Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning

Moitra, Parikshit; Chaichi, Ardalan; Hasan, Syed Mohammad Abid; Dighe, Ketan; Alafeef, Maha; Prasad, Alisha; Gartia, Manas Ranjan; Pan, Dipanjan

doi:10.1016/j.bios.2022.114200

Cited by 37 publications

(56 citation statements)

References 71 publications

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“…Standard commercial kits were used to extract and purify RNA from the clinical samples. Antisense oligonucleotides (ASOs) were designed based on the whole genome sequence of SARS-CoV-2 [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. The ASOs were 20 nucleotides in length.…”

Section: Methodsmentioning

confidence: 99%

“…The thiolated ASOs were used to cap citrate-stabilized gold nanoparticles (AuNPs). For the Raman experiments, 2 µL of Au-ASO NPs (concentration of 2 × 10 11 particles/mL) were mixed with 2 µL of SARS-CoV-2 RNA (concentration ranging from 1 fg/mL, i.e., 63 copies/mL to 1 μg/mL, i.e., 63 × 10 9 copies/mL) by gentle pipetting a few times on an ice rack [ 12 , 30 , 33 , 34 ]. An amount of 2 µL of this mixture was drop-casted on a clean stainless-steel slide and then allowed to air-dry at room temperature.…”

Section: Methodsmentioning

confidence: 99%

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A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

et al. 2022

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In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model’s uncertainty is unacceptably high.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

et al. 2022

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“…Using a machine-learning algorithm to analyze the Raman spectrum, the test was successful in detecting SARS-CoV-2 with sensitivity and specificity of 100% and 90%, respectively. The system showed a limit of detection of 63 copies/mL of SARS-CoV-2 RNA concentration …”

Section: Diagnostic Tests Of Sars-cov-2 Based On Nanomaterials and 2d...mentioning

confidence: 99%

“…The system showed a limit of detection of 63 copies/mL of SARS-CoV-2 RNA concentration. 192 Apart from Raman spectroscopy, the FT-IR technique has been used for the diagnosis of COVID-19. Kitane et al 193 reported a label-free approach for the rapid detection of SARS-CoV-2 using the FT-IR spectroscopy technique.…”

Section: Diagnostic Tests Of Sars-cov-2 Based On Nanomaterials and 2d...mentioning

confidence: 99%

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Alafeef

Pan

2022

ACS Nano

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Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

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“…In addition, quantification of viral nucleic acids can be utilized in the early diagnosis, treatment, and assessment of how an individual is responding to antiviral therapy (Amini et al, 2017;Hsieh et al, 2022a). There is an urgent need to produce a diagnostic device, such as biosensing for infectious samples detection, which can carry out rapid, sensitive, and accurate detection of viral infection and replication during its early stages (Moitra et al, 2021;Hsieh et al, 2022b;Guo et al, 2022;Dighe et al, 2022;Liang et al, 2022;Moitra et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Flexible Terahertz Metamaterial Biosensor for Ultra-Sensitive Detection of Hepatitis B Viral DNA Based on the Metal-Enhanced Sandwich Assay

Wang

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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The high sensitivity and specificity of terahertz (THz) biosensing are both promising and challenging in DNA sample detection. This study produced and refined a flexible THz MM biosensor for ultrasensitive detection of HBV in clinical serum samples based on a gold magnetic nanoparticle-mediated rolling circle amplification (GMNPs@RCA) sandwich assay under isothermal conditions. Typically, solid-phase RCA reactions mediated by circular padlock probes (PLPs) are triggered under isothermal conditions in the presence of HBV DNA, resulting in long single-stranded DNA (ssDNA) with high fidelity and specificity. Then, the resultant ssDNA was conjugated with detection probes (DPs) immobilized on gold nanoparticles (DP@AuNPs) to form GMNPs-RCA-AuNPs sandwich complexes. The HBV DNA concentrations were quantified by introducing GMNPs-RCA-AuNPs complexes into the metasurface of a flexible THz metamaterial-based biosensor chip and resulting in a red shift of the resonance peak of the THz metamaterials. This biosensor can lead to highly specific and sensitive detection with one-base mismatch discrimination and a limit of detection (LOD) down to 1.27E + 02 IU/ml of HBV DNA from clinical serum samples. The HBV DNA concentration was linearly correlated with the frequency shift of the THz metamaterials within the range of 1.27E + 02∼1.27E + 07 IU/ml, illustrating the applicability and accuracy of our assay in real clinical samples. This strategy constitutes a promising THz sensing method to identify virus DNA. In the future, it is hoped it can assist with pathogen identification and clinical diagnosis.

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Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning

Cited by 37 publications

References 71 publications

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Flexible Terahertz Metamaterial Biosensor for Ultra-Sensitive Detection of Hepatitis B Viral DNA Based on the Metal-Enhanced Sandwich Assay

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