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      Guidelines for Robotic Flexible Endoscopy at the Time of COVID-19

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          Abstract

          Flexible endoscopy involves the insertion of a long narrow flexible tube into the body for diagnostic and therapeutic procedures. In the gastrointestinal (GI) tract, flexible endoscopy plays a major role in cancer screening, surveillance, and treatment programs. As a result of gas insufflation during the procedure, both upper and lower GI endoscopy procedures have been classified as aerosol generating by the guidelines issued by the respective societies during the COVID-19 pandemic—although no quantifiable data on aerosol generation currently exists. Due to the risk of COVID-19 transmission to healthcare workers, most societies halted non-emergency and diagnostic procedures during the lockdown. The long-term implications of stoppage in cancer diagnoses and treatment is predicted to lead to a large increase in preventable deaths. Robotics may play a major role in this field by allowing healthcare operators to control the flexible endoscope from a safe distance and pave a path for protecting healthcare workers through minimizing the risk of virus transmission without reducing diagnostic and therapeutic capacities. This review focuses on the needs and challenges associated with the design of robotic flexible endoscopes for use during a pandemic. The authors propose that a few minor changes to existing platforms or considerations for platforms in development could lead to significant benefits for use during infection control scenarios.

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          Most cited references64

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          Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents

          Summary Currently, the emergence of a novel human coronavirus, SARS-CoV-2, has become a global health concern causing severe respiratory tract infections in humans. Human-to-human transmissions have been described with incubation times between 2-10 days, facilitating its spread via droplets, contaminated hands or surfaces. We therefore reviewed the literature on all available information about the persistence of human and veterinary coronaviruses on inanimate surfaces as well as inactivation strategies with biocidal agents used for chemical disinfection, e.g. in healthcare facilities. The analysis of 22 studies reveals that human coronaviruses such as Severe Acute Respiratory Syndrome (SARS) coronavirus, Middle East Respiratory Syndrome (MERS) coronavirus or endemic human coronaviruses (HCoV) can persist on inanimate surfaces like metal, glass or plastic for up to 9 days, but can be efficiently inactivated by surface disinfection procedures with 62–71% ethanol, 0.5% hydrogen peroxide or 0.1% sodium hypochlorite within 1 minute. Other biocidal agents such as 0.05–0.2% benzalkonium chloride or 0.02% chlorhexidine digluconate are less effective. As no specific therapies are available for SARS-CoV-2, early containment and prevention of further spread will be crucial to stop the ongoing outbreak and to control this novel infectious thread.
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            Viral load of SARS-CoV-2 in clinical samples

            An outbreak caused by a novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in Wuhan in December 2019, 1 and has since spread within China and to other countries. Real-time RT-PCR assays are recommended for diagnosis of SARS-CoV-2 infection. 2 However, viral dynamics in infected patients are still yet to be fully determined. Here, we report our findings from different types of clinical specimens collected from 82 infected individuals. Serial samples (throat swabs, sputum, urine, and stool) from two patients in Beijing were collected daily after their hospitalisation (patient 1, days 3–12 post-onset; patient 2, days 4–15 post-onset). These samples were examined by an N-gene-specific quantitative RT-PCR assay, as described elsewhere. 3 The viral loads in throat swab and sputum samples peaked at around 5–6 days after symptom onset, ranging from around 104 to 107 copies per mL during this time (figure A, B ). This pattern of changes in viral load is distinct from the one observed in patients with SARS, which normally peaked at around 10 days after onset. 4 Sputum samples generally showed higher viral loads than throat swab samples. No viral RNA was detected in urine or stool samples from these two patients. Figure Viral dynamics of SARS-CoV-2 in infected patients Viral load (mean [SD]) from serial throat swab and sputum samples in patient 1 (A) and patient 2 (B). (C) Viral load (median [IQR]) in throat and sputum samples collected from 80 patients at different stages after disease onset. (D) Correlation between viral load in throat swab samples and viral load in sputum samples. We also studied respiratory samples (nasal [n=1] and throat swabs [n=67], and sputum [n=42]) collected from 80 individuals at different stages of infection. The viral loads ranged from 641 copies per mL to 1·34 × 1011 copies per mL, with a median of 7·99 × 104 in throat samples and 7·52 × 105 in sputum samples (figure C). The only nasal swab tested in this study (taken on day 3 post-onset) showed a viral load of 1·69 × 105 copies per mL. Overall, the viral load early after onset was high (>1 × 106 copies per mL). However, a sputum sample collected on day 8 post-onset from a patient who died had a very high viral load (1·34 × 1011 copies per mL). Notably, two individuals, who were under active surveillance because of a history of exposure to SARS-CoV-2-infected patients showed positive results on RT-PCR a day before onset, suggesting that infected individuals can be infectious before them become symptomatic. Among the 30 pairs of throat swab and sputum samples available, viral loads were significantly correlated between the two sample types for days 1–3 (R2=0·50, p=0·022), days 4–7 (R2=0·93, p<0·001), and days 7–14 (R2=0·95, p=0·028). From 17 confirmed cases of SARS-CoV-2 infection with available data (representing days 0–13 after onset), stool samples from nine (53%; days 0–11 after onset) were positive on RT-PCR analysis. Although the viral loads were less than those of respiratory samples (range 550 copies per mL to 1·21 × 105 copies per mL), precautionary measures should be considered when handling faecal samples.
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              Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors

              According to GLOBOCAN 2018 data, colorectal cancer (CRC) is the third most deadly and fourth most commonly diagnosed cancer in the world. Nearly 2 million new cases and about 1 million deaths are expected in 2018. CRC incidence has been steadily rising worldwide, especially in developing countries that are adopting the “western” way of life. Obesity, sedentary lifestyle, red meat consumption, alcohol, and tobacco are considered the driving factors behind the growth of CRC. However, recent advances in early detection screenings and treatment options have reduced CRC mortality in developed nations, even in the face of growing incidence. Genetic testing and better family history documentation can enable those with a hereditary predisposition for the neoplasm to take preventive measures. Meanwhile, the general population can reduce their risk by lowering their red meat, alcohol, and tobacco consumption and raising their consumption of fibre, wholesome foods, and certain vitamins and minerals.
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                Author and article information

                Contributors
                Journal
                Front Robot AI
                Front Robot AI
                Front. Robot. AI
                Frontiers in Robotics and AI
                Frontiers Media S.A.
                2296-9144
                25 February 2021
                2021
                : 8
                : 612852
                Affiliations
                [ 1 ]School of Electronic and Electrical Engineering, University of Leeds, Leeds, United Kingdom
                [ 2 ]Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
                [ 3 ]Department of Surgical Sciences, University of Torino, Torino, Italy
                [ 4 ]Department of Gastroenterology, Hepatology, Nutrition, Vanderbilt University Medical Center, Nashville, TN, United States
                [ 5 ]Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, United States
                Author notes

                Edited by: Mahdi Tavakoli, University of Alberta, Canada

                Reviewed by: J. Micah Prendergast, Delft University of Technology, Netherlands

                Antonia Tzemanaki, University of Bristol, United Kingdom

                *Correspondence: Pietro Valdastri, p.valdastri@ 123456leeds.ac.uk ; Onaizah Onaizah, o.onaizah@ 123456leeds.ac.uk

                This article was submitted to Biomedical Robotics, a section of the journal Frontiers in Robotics and AI

                Article
                612852
                10.3389/frobt.2021.612852
                7947201
                90a167a1-d4f0-4cf4-8fc5-c6ccd25c1ba1
                Copyright © 2021 Onaizah, Koszowska, Winters, Subramanian, Jayne, Arezzo, Obstein and Valdastri.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 September 2020
                : 20 January 2021
                Funding
                Funded by: Cancer Research UK 10.13039/501100000289
                Award ID: 27744
                Funded by: Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038
                Award ID: PDF-546133-2020
                Funded by: Engineering and Physical Sciences Research Council 10.13039/501100000266
                Award ID: EP/R045291/1 EP/P027938/1
                Funded by: National Institute of Biomedical Imaging and Bioengineering 10.13039/100000070
                Award ID: R01EB018992
                Funded by: Horizon 2020 10.13039/501100007601
                Award ID: 818045
                Funded by: Ministero della Salute 10.13039/501100003196
                Award ID: n. PE-2013-02359172
                Categories
                Robotics and AI
                Mini Review

                robotic flexible endoscopy,endoscopes,gastrointestinal,infection control,aerosol generating procedure,covid-19

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