COVID-19 may improve our CBRNE detection capabilities

By Prof. Emilio Gómez-González, University of Seville and Institute of Biomedicine of Seville, and Inspector José Manuel Navas, TEDAX Unit - CBRNE team leader, Spain

More than two years have elapsed since the World Health Organization (WHO) declared a public health emergency on January 30th, 2020, when a new strain of coronavirus began to spread from continental China. It was named as ‘severe acute respiratory syndrome coronavirus 2’ (SARS-CoV-2), and its propagation rapidly evolved into the current pandemic of the associated coronavirus disease 2019 (COVID-19). By mid-April 2022, more than 507 million persons have been infected, and over 6.2 million deaths have been reported worldwide. The new virus propagates among humans mainly through the aerosols and small droplets of respiratory fluids exhaled when talking, coughing, sneezing, or just breathing. This mechanism of contagion has proven to be very effective, and a substantial challenge for the current detection techniques and epidemiological control methods in place. In addition to the obvious impact of the COVID-19 pandemic on public health, European and international law-enforcement agencies such as EUROPOL, INTERPOL, UNICRI, CTPN , and military institutions like NATO have drawn attention to new biological threats that may exist if similar infectious agents –potentially enhanced by artificial intelligence techniques – are maliciously used.

Optical imaging spectroscopy for early detection of SARS-CoV-2

The response of the international scientific community to the compromised public health resulting from the propagation of the SARS-CoV-2 was immediate. Thus, in April 2020 a research project coordinated by the University of Seville –entitled ‘Project C-CLEAN’, led by the first author– was launched with the collaboration of eleven Spanish institutions –including, prominently, the Group of Interdisciplinary Physics of the University of Seville, and the EOD-CBRN Group of the Spanish National Police– and the support of the European Commission. This Project was funded by the Emergency Call for Research Projects on SARS-CoV-2 and COVID-19 of the Institute of Health 'Carlos III' of the Spanish Ministry of Science and Innovation, and its overall aim was the design and development of a new technology for optical detection of the virus in fluid samples. The resulting methodology was tested on synthetic models and published in 2021, and the successful outcomes of a proof of concept with human samples were published in February 2022. The new technique –designated as ‘optical imaging spectroscopy’– relies on an innovative combination of hyperspectral imaging in the visible and near infrared ranges and the use of advanced statistics and artificial intelligence (machine learning) algorithms for the classification of samples. It allows for the detection of the tested viruses in liquid droplets and dry residues deposited on surfaces, and presents the advantages of simultaneous, rapid processing of multiple samples –those within the field of view of the camera– without contact or reagents, and with relatively simple equipment, usable by personnel with minimal training.

Initially, the technology was developed using two types of synthetic viruses commonly employed as laboratory models of the SARS-CoV-2 virus: lentiviral particles pseudotyped with the G protein of the vesicular stomatitis and with the spike S protein of the SARS-CoV-2. These models were prepared in two biofluids (saline solution and artificial saliva). Aforementioned technique demonstrated it was able to detect, differentiate, and quantify these two types of synthetic viral models. Subsequently, the technique was extended for the detection of the SARS-CoV-2 virus in human samples. The proof of concept obtained a sensitivity of 100% and a specificity of 87.5% in nasopharyngeal exudates (the same samples used in molecular tests) from symptomatic people. It was also possible to detect the presence of SARS-CoV-2 in fresh saliva of asymptomatic people.

EOD-CBRN first responder of the Spanish National Police preparing samples of synthetic viral models at the facilities of the Group of Interdisciplinary Physics, ETSI-Engineering School, University of Seville, in the C-CLEAN Project

The potential of imaging spectroscopy for CBRNE risk detection One of the most interesting features of the new technology of imaging spectroscopy for detection of pathogens lies with the possibility of generating sets of numerical parameters (called ‘descriptors’) that characterize certain properties of the light and its interaction with the substance analyzed. These parameters allow for the generation of specific spectral signatures, conceptually analogous to ‘fingerprints’, which could potentially be used to detect and identify high-risk pathogens and hazardous chemical substances.

In addition, although still at initial stages in its application to the chemical, biological, radiological, nuclear and explosive (CBRNE) scenarios, this approach presents additional advantages from the first responders’ point of view. It is non-destructive and contactless, –as hyperspectral cameras register the energy reflected or scattered by the elements in their field of view–, the operator can work remotely (from centimeters to over meters) depending on the optical equipment, and it does not use reagents, therefore not altering the explored substances. In addition, it may be possible to use illumination wavelengths passing through certain materials (e.g., containers) and being scattered by the targeted elements. In addition, as imaging spectroscopy analyses all pixels of the registered images, it may allow for the exploration of complex scenarios with multiple samples and post-explosion studies.

Prototype of the optical setup of the C-CLEAN Project at the facilities of the Group of Interdisciplinary Physics, ETSI-Engineering School, University of Seville

Conclusions and future lines

There remains extensive work ahead, but the positive results obtained by optical imaging spectroscopy for virus detection, endorsed by corresponding scientific publications, confirm the interest and perspectives of this technique, and how it could improve the CBRNE detection capabilities in the future. Furthermore, this research underpins the value of collaboration between the academia and police force [13] with the common goals of preserving and improving the security of our societies.

Acknowledgments

This research was funded by Grant Number COV20-00080 of the 2020 Emergency Call for Research Projects about the SARS-CoV-2 virus and the COVID-19 disease of the Institute of Health ‘Carlos III’, Spanish Ministry of Science and Innovation, supported by the European Commission through the HUMAINT Project of the Joint Research Centre, by Grant Number EQC2019-006240-P funded by MICIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, by a 2021 Research Grant of the Spanish Police Foundation, and supported by the 2022 Program of Company Acceleration of Biotechnology and Health Technology in Boston of the Extenda Trade Promotion Agency of the Andalusia’s Regional Government.

The authors working in the C-CLEAN Project at the facilities of the Group of Interdisciplinary Physics, ETSI-Engineering School, University of Seville, Spain

REFERENCES

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[13] Navas Garcia, J. The EOD-CBRN Unit of the Spanish National Police. NCT Magazine (2020). https://nct-magazine.com/nct-magazine-june-2020/the-eod-cbrn-unit-of-the-spanish-national-police/.

AUTHOR

Emilio Gómez-González is Full Professor of Applied Physics at the University of Seville and Director of its Group of Interdisciplinary Physics, Dpt. of Applied Physics III, ETSI Engineering School University of Seville, Spain. He also belongs to the Group of Applied Neuroscience, Institute of Biomedicine of Seville.

With over 20 years of experience in academia and in real-world applications of research, his work lines comprise optical technologies and photonics combined with artificial intelligence in highly demanding scenarios, from image guided surgery and medicine –neurosurgery, fetal surgery– to EOD-CBRN police applications. Significant developments include the codirection of internationally pioneering surgical procedures, the design of a surgical helmet microscope and multispectral imaging devices, and advances in non-invasive ultrasound focusing techniques. He collaborates with the Centre for Advanced Studies (Project Humaint) of the Joint Research Center of the European Commission in the study of artificial intelligence systems and their impact on human behavior, particularly related to medicine and new challenges in ethics and security, and with the EOD-CBRN Group of the Spanish National Police. Since the COVID-19 outbreak, he leads a multidisciplinary project of eleven Spanish institutions supported by the European Commission to develop new optical methods for virus detection.

AUTHOR

Jose M. Navas is a Police Inspector that belongs to the Intelligence Department and works as Head of EOD-CBRN Group of the Spanish National Police (Team Leader in Seville). Since joining the force in 1998, he has also worked in Judicial Police and Counter-Terrorism Unit. He has led the disposal and neutralization of numerous Spanish Civil War bombs and CBRN incidents. He is a frequent lecturer in police training and EOD-CBRN refresher courses, in collaboration with other national institutions (Civil Protection, Local Police).

Since 2007, he has attended many international meetings and working groups. In 2008 he joined the CBRN Task Force Group established by the European Commission. He leads research conducted alongside the Group of Interdisciplinary Physics of the ETSI Engineering School of the University of Seville, funded by the Spanish Police Foundation, to develop a First Responder Robot for scenarios with CBRN agents and explosives, and improvements of X-ray and thermal imaging techniques. Most recent projects in collaboration with national and European funding include an early warning network system along the river of Seville, and a technology for detection of the SARS-COV-2 virus on surfaces, using advanced imaging and artificial intelligence.