Covid-19 Ai Structural Model to Monitor the Multiplicative Nature of Covid-19 Infections

In the present COVID-19 situation, it poses a danger to a person's life because of organ infection and other health problems. It is mandatory to research work to find a better COVID-19 infection diagnosis method through scans and contact tracing through the AI method. In this, a novel AI structural model is intended to identify the infection features in the respective regions of human being availability, which makes the infection monitoring easier to identify an infected and non-infected human being from the population identified. The method used for monitoring the multiplicative nature of Coronavirus infections is through contact feature tracing and infection confirmation status and confirms the Coronavirus cases from scans and feature analysis to include real-time contact tracking from the same region and distant regions, providing an efficient method to track the infection spread. The anticipated model is used to forecast coronavirus transmission using feature forecasting data. The performance assessment is compared based on the outcomes of the suggested model and shows an enhanced COVID-19 diagnostic model.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.

Refractive index based optically transparent biosensor device design for early detection of coronavirus.

For the quick detection of the new Coronavirus (COVID-19), a highly sensitive D-shaped gold-coated surface Plasmon resonance (SPR) biosensor is presented. The COVID-19 virus may be quickly and accurately identified using the SPR-based biosensor, which is essential for halting the spread of this excruciating epidemic. The suggested biosensor is used for detection of the IBV i.e. infectious bronchitis viruses contaminated cell that belongs to the family of COVID-19 having a refractive index of - 0.96, - 0.97, - 0.98, - 0.99, - 1 that is observed with the change in EID concentration. Some important optical parameter variations are examined in the investigation process. Multiphysics version 5.3 with the Finite element method is used for the proposed biosensor. The proposed sensor depicts maximum wavelength sensitivity of 40,141.76 nm/RIU. Some other parameters such as confinement loss, crosstalk, and insertion loss are also analyzed for the proposed sensor. The reported minimum insertion loss for the refractive index (RI) - 1 is 2.9 dB. Simple design, good sensitivity, and lower value of losses make the proposed sensor proficient for the detection of infectious bronchitis viruses belonging to COVID-19.

Numerical Analysis of Coronavirus Detection Using Photonic Crystal Fibre-Based SPR Sensor.

Coronavirus disease (COVID-19) is a worldwide health emergency caused by the coronavirus 2 (severe acute respiratory illness) (SARS-CoV-2). COVID-19 has a wide range of symptoms, making a definitive diagnosis difficult. The shortage of equipment for testing technology COVID-19 has resulted in long queues for COVID-19 testing, which is a major problem. COVID-19 testing is currently performed using sluggish and costly technology like single-photon emission computed tomography (SPECT), computed tomography (CT), positron emission tomography (PET), and enzyme-linked immunosorbent assay (ELISA). The gold standard test for diagnosing COVID-19 is real-time reverse transcriptase-polymerase chain reaction (RT-PCR), which necessitates highly skilled workers and has a lengthy turnaround time. However, rapid and affordable immunodiagnostic techniques (antigen or antibody tests) are also available with some trade off accuracy. Optical sensors are frequently employed in a variety of applications, because of their increased sensitivity, strong selectivity, rapid reaction times, and outstanding resolution. The use of photonic crystal fibre (PCF) is advantageous for the quick detection of the new coronavirus and is suggested with the use of a PCF-based (Au/BaTiO3/graphene) multilayered surface plasmon resonance (SPR) biosensor. The proposed sensor can quickly detect the COVID-19 virus in two different ligand-analyte environments: (i) the virus spike receptor-binding domain (RBD) as an analyte and monoclonal antibodies (mAbs) as a probe ligand, and (ii) monoclonal antibodies (IgG or IgM) as an analyte and the virus spike RBD as a probe ligand. The finite element method (FEM) is used to quantitatively examine the performance of the PCF-based multilayered SPR sensor.

Evaluation of reverse transcriptase-polymerase spiral reaction assay for rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2.

BACKGROUND AND AIM: Existing real-time reverse transcriptase PCR (RT-qPCR) has certain limitations for the point-of-care detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since it requires sophisticated instruments, reagents and skilled laboratory personnel. In this study, we evaluated an assay termed the reverse transcriptase-polymerase spiral reaction (RT-PSR) for rapid and visual detection of SARS-CoV-2. METHODS: The RT-PSR assay was optimized using RdRp gene and evaluated for the detection of SARS-CoV-2. The time of 60min and a temperature of 63°C was optimized for targeting the RNA-dependent RNA polymerase gene of SARS-CoV-2. The sensitivity of the assay was evaluated by diluting the in-vitro transcribed RNA, which amplifies as low as ten copies. RESULTS: The specific primers designed for this assay showed 100% specificity and did not react when tested with other lung infection-causing viruses and bacteria. The optimized assay was validated with 190 clinical samples in two phases, using automated RTPCR based TrueNat test, and the results were comparable. CONCLUSIONS: The RT-PSR assay can be considered for rapid and sensitive detection of SARS-CoV-2, particularly in resource-limited settings. To our knowledge, there is as yet no RT-PSR-based kit developed for SARS-CoV-2.

COVID-19 pneumonia level detection using deep learning algorithm and transfer learning.

The first COVID-19 confirmed case was reported in Wuhan, China, and spread across the globe with an unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, developing smart, fast, and efficient detection techniques is significant. To this end, we have developed an Artificial Intelligence engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT scan images of the confirmed COVID-19 patient using Morphological approaches. The second phase classifies the pneumonia level of the confirmed COVID-19 patient. We use a modified Convolution Neural Network (CNN) and k-Nearest Neighbor; we also compared the results of both models to the other classification algorithms to precisely classify lung inflammation. The experiments show that the CNN model can provide testing accuracy up to 95.65% compared with exiting classification techniques. The proposed system in this work can be applied efficiently to CT scan and X-ray image datasets. Also, in this work, the Transfer Learning technique has been used to train the pre-trained modified CNN model on a smaller dataset than the original dataset; the modified CNN achieved 92.80% of testing accuracy for detecting pneumonia on chest X-ray images for the relatively extensive dataset.

Rapid and high-sensitive LSPR sensor for coronavirus detection

A rapid, portable, and cost-effective method to detect the infection of SARS-CoV-2 is fundamental toward mitigating the current COVID-19 pandemic. A localized surface plasmon resonance (LSPR) sensor based on human angiotensin-converting enzyme 2 protein (ACE2) functionalized silver nanotriangle array is developed for rapid coronavirus detection. The sensor is validated by SARS-CoV-2 spike RBD protein and CoV NL63 virus with high sensitivity and specificity. A linear shift of the LSPR wavelength and transmission intensity at a fixed wavelength (750 nm) versus the logarithm of the concentration of the spike RBD protein and CoV NL63 is observed. The limits of detection for the spike RBD protein, CoV NL63 in untreated saliva are determined to be 0.38 pM, and 625 PFU/mL, respectively, while the detection time is found to be less than 20 min. Such a LSPR sensor could serve as a potential rapid point-of-care diagnostic platform for COVID-19. © 2022 SPIE

The diagnosis of COVID-19 in CT images using hybrid machine learning approaches (CNN & SVM)

The new coronavirus disease (2019) has spread quickly as an acute respiratory distress syndrome (ARDS) among millions of individuals worldwide. Furthermore, the number of COVID-19 checking obtainable in hospitals is very limited as compared to the rising number of infections every day. As an outcome, an automatic detection system must be implemented as a quick diagnostic tool for preventing or reducing the spread of COVID-19 among humans. The present paper aims to propose an automated system by means of a hybrid Deep Learning ("convolutional neural network "(CNN)) and "support vector machine (SVM) " approach for identifying COVID-19 pneumonia-infected patients on the basis of chest computed tomography (746 CT images of "COVID-19" and "non-COVID-19"). The proposed system is composed of three phases. The first, pre-processing phase begins with converting CT images into greyscale level CT images of equal size (256×256). The "contrast limited adaptive histogram equalization" technology is adopted to enhance the intensity levels, and demonstrate the feature of lung tissue. It is also necessary to normalize the division of the image elements by 255 to make the values between 0 and 1, as this will speed up the processing process. The second phase, the CNN (SimpNet model), was applied as a deep feature extraction technique to identify CT samples. The SVM classifier and SoftMax function are employed in the third phase to classify COVID-19 pneumonia-infected patients. Specificity, Sensitivity, "F-score ", Accuracy, and "area under curve" are used as criteria to estimate the efficiency of the classification. The results showed a high accuracy rate of COVID-19 classification which reached (98%) and (99.1%) for CNN-SoftMax and CNN-SVM classifier, respectively in the tested dataset (225 CT images). © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal.

Artificial Intelligence (AI) in the diagonosis of COVIS-19 Detection: A Review

Coronaviruses are a large viral family that attacks key organs, particularly the lungs. The infection spread is growing by the day, affecting almost every industry. Various Artificial Intelligence studies have been proposed, to learn the measurable information of people who have been affected with COVID-19 and those who have recovered, as well as the death rate. Various data samples like chest images, lung images, swab results, blood samples, and CT scans are used to predict the COVID-19. The paper gives an in-depth look at how AI and machine learning techniques can be used to accurately predict COVID-19. The proposed review is centered around investigating the different AI methods, models, and logical registering procedures used in foreseeing the COVID-19 sickness. The study also summarizes the difficulties associated with current methods and future exploration works.

Silver nanotriangle array based LSPR sensor for rapid coronavirus detection.

A rapid, portable, and cost-effective method to detect the infection of SARS-CoV-2 is fundamental toward mitigating the current COVID-19 pandemic. Herein, a human angiotensin-converting enzyme 2 protein (ACE2) functionalized silver nanotriangle (AgNT) array localized surface plasmon resonance (LSPR) sensor is developed for rapid coronavirus detection, which is validated by SARS-CoV-2 spike RBD protein and CoV NL63 virus with high sensitivity and specificity. A linear shift of the LSPR wavelength versus the logarithm of the concentration of the spike RBD protein and CoV NL63 is observed. The limits of detection for the spike RBD protein, CoV NL63 in buffer and untreated saliva are determined to be 0.83 pM, 391 PFU/mL, and 625 PFU/mL, respectively, while the detection time is found to be less than 20 min. Thus, the AgNT array optical sensor could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

Updating the use of nano-biosensors as promising devices for the diagnosis of coronavirus family members: A systematic review.

Coronavidae viruses, such as SARS-CoV, SARS-CoV-2, and MERS-CoV, cause severe lower respiratory tract infection, acute respiratory distress syndrome and extrapulmonary manifestations, such as diarrhea and fever, eventually leading to death. Fast, accurate, reproductible, and cost-effective SARS-CoV-2 identification can be achieved employing nano-biosensors, reinforcing conventional methodologies to avoid the spread of COVID-19 within and across communities. Nano-biosensors built using gold, silver, graphene, In2O3 nanowire and iron oxide nanoparticles, Quantum Dots and carbon nanofibers have been successfully employed to detect specific virus antigens - nucleic acid sequences and/or proteins -or host antibodies produced in response to viral infection. Biorecognition counterpart molecules have been immobilized on the surface of these nanomaterials, leading to selective virus detection by optical or electrochemical transducer systems. This systematic review assessed studies on described and tested immunonsensors and genosensors designed from distinct nanomaterials available at the Pubmed, Scopus, and Science Direct databases. Twenty-three nano biosensors were found suitable for unequivocal coronavirus detection in clinical samples. Nano-biosensors coupled to RT-LAMP/RT-PCR assays can optimize RNA extraction, reduce analysis times and/or eliminate sophisticated instrumentation. Although promising for the diagnosis of Coronavidae family members, further trials in large populations must be adequately and rigorously conducted to address nano-biosensor applicability in the clinical practice for early coronavirus infection detection.

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images.

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques.

Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.

A rapid and label-free DNA-based interference reduction nucleic acid amplification strategy for viral RNA detection.

Common reference methods for COVID-19 diagnosis include thermal cycling amplification (e.g. RT-PCR) and isothermal amplification methods (e.g. LAMP and RPA). However, they may not be suitable for direct detection in environmental and biological samples due to background signal interference. Here, we report a rapid and label-free interference reduction nucleic acid amplification strategy (IR-NAAS) that exploits the advantages of luminescent iridium(III) probes, time-resolved emission spectroscopy (TRES) and multi-branch rolling circle amplification (mbRCA). Using IR-NAAS, we established a luminescence approach for diagnosing COVID-19 RNAs sequences RdRp, ORF1ab and N with a linear range of 0.06-6.0 × 105 copies/mL and a detection limit of down to 7.3 × 104 copies/mL. Moreover, the developed method was successfully applied to detect COVID-19 RNA sequences from various environmental and biological samples, such as domestic sewage, and mice urine, blood, feces, lung tissue, throat and nasal secretions. Apart from COVID-19 diagnosis, IR-NAAS was also demonstrated for detecting other RNA viruses, such as H1N1 and CVA10, indicating that this approach has great potential approach for routine preliminary viral detection.

An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans.

The novel coronavirus outbreak of 2019 reached pandemic status in March 2020. Since then, many countries have joined efforts to fight the COVID-19 pandemic. A central task for governments is the rapid and effective identification of COVID-19 positive patients. While many molecular tests currently exist, not all hospitals have immediate access to these. However, CT scans, which are readily available at most hospitals, offer an additional method to diagnose COVID-19. As a result, hospitals lacking molecular tests can benefit from it as a way of mitigating said shortage. Furthermore, radiologists have come to achieve accuracy levels over 80% on identifying COVID-19 cases by CT scan image analysis. This paper adds to the existing literature a model based on ensemble methods and 2-stage transfer learning to detect COVID-19 cases based on CT scan images, relying on a simple architecture, yet complex enough model definition, to attain a competitive performance. The proposed model achieved an accuracy of 86.70%, an F1 score of 85.86% and an AUC of 90.82%, proving capable of assisting radiologists with COVID-19 diagnosis. Code developed for this research can be found in the following repository: https://github.com/josehernandezsc/COVID19Net.

A deep learning approach to detect Covid-19 coronavirus with X-Ray images.

Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5-fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

Discovery of Novel Coronaviruses in Rodents.

The recent emergence of SARS, SARS-CoV2 and MERS and the discovery of novel coronaviruses in animals and birds suggest that the Coronavirus family is far more diverse than previously thought. In the last decade, several new coronaviruses have been discovered in rodents around the globe, suggesting that they are the natural reservoirs of the virus. In this chapter we describe the process of screening rodent tissue for novel coronaviruses with PCR, a method that is easily adaptable for screening a range of animals.