ◽  
Fiorella Coronado-Marquina ◽  
Jairo Andrés Mendez Rico ◽  
María Paquita García Mendoza ◽  
Nancy Rojas-Serrano ◽  
...  
Keyword(s):  
Thermal Shock ◽  
Real Time ◽  
Internal Control ◽  
Rna Extraction ◽  
Cost Effective ◽  
Proteinase K ◽  
Rt Pcr ◽  
Critical Material ◽  
Proteinase K Treatment ◽  
Commercial Kit

One of the biggest challenges during the pandemic has been obtaining and maintaining critical material to conduct the increasing demand for molecular tests. Sometimes, the lack of suppliers and the global shortage of these reagents, a consequence of the high demand, make it difficult to detect and diagnose patients with suspected SARS-CoV-2 infection, negatively impacting the control of virus spread. Many alternatives have enabled the continuous processing of samples and have presented a decrease in time and cost. These measures thus allow broad testing of the population and should be ideal for controlling the disease. In this sense, we compared the SARS-CoV-2 molecular detection effectiveness by Real time RT-PCR using two different protocols for RNA extraction. The experiments were conducted in the National Institute of Health (INS) from Peru. We compared Ct values average (experimental triplicate) results from two different targets, a viral and internal control. All samples were extracted in parallel using a commercial kit and our alternative protocol–samples submitted to proteinase K treatment (3 μg/μL, 56°C for 10 minutes) followed by thermal shock (98°C for 5 minutes followed by 4°C for 2 minutes); the agreement between results was 100% in the samples tested. In addition, we compared the COVID-19 positivity between six epidemiological weeks: the initial two in that the Real time RT-PCR reactions were conducted using RNA extracted by commercial kit, followed by two other using RNA obtained by our kit-free method, and the last two using kit once again; they did not differ significantly. We concluded that our in-house method is an easy, fast, and cost-effective alternative method for extracting RNA and conducing molecular diagnosis of COVID-19.

scholarly journals A machine learning-based system for detecting leishmaniasis in microscopic images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Detection ◽  
Detection System ◽  
Cost Effective ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Parasite Detection ◽  
Image Creation

Abstract Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

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