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Register a new userFast Accurate Point of Care COVID-19 Pandemic Diagnosis Enabled Through Advanced Lab-on-a-Chip Optical Biosensors: Opportunities and Challenges
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The sudden rise of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic early 2020 throughout the world has called into drastic action measures to do instant detection and reduce the spread rate. The common diagnostics testing methods has been only partially effective in satisfying the booming demand for fast detection methods to contain the further spread. However, the point-of-risk accurate diagnosis of this new emerging viral infection is paramount as simultaneous normal working operation and dealing with symptoms of SARS-CoV-2 can become the norm for years to come. Sensitive cost-effective biosensor with mass production capability is crucial throughout the world until a universal vaccination become available. Optical label-free biosensors can provide a non-invasive, extremely sensitive rapid detection technique up to ~1 fM concentration along with few minutes sensing. These biosensors can be manufactured on a mass-scale (billions) to detect the COVID-19 viral load in nasal, saliva, urinal, and serological samples even if the infected person is asymptotic. Methods investigated here are the most advanced available platforms for biosensing optical devices resulted from the integration of state-of-the-art designs and materials. These approaches are including but not limited to integrated optical devices, plasmonic resonance and also emerging nanomaterial biosensors. The lab-on-a-chip platforms examined here are suitable not only for SARS-CoV-2 spike protein detection but also other contagious virions such as influenza, and middle east respiratory syndrome (MERS).
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Optically hyperpolarized $^{129}$Xe gas has become a powerful contrast agent in nuclear magnetic resonance (NMR) spectroscopy and imaging, with applications ranging from studies of the human lung to the targeted detection of biomolecules. Equally attractive is its potential use to enhance the sensitivity of microfluidic NMR experiments, in which small sample volumes yield poor sensitivity. Unfortunately, most $^{129}$Xe polarization systems are large and non-portable. Here we present a microfabricated chip that optically polarizes $^{129}$Xe gas. We have achieved $^{129}$Xe polarizations greater than 0.5$%$ at flow rates of several microliters per second, compatible with typical microfluidic applications. We employ in situ optical magnetometry to sensitively detect and characterize the $^{129}$Xe polarization at magnetic fields of 1 $mu$T. We construct the device using standard microfabrication techniques, which will facilitate its integration with existing microfluidic platforms. This device may enable the implementation of highly sensitive $^{129}$Xe NMR in compact, low-cost, portable devices.
The outbreak of COVID-19 pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this paper, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contract tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers and researchers to fast develop and deploy effective digital contact tracing applications, to conquer COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.
Medical robots can play an important role in mitigating the spread of infectious diseases and delivering quality care to patients during the COVID-19 pandemic. Methods and procedures involving medical robots in the continuum of care, ranging from disease prevention, screening, diagnosis, treatment, and homecare have been extensively deployed and also present incredible opportunities for future development. This paper provides an overview of the current state-of-the-art, highlighting the enabling technologies and unmet needs for prospective technological advances within the next 5-10 years. We also identify key research and knowledge barriers that need to be addressed in developing effective and flexible solutions to ensure preparedness for rapid and scalable deployment to combat infectious diseases.
Background: The inability to test at scale has become humanitys Achilles heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture. Results: Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19 coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to channel clinical-testing and treatment to those who need it the most, thereby saving more lives.
The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environments and third world countries. Our contributions towards rapid large-scale testing include a novel deep learning architecture capable of analyzing ultrasound data that can run in real-time and significantly improve the current state-of-the-art detection accuracies using image-based COVID-19 detection.
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