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      Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases

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          Abstract

          On global scale, the current situation of pandemic is symptomatic of increased incidences of contagious diseases caused by pathogens. The faster spread of these diseases, in a moderately short timeframe, is threatening the overall population wellbeing and conceivably the economy. The inadequacy of conventional diagnostic tools in terms of time consuming and complex laboratory-based diagnosis process is a major challenge to medical care. In present era, the development of point-of-care testing (POCT) is in demand for fast detection of infectious diseases along with “on-site” results that are helpful in timely and early action for better treatment. In addition, POCT devices also play a crucial role in preventing the transmission of infectious diseases by offering real-time testing and lab quality microbial diagnosis within minutes. Timely diagnosis and further treatment optimization facilitate the containment of outbreaks of infectious diseases. Presently, efforts are being made to support such POCT by the technological development in the field of internet of medical things (IoMT). The IoMT offers wireless-based operation and connectivity of POCT devices with health expert and medical centre. In this review, the recently developed POC diagnostics integrated or future possibilities of integration with IoMT are discussed with focus on emerging and re-emerging infectious diseases like malaria, dengue fever, influenza A (H1N1), human papilloma virus (HPV), Ebola virus disease (EVD), Zika virus (ZIKV), and coronavirus (COVID-19). The IoMT-assisted POCT systems are capable enough to fill the gap between bioinformatics generation, big rapid analytics, and clinical validation. An optimized IoMT-assisted POCT will be useful in understanding the diseases progression, treatment decision, and evaluation of efficacy of prescribed therapy.

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          Estimates of the severity of coronavirus disease 2019: a model-based analysis

          Summary Background In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older. Interpretation These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding UK Medical Research Council.
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            Global trends in emerging infectious diseases

            The next new disease Emerging infectious diseases are a major threat to health: AIDS, SARS, drug-resistant bacteria and Ebola virus are among the more recent examples. By identifying emerging disease 'hotspots', the thinking goes, it should be possible to spot health risks at an early stage and prepare containment strategies. An analysis of over 300 examples of disease emerging between 1940 and 2004 suggests that these hotspots can be accurately mapped based on socio-economic, environmental and ecological factors. The data show that the surveillance effort, and much current research spending, is concentrated in developed economies, yet the risk maps point to developing countries as the more likely source of new diseases. Supplementary information The online version of this article (doi:10.1038/nature06536) contains supplementary material, which is available to authorized users.
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              SARS and MERS: recent insights into emerging coronaviruses

              Key Points Severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are zoonotic pathogens that can cause severe respiratory disease in humans. Although disease progression is fairly similar for SARS and MERS, the case fatality rate of MERS is much higher than that of SARS. Comorbidities have an important role in SARS and MERS. Several risk factors are associated with progression to acute respiratory distress syndrome (ARDS) in SARS and MERS cases, especially advanced age and male sex. For MERS, additional risk factors that are associated with severe disease include chronic conditions such as diabetes mellitus, hypertension, cancer, renal and lung disease, and co-infections. Although the ancestors of SARS-CoV and MERS-CoV probably circulate in bats, zoonotic transmission of SARS-CoV required an incidental amplifying host. Dromedary camels are the MERS-CoV reservoir from which zoonotic transmission occurs; serological evidence indicates that MERS-CoV-like viruses have been circulating in dromedary camels for at least three decades. Human-to-human transmission of SARS-CoV and MERS-CoV occurs mainly in health care settings. Patients do not shed large amounts of virus until well after the onset of symptoms, when patients are most probably already seeking medical care. Analysis of hospital surfaces after the treatment of patients with MERS showed the ubiquitous presence of infectious virus. Our understanding of the pathogenesis of SARS-CoV and MERS-CoV is still incomplete, but the combination of viral replication in the lower respiratory tract and an aberrant immune response is thought to have a crucial role in the severity of both syndromes. The severity of the diseases that are caused by emerging coronaviruses highlights the need to develop effective therapeutic measures against these viruses. Although several treatments for SARS and MERS (based on inhibition of viral replication with drugs or neutralizing antibodies, or on dampening the host response) have been identified in animal models and in vitro studies, efficacy data from human clinical trials are urgently required. Supplementary information The online version of this article (doi:10.1038/nrmicro.2016.81) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Biosens Bioelectron
                Biosens Bioelectron
                Biosensors & Bioelectronics
                Published by Elsevier B.V.
                0956-5663
                1873-4235
                6 February 2021
                6 February 2021
                : 113074
                Affiliations
                [1 ]Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, 125001, India
                [2 ]University Institute of Engineering and Technology, Panjab University, Chandigarh, India
                [3 ]NanoBioTech Laboratory, Health Systems Engineering, Department of Natural Sciences, Florida Polytechnic University, Lakeland, FL, 33805-8531, United States
                [4 ]Department of Biomedical Engineering, Florida International University, Miami, FL, 33174, USA
                [5 ]Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
                Author notes
                []Corresponding author.
                [∗∗ ]Corresponding author. Department of Biomedical Engineering, Florida International University, Miami, FL, 33174, USA.
                [∗∗∗ ]Corresponding author.
                Article
                S0956-5663(21)00111-1 113074
                10.1016/j.bios.2021.113074
                7866895
                33596516
                95294e0c-7791-4528-b34b-841d98ee5186
                © 2021 Published by Elsevier B.V.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 23 September 2020
                : 1 February 2021
                : 2 February 2021
                Categories
                Article

                Biomedical engineering
                artificial intelligence,biosensor,infectious diseases,intelligent healthcare,internet of medical things,point-of-care-testing

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