COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections.
TL;DR: In this paper, a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection, was presented.
Abstract: The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.
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TL;DR: In this paper , air lasing naturally created inside a filament can serve as an ideal light source to probe Raman coherence excited by the femtosecond pump, producing coherent Raman signal with molecular vibrational signatures.
Abstract: Remote or standoff detection of greenhouse gases, air pollutants, and biological agents with innovative ultrafast laser technology attracts growing interests in recent years. Hybrid femtosecond/picosecond coherent Raman spectroscopy is considered as one of the most versatile techniques due to its great advantages in terms of detection sensitivity and chemical specificity. However, the simultaneous requirement for the femtosecond pump and the picosecond probe increases the complexity of optical system. Herein, we demonstrate that air lasing naturally created inside a filament can serve as an ideal light source to probe Raman coherence excited by the femtosecond pump, producing coherent Raman signal with molecular vibrational signatures. The combination of pulse self-compression effect and air lasing action during filamentation improves Raman excitation efficiency and greatly simplifies the experimental setup. The air-lasing-assisted Raman spectroscopy was applied to quantitatively detect greenhouse gases mixed in air, and it was found that the minimum detectable concentrations of CO2 and SF6 can reach 0.1% and 0.03%, respectively. The ingenious designs, especially the optimization of pump-seed delay and the choice of perpendicular polarization, ensure a high detection sensitivity and signal stability. Moreover, it is demonstrated that this method can be used for simultaneously measuring CO2 and SF6 gases and distinguishing 12CO2 and 13CO2. The developed scheme provides a new route for high-sensitivity standoff detection and combustion diagnosis.
52 citations
TL;DR: In this paper, a very distinct spectroscopic signature of the SARS-CoV-2 virus can be obtained using surface enhanced Raman spectroscopy (SERS) of virion particles.
Abstract: The COVID-19 pandemic demonstrated the critical need for accurate and rapid testing for virus detection. This need has generated a high number of new testing methods aimed at replacing RT-PCR, which is the golden standard for testing. Most of the testing techniques are based on biochemistry methods and require chemicals that are often expensive and the supply might become scarce in a large crisis. In the present paper we suggest the use of methods based on physics that leverage novel nanomaterials. We demonstrate that using Surface Enhanced Raman Spectroscopy (SERS) of virion particles a very distinct spectroscopic signature of the SARS-CoV-2 virus can be obtained. We demonstrate that the spectra are mainly composed by signals from the spike (S) and nucleocapsid (N) proteins. It is believed that a clinical test using SERS can be developed. The test will be fast, inexpensive, and reliable. It is also clear that SERS can be used for analysis of structural changes on the S and N proteins. This will be an example of application of nanotechnology and properties of nanoparticles for health and social related matters.
38 citations
TL;DR: In this paper, a multiplexed, 1-2-step, fast (20-30min) SARS-CoV-2 molecular test using reverse transcription recombinase polymerase amplification was developed to simultaneously detect two conserved targets -the E and RdRP genes.
33 citations
Imperial College London1, University of Central Lancashire2, Monash University, Clayton campus3, University of Strathclyde4, University of Manchester5, Imperial College Healthcare6, Federal University of Rio Grande do Norte7, Lancashire Teaching Hospitals NHS Foundation Trust8, University of Ioannina9, Keele University10, University of Wisconsin–Eau Claire11
TL;DR: In this article, the authors highlight applications of IR and Raman spectroscopy, with a focus on cancer and infectious diseases since 2015, and highlight the diverse sample types that can be analyzed, such as biofluids, cells and tissues.
Abstract: Analytical technologies that can improve disease diagnosis are highly sought after. Current screening/diagnostic tests for several diseases are limited by their moderate diagnostic performance, invasiveness, costly and laborious methodologies or the need for multiple tests before a definitive diagnosis. Spectroscopic techniques, including infrared (IR) and Raman, have attracted great interest in the medical field, with applications expanding from early disease detection to monitoring and real-time diagnosis.
This review highlights applications of IR and Raman spectroscopy, with a focus on cancer and infectious diseases since 2015, and underscores the diverse sample types that can be analyzed, such as biofluids, cells and tissues. Studies involving more than 25 participants per group (disease and control group; if no control group >25 in disease group) were considered eligible, to retain the clinical focus of the paper. Following literature searches, we identified 94 spectroscopic studies on different cancers and 30 studies on infectious diseases. The review suggests that such technologies have the potential to develop into an objective, inexpensive, point-of-care test or facilitate disease diagnosis and monitoring. Up-to-date considerations for the implementation of spectroscopic techniques into a clinical setting, health economics and successful applications of vibrational spectroscopic tests in the clinical arena are also discussed.
30 citations
TL;DR: In this article , the concentrations of both atmospheric and marine microplastics were measured over the Baltic along a research cruise that started in the Gdansk harbour, till the Gotland island, and the way back.
28 citations
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TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
47,974 citations
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
46,982 citations
TL;DR: A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
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16,496 citations
TL;DR: It is demonstrating that cross-neutralizing antibodies targeting conserved S epitopes can be elicited upon vaccination, and it is shown that SARS-CoV-2 S uses ACE2 to enter cells and that the receptor-binding domains of Sars- coV- 2 S and SARS S bind with similar affinities to human ACE2, correlating with the efficient spread of SATS among humans.
7,219 citations
TL;DR: This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data.
Abstract: Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.
5,782 citations