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      Optical imaging spectroscopy for rapid, primary screening of SARS-CoV-2: a proof of concept

      research-article
      1 , 2 , , 1 , 3 , 4 , 5 , 1 , 2 , 1 , 6 , 1 , 1 , 2 , 4 , 7 , 8 , 2 , 9 , 10 , 2 , 4 , 11 , 11 , 10 , 12 , 13 , 11 , 14 , 15 , 7 , 8 , 16 , 17 , 2 , 18 , 19 , 20 , 21 , 4 , 2 , 10 , 22 , 2 , 10 , 23 , 24
      Scientific Reports
      Nature Publishing Group UK
      Viral infection, Near-infrared spectroscopy, Near-infrared spectroscopy

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          Abstract

          Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.

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          Most cited references78

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          Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study

          Our previous study demonstrated increased expression of Heat shock protein (Hsp) 90 in the skin of patients with systemic sclerosis (SSc). We aimed to evaluate plasma Hsp90 in SSc and characterize its association with SSc-related features. Ninety-two SSc patients and 92 age-/sex-matched healthy controls were recruited for the cross-sectional analysis. The longitudinal analysis comprised 30 patients with SSc associated interstitial lung disease (ILD) routinely treated with cyclophosphamide. Hsp90 was increased in SSc compared to healthy controls. Hsp90 correlated positively with C-reactive protein and negatively with pulmonary function tests: forced vital capacity and diffusing capacity for carbon monoxide (DLCO). In patients with diffuse cutaneous (dc) SSc, Hsp90 positively correlated with the modified Rodnan skin score. In SSc-ILD patients treated with cyclophosphamide, no differences in Hsp90 were found between baseline and after 1, 6, or 12 months of therapy. However, baseline Hsp90 predicts the 12-month change in DLCO. This study shows that Hsp90 plasma levels are increased in SSc patients compared to age-/sex-matched healthy controls. Elevated Hsp90 in SSc is associated with increased inflammatory activity, worse lung functions, and in dcSSc, with the extent of skin involvement. Baseline plasma Hsp90 predicts the 12-month change in DLCO in SSc-ILD patients treated with cyclophosphamide.
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            Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

            Summary Background Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19. Methods We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R 0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings Simulated outbreaks starting with five initial cases, an R 0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R 0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R 0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R 0 of 2·5 more than 70% of contacts had to be traced, and for an R 0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R 0 was 1·5. For R 0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts. Funding Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.
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              CRISPR-Cas12–based detection of SARS-CoV-2

              An outbreak of betacoronavirus SARS-CoV-2 began in Wuhan, China in December 2019. COVID-19, the disease associated with infection, rapidly spread to produce a global pandemic. We report development of a rapid (<40 min), easy-to-implement and accurate CRISPR-Cas12-based lateral flow assay for detection of SARS-CoV-2 from respiratory swab RNA extracts. We validated our method using contrived reference samples and clinical samples from US patients, including 36 patients with COVID-19 infection and 42 patients with other viral respiratory infections. Our CRISPR-based DETECTR assay provides a visual and faster alternative to the US CDC SARS-CoV-2 real-time RT-PCR assay, with 95% positive predictive agreement and 100% negative predictive agreement.. SARS-CoV-2 in patient samples is detected in under an hour using a CRISPR-based lateral flow assay.
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                Author and article information

                Contributors
                egomez@us.es
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 February 2022
                18 February 2022
                2022
                : 12
                : 2356
                Affiliations
                [1 ]GRID grid.9224.d, ISNI 0000 0001 2168 1229, Department of Applied Physics III, ETSI School of Engineering, , Universidad de Sevilla, ; Camino de los Descubrimientos s/n, 41092 Sevilla, Spain
                [2 ]GRID grid.414816.e, ISNI 0000 0004 1773 7922, Institute of Biomedicine of Seville (IBIS), ; 41013 Sevilla, Spain
                [3 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, School of Biomedical Engineering, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [4 ]GRID grid.419693.0, ISNI 0000 0004 0546 8753, Unidad de Producción y Reprogramación Celular (UPRC), Red Andaluza de Diseño y Traslación de Terapias Avanzadas, Consejería de Salud y Familias, , Junta de Andalucía, ; 41092 Sevilla, Spain
                [5 ]EOD-CBRN Group, Spanish National Police, 41011 Sevilla, Spain
                [6 ]GRID grid.9224.d, ISNI 0000 0001 2168 1229, Technology and Innovation Centre, , Universidad de Sevilla, ; 41012 Sevilla, Spain
                [7 ]GRID grid.411109.c, ISNI 0000 0000 9542 1158, Service of Intensive Care, , University Hospital ‘Virgen del Rocio’, ; 41013 Sevilla, Spain
                [8 ]GRID grid.9224.d, ISNI 0000 0001 2168 1229, Department of Medicine, College of Medicine, , Universidad de Sevilla, ; 41009 Seville, Spain
                [9 ]GRID grid.411109.c, ISNI 0000 0000 9542 1158, Service of Anesthesiology, , University Hospital ‘Virgen del Rocio’, ; 41013 Sevilla, Spain
                [10 ]GRID grid.9224.d, ISNI 0000 0001 2168 1229, Department of Surgery, College of Medicine, , Universidad de Sevilla, ; 41009 Seville, Spain
                [11 ]GRID grid.450285.e, ISNI 0000 0004 1793 7043, Instituto de Astrofísica de Andalucía, CSIC, ; 18008 Granada, Spain
                [12 ]GRID grid.411375.5, ISNI 0000 0004 1768 164X, Department of Ophthalmology, , University Hospital ‘Virgen Macarena’, ; 41009 Sevilla, Spain
                [13 ]GRID grid.413448.e, ISNI 0000 0000 9314 1427, OftaRed, , Institute of Health ‘Carlos III’, ; 28029 Madrid, Spain
                [14 ]GRID grid.510988.d, Centro Astronomico Hispano Alemán, ; 04550 Almeria, Spain
                [15 ]GRID grid.9224.d, ISNI 0000 0001 2168 1229, Department of Electronic Engineering, ETSI School of Engineering, , Universidad de Sevilla, ; 41092 Sevilla, Spain
                [16 ]GRID grid.7759.c, ISNI 0000000103580096, Department of Biomedicine, Biotechnology and Public Health, , University of Cadiz, ; 11003 Cadiz, Spain
                [17 ]GRID grid.512013.4, Instituto de Investigación e Innovación Biomedica de Cádiz (INIBICA), ; 11009 Cadiz, Spain
                [18 ]GRID grid.467320.4, Corporación Tecnológica de Andalucía, ; 41092 Sevilla, Spain
                [19 ]CER ‘Dr. Gregorio Medina Blanco’, 41807 Espartinas, Sevilla Spain
                [20 ]CAMBRICO BIOTECH, 41015 Sevilla, Spain
                [21 ]Joint Research Centre, European Commission, 41092 Sevilla, Spain
                [22 ]GRID grid.411109.c, ISNI 0000 0000 9542 1158, Department of General Surgery, , University Hospital ‘Virgen del Rocío’, ; 41013 Sevilla, Spain
                [23 ]GRID grid.411109.c, ISNI 0000 0000 9542 1158, Service of Neurosurgery, , University Hospital ‘Virgen del Rocío’, ; 41013 Sevilla, Spain
                [24 ]Centre for Advanced Neurology, 41013 Sevilla, Spain
                Article
                6393
                10.1038/s41598-022-06393-3
                8857323
                35181702
                8ae84b9e-55ff-4e88-8857-e514d4a862c2
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 September 2021
                : 28 January 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004587, Instituto de Salud Carlos III;
                Award ID: COV20-00080
                Award ID: COV20-00173
                Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;
                Award ID: EQC2019-006240-P
                Funded by: FundRef http://dx.doi.org/10.13039/501100011033, Agencia Estatal de Investigación;
                Award ID: RTI2018-094465-J
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                viral infection,near-infrared spectroscopy
                Uncategorized
                viral infection, near-infrared spectroscopy

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