CRISPR in Targeted Therapy and Adoptive T Cell Immunotherapy for Hepatocellular Carcinoma.

Despite recent therapeutic advancements, outcomes for advanced hepatocellular carcinoma (HCC) remain unsatisfactory, highlighting the need for novel treatments. The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) gene-editing technology offers innovative treatment approaches, involving genetic manipulation of either cancer cells or adoptive T cells to combat HCC. This review comprehensively assesses the applications of CRISPR systems in HCC treatment, focusing on in vivo targeting of cancer cells and the development of chimeric antigen receptor (CAR) T cells and T cell receptor (TCR)-engineered T cells. We explore potential synergies between CRISPR-based cancer therapeutics and existing treatment options, discussing ongoing clinical trials and the role of CRISPR technology in improving HCC treatment outcomes with advanced safety measures. In summary, this review provides insights into the promising prospects and current challenges of using CRISPR technology in HCC treatment, with the ultimate goal of improving patient outcomes and revolutionizing the landscape of HCC therapeutics.

Green Synthesis of Ceria Nanoparticles from Cassava Tubers for Electrochemical Aptasensor Detection of SARS-CoV-2 on a Screen-Printed Carbon Electrode.

Green synthesis approaches for making nanosized ceria using starch from cassava as template molecules to control the particle size are reported. The results of the green synthesis of ceria with an optimum calcination temperature of 800 °C shows a size distribution of each particle of less than 30 nm with an average size of 9.68 nm, while the ratio of Ce3+ to Ce4+ was 25.6%. The green-synthesized nanoceria are applied to increase the sensitivity and attach biomolecules to the electrode surface of the electrochemical aptasensor system for coronavirus disease (COVID-19). The response of the aptasensor to the receptor binding domain of the virus was determined with the potassium ferricyanide redox system. The screen-printed carbon electrode that has been modified with green-synthesized nanoceria shows 1.43 times higher conductivity than the bare electrode, while those modified with commercial ceria increase only 1.18 times. Using an optimized parameter for preparing the aptasensors, the detection and quantification limits were 1.94 and 5.87 ng·mL-1, and the accuracy and precision values were 98.5 and 89.1%. These results show that green-synthesized ceria could be a promising approach for fabricating an electrochemical aptasensor.

The Optimization of a Label-Free Electrochemical DNA Biosensor for Detection of Sus scrofa mtDNA as Food Adulterations.

Fast, sensitive, and easy-to-use methods for detecting DNA related to food adulteration, health, religious, and commercial purposes are evolving. In this research, a label-free electrochemical DNA biosensor method was developed for the detection of pork in processed meat samples. Gold electrodeposited screen-printed carbon electrodes (SPCEs) were used and characterized using SEM and cyclic voltammetry. A biotinylated probe DNA sequence of the Cyt b S. scrofa gene mtDNA used as a sensing element containing guanine substituted by inosine bases. The detection of probe-target DNA hybridization on the streptavidin-modified gold SPCE surface was carried out by the peak guanine oxidation of the target using differential pulse voltammetry (DPV). The optimum experimental conditions of data processing using the Box-Behnken design were obtained after 90 min of streptavidin incubation time, at the DNA probe concentration of 1.0 µg/mL, and after 5 min of probe-target DNA hybridization. The detection limit was 0.135 µg/mL, with a linearity range of 0.5-1.5 µg/mL. The resulting current response indicated that this detection method was selective against 5% pork DNA in a mixture of meat samples. This electrochemical biosensor method can be developed into a portable point-of-care detection method for the presence of pork or food adulterations.

A label-free electrochemical DNA biosensor used a printed circuit board gold electrode (PCBGE) to detect SARS-CoV-2 without amplification.

The emergence of coronavirus disease 2019 (COVID-19) motivates continuous efforts to develop robust and accurate diagnostic tests to detect severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Detection of viral nucleic acids provides the highest sensitivity and selectivity for diagnosing early and asymptomatic infection because the human immune system may not be active at this stage. Therefore, this work aims to develop a label-free electrochemical DNA biosensor for SARS-CoV-2 detection using a printed circuit board-based gold substrate (PCBGE). The developed sensor used the nucleocapsid phosphoprotein (N) gene as a biomarker. The DNA sensor-based PCBGE was fabricated by self-assembling a thiolated single-stranded DNA (ssDNA) probe onto an Au surface, which performed as the working electrode (WE). The Au surface was then treated with 6-mercapto-1-hexanol (MCH) before detecting the target N gene to produce a well-oriented arrangement of the immobilized ssDNA chains. The successful fabrication of the biosensor was characterized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and atomic force microscopy (AFM). The DNA biosensor performances were evaluated using a synthetic SARS-CoV-2 genome and 20 clinical RNA samples from healthy and infected individuals through EIS. The developed DNA biosensor can detect as low as 1 copy per µL of the N gene within 5 minutes with a LOD of 0.50 µM. Interestingly, the proposed DNA sensor could distinguish the expression of SARS-CoV-2 RNA in a patient diagnosed with COVID-19 without any amplification technique. We believe that the proposed DNA sensor platform is a promising point-of-care (POC) device for COVID-19 viral infection since it offers a rapid detection time with a simple design and workflow detection system, as well as an affordable diagnostic assay.

Aptamer-Based Electrochemical Biosensors for the Detection of Salmonella: A Scoping Review.

The development of rapid, accurate, and efficient detection methods for Salmonella can significantly control the outbreak of salmonellosis that threatens global public health. Despite the high sensitivity and specificity of the microbiological, nucleic-acid, and immunological-based methods, they are impractical for detecting samples outside of the laboratory due to the requirement for skilled individuals and sophisticated bench-top equipment. Ideally, an electrochemical biosensor could overcome the limitations of these detection methods since it offers simplicity for the detection process, on-site quantitative analysis, rapid detection time, high sensitivity, and portability. The present scoping review aims to assess the current trends in electrochemical aptasensors to detect and quantify Salmonella. This review was conducted according to the latest Preferred Reporting Items for Systematic review and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A literature search was performed using aptamer and Salmonella keywords in three databases: PubMed, Scopus, and Springer. Studies on electrochemical aptasensors for detecting Salmonella published between January 2014 and January 2022 were retrieved. Of the 787 studies recorded in the search, 29 studies were screened for eligibility, and 15 studies that met the inclusion criteria were retrieved for this review. Information on the Salmonella serovars, targets, samples, sensor specification, platform technologies for fabrication, electrochemical detection methods, limit of detection (LoD), and detection time was discussed to evaluate the effectiveness and limitations of the developed electrochemical aptasensor platform for the detection of Salmonella. The reported electrochemical aptasensors were mainly developed to detect Salmonella enterica Typhimurium in chicken meat samples. Most of the developed electrochemical aptasensors were fabricated using conventional electrodes (13 studies) rather than screen-printed electrodes (SPEs) (two studies). The developed aptasensors showed LoD ranges from 550 CFU/mL to as low as 1 CFU/mL within 5 min to 240 min of detection time. The promising detection performance of the electrochemical aptasensor highlights its potential as an excellent alternative to the existing detection methods. Nonetheless, more research is required to determine the sensitivity and specificity of the electrochemical sensing platform for Salmonella detection, particularly in human clinical samples, to enable their future use in clinical practice.

Detection of Tropical Diseases Caused by Mosquitoes Using CRISPR-Based Biosensors.

Tropical diseases (TDs) are among the leading cause of mortality and fatality globally. The emergence and reemergence of TDs continue to challenge healthcare system. Several tropical diseases such as yellow fever, tuberculosis, cholera, Ebola, HIV, rotavirus, dengue, and malaria outbreaks have led to endemics and epidemics around the world, resulting in millions of deaths. The increase in climate change, migration and urbanization, overcrowding, and other factors continue to increase the spread of TDs. More cases of TDs are recorded as a result of substandard health care systems and lack of access to clean water and food. Early diagnosis of these diseases is crucial for treatment and control. Despite the advancement and development of numerous diagnosis assays, the healthcare system is still hindered by many challenges which include low sensitivity, specificity, the need of trained pathologists, the use of chemicals and a lack of point of care (POC) diagnostic. In order to address these issues, scientists have adopted the use of CRISPR/Cas systems which are gene editing technologies that mimic bacterial immune pathways. Recent advances in CRISPR-based biotechnology have significantly expanded the development of biomolecular sensors for diagnosing diseases and understanding cellular signaling pathways. The CRISPR/Cas strategy plays an excellent role in the field of biosensors. The latest developments are evolving with the specific use of CRISPR, which aims for a fast and accurate sensor system. Thus, the aim of this review is to provide concise knowledge on TDs associated with mosquitoes in terms of pathology and epidemiology as well as background knowledge on CRISPR in prokaryotes and eukaryotes. Moreover, the study overviews the application of the CRISPR/Cas system for detection of TDs associated with mosquitoes.

Computer aided detection of tuberculosis using two classifiers.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.

Quantum Dot-Based Lateral Flow Immunoassay as Point-of-Care Testing for Infectious Diseases: A Narrative Review of Its Principle and Performance.

Infectious diseases are the world's greatest killers, accounting for millions of deaths worldwide annually, especially in low-income countries. As the risk of emerging infectious diseases is increasing, it is critical to rapidly diagnose infections in the early stages and prevent further transmission. However, current detection strategies are time-consuming and have exhibited low sensitivity. Numerous studies revealed the advantages of point-of-care testing, such as those which are rapid, user-friendly and have high sensitivity and specificity, and can be performed at a patient's bedside. The Lateral Flow Immunoassay (LFIA) is the most popular diagnostic assay that fulfills the POCT standards. However, conventional AuNPs-LFIAs are moderately sensitive, meaning that rapid detection remains a challenge. Here, we review quantum dot (QDs)-based LFIA for highly sensitive rapid diagnosis of infectious diseases. We briefly describe the principles of LFIA, strategies for applying QDs to enhance sensitivity, and the published performance of the QD-LFIA tested against several infectious diseases.

CRISPR-Cas Systems-Based Bacterial Detection: A Scoping Review.

Recently, CRISPR-Cas system-based assays for bacterial detection have been developed. The aim of this scoping review is to map existing evidence on the utilization of CRISPR-Cas systems in the development of bacterial detection assays. A literature search was conducted using three databases (PubMed, Scopus, and Cochrane Library) and manual searches through the references of identified full texts based on a PROSPERO-registered protocol (CRD42021289140). Studies on bacterial detection using CRISPR-Cas systems that were published before October 2021 were retrieved. The Critical Appraisal Skills Programme (CASP) qualitative checklist was used to assess the risk of bias for all the included studies. Of the 420 studies identified throughout the search, 46 studies that met the inclusion criteria were included in the final analysis. Bacteria from 17 genera were identified utilising CRISPR-Cas systems. Most of the bacteria came from genera such as Staphylococcus, Escherichia, Salmonella, Listeria, Mycobacterium and Streptococcus. Cas12a (64%) is the most often used Cas enzyme in bacterial detection, followed by Cas13a (13%), and Cas9 (11%). To improve the signal of detection, 83% of the research exploited Cas enzymes' trans-cleavage capabilities to cut tagged reporter probes non-specifically. Most studies used the extraction procedure, whereas only 17% did not. In terms of amplification methods, isothermal reactions were employed in 66% of the studies, followed by PCR (23%). Fluorescence detection (67%) was discovered to be the most commonly used method, while lateral flow biosensors (13%), electrochemical biosensors (11%), and others (9%) were found to be less commonly used. Most of the studies (39) used specific bacterial nucleic acid sequences as a target, while seven used non-nucleic acid targets, including aptamers and antibodies particular to the bacteria under investigation. The turnaround time of the 46 studies was 30 min to 4 h. The limit of detection (LoD) was evaluated in three types of concentration, which include copies per mL, CFU per mL and molarity. Most of the studies used spiked samples (78%) rather than clinical samples (22%) to determine LoD. This review identified the gap in clinical accuracy evaluation of the CRISPR-Cas system in bacterial detection. More research is needed to assess the diagnostic sensitivity and specificity of amplification-free CRISPR-Cas systems in bacterial detection for nucleic acid-based tests.

Gold-Modified Molecularly Imprinted N-Methacryloyl-(l)-phenylalanine-containing Electrodes for Electrochemical Detection of Dopamine.

A molecularly imprinted polymer-based pencil graphite electrode (MIP PGE) sensor, modified with gold nanoparticles, was utilized for the detection of dopamine in the presence of other biochemical compounds using cyclic voltammetry (CV) and differential pulse voltammetry (DPV), depending on its strong electroactivity function. The pulse voltammetry methods recorded the highest response. In addition to the high oxidation rate of DA and the other biomolecule interferences available in the sample matrix used, which cause overlapping voltammograms, we aimed to differentiate them in a highly sensitive limit of detection range. The calibration curves for DA were obtained using the CV and DPV over the concentration range of 0.395-3.96 nM in 0.1 M phosphate buffer solution (PBS) at pH 7.4 with a correlation coefficient of 0.996 and a detection limit of 0.193 nM. The electrochemical technique was employed to detect DA molecules quantitatively in human blood plasma selected as real samples without applying any pre-treatment processes. MIP electrodes proved their ability to detect DA with high selectivity, even with epinephrine and norepinephrine competitor molecules and interferences, such as ascorbic acid (AA). The high level of recognition achieved by molecularly imprinted polymers (MIPs) is essential for many biological and pharmaceutical studies.

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview.

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10-18 M), Femtomolar (10-15 M) and Picomolar (10-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10-9 M and micromolar 10-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images.

The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K-nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG-19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID-19.

RT-LAMP CRISPR-Cas12/13-Based SARS-CoV-2 Detection Methods.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has attracted public attention. The gold standard for diagnosing COVID-19 is reverse transcription-quantitative polymerase chain reaction (RT-qPCR). However, RT-qPCR can only be performed in centralized laboratories due to the requirement for advanced laboratory equipment and qualified workers. In the last decade, clustered regularly interspaced short palindromic repeats (CRISPR) technology has shown considerable promise in the development of rapid, highly sensitive, and specific molecular diagnostic methods that do not require complicated instrumentation. During the current COVID-19 pandemic, there has been growing interest in using CRISPR-based diagnostic techniques to develop rapid and accurate assays for detecting SARS-CoV-2. In this work, we review and summarize reverse-transcription loop-mediated isothermal amplification (RT-LAMP) CRISPR-based diagnostic techniques for detecting SARS-CoV-2.

Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.

Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

Automated detection of Mycobacterium tuberculosis using transfer learning.

INTRODUCTION: Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges: (1) The size of Mycobacterium tuberculosis is very small and difficult to identify as a result of low contrast background, heterogenous shape, irregular appearance and faint boundaries (2) Mycobacterium tuberculosis overlapped with each other making it difficult to conduct accurate diagnosis (3) Large amount of slide can be time consuming and tedious to microbiologist and which can lead to misinterpretations. METHODOLOGY: To solve these challenges and limitations, we proposed an automated-based detection method using pretrained AlexNet to trained the model in 3 sets of experiments A, B and C and adjust the protocols accordingly. We compared the detection of tuberculosis using AlexNet Models with the ground truth result provided by microbiologist and analyzed inconsistencies between network models and human. RESULTS: 98.15 % accuracy, 96.77% sensitivity and 100% specificity for experiment A, 98.09% accuracy, 98.59% sensitivity and 97.67% specificity for experiment B and 98.73% testing accuracy, 98.59 sensitivity, 98.84% specificity ofr experiment C which sound robust and promising. CONCLUSIONS: The results indicated that network performance was successful with high accuracies, sensitivities and specificities and it can be used to support microbiologist for diagnosis of tuberculosis.

CRISPR-Cas13 System as a Promising and Versatile Tool for Cancer Diagnosis, Therapy, and Research.

Over the past decades, significant progress has been made in targeted cancer therapy. In precision oncology, molecular profiling of cancer patients enables the use of targeted cancer therapeutics. However, current diagnostic methods for molecular analysis of cancer are costly and require sophisticated equipment. Moreover, targeted cancer therapeutics such as monoclonal antibodies and small-molecule drugs may cause off-target effects and they are available for only a minority of cancer driver proteins. Therefore, there is still a need for versatile, efficient, and precise tools for cancer diagnostics and targeted cancer treatment. In recent years, the CRISPR-based genome and transcriptome engineering toolbox has expanded rapidly. Particularly, the RNA-targeting CRISPR-Cas13 system has unique biochemical properties, making Cas13 a promising tool for cancer diagnosis, therapy, and research. Cas13-based diagnostic methods allow early detection and monitoring of cancer markers from liquid biopsy samples without the need for complex instrumentation. In addition, Cas13 can be used for targeted cancer therapy through degrading and manipulating cancer-associated transcripts with high efficiency and specificity. Moreover, Cas13-mediated programmable RNA manipulation tools offer invaluable opportunities for cancer research, identification of drug-resistance mechanisms, and discovery of novel therapeutic targets. Here, we review and discuss the current use and potential applications of the CRISPR-Cas13 system in cancer diagnosis, therapy, and research. Thus, researchers will gain a deep understanding of CRISPR-Cas13 technologies, which have the potential to be used as next-generation cancer diagnostics and therapeutics.

Electrochemical Sensor for Determination of Various Phenolic Compounds in Wine Samples Using Fe3O4 Nanoparticles Modified Carbon Paste Electrode.

Phenolic compounds contain classes of flavonoids and non-flavonoids, which occur naturally as secondary metabolites in plants. These compounds, when consumed in food substances, improve human health because of their antioxidant properties against oxidative damage diseases. In this study, an electrochemical sensor was developed using a carbon paste electrode (CPE) modified with Fe3O4 nanoparticles (MCPE) for the electrosensitive determination of sinapic acid, syringic acid, and rutin. The characterization techniques adapted for CPE, MCPE electrodes, and the solution interface were cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Scan rate and pH were the parameters subjected to optimization studies for the determination of phenolic compounds. The incorporation of Fe3O4 nanoparticles to the CPE as a sensor showed excellent sensitivity, selectivity, repeatability, reproducibility, stability, and low preparation cost. The limits of detection (LOD) obtained were 2.2 × 10-7 M for sinapic acid, 2.6 × 10-7 M for syringic acid, and 0.8 × 10-7 M for rutin, respectively. The fabricated electrochemical sensor was applied to determine phenolic compounds in real samples of red and white wine.

Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19.

The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.

CRISPR-based tools: Alternative methods for the diagnosis of COVID-19.

The recently emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spread all over the world rapidly and caused a global pandemic. To prevent the virus from spreading to more individuals, it is of great importance to identify and isolate infected individuals through testing. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the gold standard method for the diagnosis of coronavirus disease (COVID-19) worldwide. However, performing RT-qPCR is limited to centralized laboratories because of the need for sophisticated laboratory equipment and skilled personnel. Further, it can sometimes give false negative or uncertain results. Recently, new methods have been developed for nucleic acid detection and pathogen diagnosis using CRISPR-Cas systems. These methods present rapid and cost-effective diagnostic platforms that provide high sensitivity and specificity without the need for complex instrumentation. Using the CRISPR-based SARS-CoV-2 detection methods, it is possible to increase the number of daily tests in existing laboratories, reduce false negative or uncertain result rates obtained with RT-qPCR, and perform testing in resource-limited settings or at points of need where performing RT-qPCR is not feasible. Here, we briefly describe the RT-qPCR method, and discuss its limitations in meeting the current diagnostic needs. We explain how the unique properties of various CRISPR-associated enzymes are utilized for nucleic acid detection and pathogen diagnosis. Then, we highlight the important features of CRISPR-based diagnostic methods developed for SARS-CoV-2 detection. Finally, we examine the advantages and limitations of these methods, and discuss how they can contribute to improving the efficiency of the current testing systems for combating SARS-CoV-2.

Detection of CRISPR-Cas9-Mediated Mutations Using a Carbon Nanotube-Modified Electrochemical Genosensor.

The CRISPR-Cas9 system has facilitated the genetic modification of various model organisms and cell lines. The outcomes of any CRISPR-Cas9 assay should be investigated to ensure/improve the precision of genome engineering. In this study, carbon nanotube-modified disposable pencil graphite electrodes (CNT/PGEs) were used to develop a label-free electrochemical nanogenosensor for the detection of point mutations generated in the genome by using the CRISPR-Cas9 system. Carbodiimide chemistry was used to immobilize the 5'-aminohexyl-linked inosine-substituted probe on the surface of the sensor. After hybridization between the target sequence and probe at the sensor surface, guanine oxidation signals were monitored using differential pulse voltammetry (DPV). Optimization of the sensitivity of the nanogenoassay resulted in a lower detection limit of 213.7 nM. The nanogenosensor was highly specific for the detection of the precisely edited DNA sequence. This method allows for a rapid and easy investigation of the products of CRISPR-based gene editing and can be further developed to an array system for multiplex detection of different-gene editing outcomes.