MRNA lipid nanoparticles induce immune tolerance to treat human diseases

Rapid developments in the coronavirus disease 2019 (COVID-19) mRNA vaccine showcased the power of lipid nanoparticle (LNP) delivery systems in fighting infectious diseases. In addition, mRNA therapeutics are also in development for cancer immunotherapy. Recently, mRNA therapy has been expanded to induce immune tolerance, the opposite of immune-boosting effects, to treat diseases involving enhanced immune responses including allergies and autoimmune diseases. mRNA LNPs have been used to treat peanut allergy by us and autoimmune experimental autoimmune encephalomyelitis by Ugur Sahin. It is expected that more and more research is going to delve into the immune tolerance field for allergies and autoimmune diseases, where effective therapies are in short supply. © 2023 the author(s), published by De Gruyter, Berlin/Boston.

Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning

The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this article, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Black carbon exposures and trends in Detroit, Michigan: Synthesis of fixed, mobile and indoor measurements

Black carbon (BC) is a component of fine particulate matter (PM2.5) with strong light-absorbing properties, climate warming potential, adverse impacts on human health, and a high correlation with traffic emissions in urban areas, especially emissions from heavy-duty diesel trucks. This study conducted an integrative analysis examining spatial and temporal trends and long-term BC exposures in southwest Detroit, which is home to vulnerable communities as well as extensive truck traffic and industrial emissions. The area is unusual in the density and nature of ambient and indoor monitoring, and the extensive truck traffic on both highways and many surface streets. We analyze three datasets. First, six fixed sites have continuously monitored hourly BC concentrations (and other pollutants) since late 2018. Second, 5-minute BC concentrations have been measured across the region using a mobile platform, the Michigan Pollution Assessment Laboratory (MPAL), on about two days per week. Third, 15-minute BC measurements have been obtained inside and outside of 37 residences, all located within 65 to 900 m of a major interstate highway. These data show striking spatial patterns, with southwest Detroit experiencing BC levels approximately twice those found elsewhere, but nearby, in the urban area. Time-of-day and day-of-week patterns are correlated to traffic flows, also implicating truck traffic. Data acquired by the mobile platform, providing high-resolution spatial information, illustrate high levels along trucking routes and gradients that reflect dispersion from sources, supplementing the patterns illustrated by the continuous fixed-site monitoring. The indoor measurements show high BC levels in most homes, and that indoor and outdoor levels are very similar, indicating high penetration of BC indoors, little filtration or attenuation of BC indoors, and the significance of exposure to residents. Trends of BC levels show effects of the March to April 2020 COVID-19 "lockdown, ” but levels have climbed upwards. Importantly, BC trends differ significantly from PM2.5. Overall, these data suggest that local diesel exhaust emissions is a major source of BC and PM2.5 in the region, and that exposures and health risks can be lowered by reducing and rerouting truck traffic, improving emission controls, and using buffers and filters to reduce indoor levels. © 2022 Air and Waste Management Association. All rights reserved.

Reviving the economy while saving lives: a deep reinforcement learning approach for smart POI reopening

With the gradual improvements in COVID-19 metrics and the accelerated immunization progress, countries around the world have began to focus on reviving the economy while continuously strengthening epidemic control. POInt-of-Interest (POI) reopening, as a necessity for restoring human mobilities, has become a crucial step to recouple economic recovery and public health management. In contrast to the lock-down policy, POI reopening demands a dynamic trade-off between epidemic interventions and economic costs. In the urban scenario, there exist three key challenges in developing effective POI reopening strategies as follows. (1) During the POI reopening process, there are multiple urban factors affecting the epidemic transmission, which are difficult to simultaneously incorporate and balance in a single reopening strategy;(2) the effects of POI reopening on both economic recovery and epidemic control are long-term, which are hard to capture by static models;and (3) the dual objectives of minimizing infections and maintaining POIs' visits are conflicting, making it difficult to achieve a flexible and scalable trade-off. To tackle the above challenges, we propose Reopener, a deep reinforcement learning (RL) framework for smart POI reopening. First, we utilize a bipartite graph neural network to automatically encode all urban factors that would affect the epidemic prevention and POI visit restriction. Second, we employ a RL-based deep policy network to enable flexible updates in restrictions on POIs along with the trend of epidemic. Third, we design a novel reward function to guide the RL agent to learn smartly, thus comprehensively trading off infections and visit sustainability of POIs. Extensive experimental results demonstrate that Reopener outperforms all baseline methods with remarkable improvements, by reducing the overall economic cost by at least 6.42%. Reopener can effectively suppress infections and support a phase-based POI reopening process, which provides valuable insights for strategy design in post-COVID-19 economic recovery. © 2022 Owner/Author.

Detection of COVID-19 Through Thermal and Voice Sensing Using Smartphone

Since the end of 2019, the world has been caught in the crisis of the COVID-19 which is a serious epidemic disease. This paper seeks to come up with a fast and efficient COVID-19 detection and monitoring easy to use system which can be used in the facilities of densely populated areas, such as community centers and school clinics, to quickly identify suspected COVID-19 patients. This system could detect the probability of a person getting infected by COVID-19 using an android smartphone and thermal camera. Three types of data are collected from users: breathe sound, thermal video, and health status. Generally, the breathe audio and thermal video are preprocessed into two-time series, which indicate the breath status of the user. Then, the two series are inputted into the Bidirectional Gated Recurrent Unit (BI-GRU) neural network model separately to get the infection rates. Since the real data is difficult to get due to privacy reasons, a synthetic dataset is generated based on mathematical equations to train the model. For health status, the application requires the user to fill a questionnaire and calculates an infection rate through a medical prediction model. Finally, the two values from the machine learning model and the infection rate from the user report are added together with weight to calculate the final predictive infection rate. © 2022 IEEE.

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech;in the Escalation Sub-Challenge, a three-way assessment of the level of escalation in a dialogue is featured;and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit;in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.

Precise Mobility Intervention for Epidemic Control Using Unobservable Information via Deep Reinforcement Learning

To control the outbreak of COVID-19, efficient individual mobility intervention for EPidemic Control (EPC) strategies are of great importance, which cut off the contact among people at epidemic risks and reduce infections by intervening the mobility of individuals. Reinforcement Learning (RL) is powerful for decision making, however, there are two major challenges in developing an RL-based EPC strategy: (1) the unobservable information about asymptomatic infections in the incubation period makes it difficult for RL's decision-making, and (2) the delayed rewards for RL causes the deficiency of RL learning. Since the results of EPC are reflected in both daily infections (including unobservable asymptomatic infections) and long-term cumulative cases of COVID-19, it is quite daunting to design an RL model for precise mobility intervention. In this paper, we propose a Variational hiErarcHICal reinforcement Learning method for Epidemic control via individual-level mobility intervention, namely Vehicle. To tackle the above challenges, Vehicle first exploits an information rebuilding module that consists of a contact-risk bipartite graph neural network and a variational LSTM to restore the unobservable information. The contact-risk bipartite graph neural network estimates the possibility of an individual being an asymptomatic infection and the risk of this individual spreading the epidemic, as the current state of RL. Then, the Variational LSTM further encodes the state sequence to model the latency of epidemic spreading caused by unobservable asymptomatic infections. Finally, a Hierarchical Reinforcement Learning framework is employed to train Vehicle, which contains dual-level agents to solve the delayed reward problem. Extensive experimental results demonstrate that Vehicle can effectively control the spread of the epidemic. Vehicle outperforms the state-of-the-art baseline methods with remarkably high-precision mobility interventions on both symptomatic and asymptomatic infections. © 2022 Owner/Author.

Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence (Nature Machine Intelligence, (2021), 3, 12, (1081-1089), 10.1038/s42256-021-00421-z)

In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.

Mobility-based Individual POI Recommendation to Control the COVID-19 Spread

Societal functions have stalled during COVID-19 to reduce its spread in the population. It has been shown that visits to different venues have a large effect on spreading the virus. Hence, population-level mobility interventions like reopening selective category of venues have been proposed, for example, opening schools and offices but preventing people from visiting restaurants. These measures, although help to mitigate infection, still fail to satisfy people's needs and hope of going back to normality. In this context, here we propose an individual level POI recommendation system that can recommend venues to users according to their preference and at the same time, can lead to as few infections as possible. The key idea behind the system is that the risk of getting infected grows with the number of unique customers that had visited the venue previously, and it is safer to visit a less crowded place during a specific time slot. We evaluate the proposed system using both theory and real check-in datasets from three cities. Based on simulation on real-world data, we present a surprising result: it is possible to recommend POIs in such a way that the total infected population reduces by up to 50% compared to that following original check-ins. This result is comparable to that when 50% of the visits are blocked, yet our method allows all check-in needs. © 2021 IEEE.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

Uncertainty-aware covid-19 detection from imbalanced sound data

Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital prescreening. However, there are still two unsolved issues hindering the practice. First, collected datasets for model training are often imbalanced, with a considerably smaller proportion of users tested positive, making it harder to learn representative and robust features. Second, deep learning models are generally overconfident in their predictions. Clinically, false predictions aggravate healthcare costs. Estimation of the uncertainty of screening would aid this. To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data. As such, data are utilized more effectively compared to traditional up-sampling and down-sampling approaches: an AUC of 0.74 with a sensitivity of 0.68 and a specificity of 0.69 is achieved. Simultaneously, we estimate uncertainty from the disagreement across multiple models. It is shown that false predictions often yield higher uncertainty, enabling us to suggest the users with certainty higher than a threshold to repeat the audio test on their phones or to take clinical tests if digital diagnosis still fails. This study paves the way for a more robust sound-based COVID-19 automated screening system. Copyright © 2021 ISCA.

EXPLORING AUTOMATIC COVID-19 DIAGNOSIS VIA VOICE AND SYMPTOMS FROM CROWDSOURCED DATA

The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of 0.79 has been attained, with a sensitivity of 0.68 and a specificity of 0.82. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.

Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19. © 2021, Springer Nature Switzerland AG.

Potential nanoparticle applications for prevention, diagnosis, and treatment of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses an unprecedented challenge to establish effective methods of prevention and treatment. Nanotechnology has shown excellent potential in its ability to fight a variety of healthcare problems due to nanomaterials' unique physicochemical properties and controlled nano-bio interactions. Expanding their application to a wide-range of upstream and downstream approaches is necessary to fight COVID-19. At different stages of this virus-caused disease, nanotechnology could offer promising solutions, including a way to combat the large number of fatalities caused by a late-stage cytokine storm. This mini-review provides an overview of the recent studies regarding nanoparticles' applications in vaccines, personal protective equipment, sterilization of contaminated environments, diagnostic testing, and cytokine reduction treatment in combating COVID-19.

AI-enabled Microscopic Blood Analysis for Microfluidic COVID-19 Hematology

Microscopic blood cell analysis is an important methodology for medical diagnosis, and complete blood cell counts (CBCs) are one of the routine tests operated in hospitals. Results of the CBCs include amounts of red blood cells, white blood cells and platelets in a unit blood sample. It is possible to diagnose diseases such as anemia when the numbers or shapes of red blood cells become abnormal. The percentage of white blood cells is one of the important indicators of many severe illnesses such as infection and cancer. The amounts of platelets are decreased when the patient suffers hemophilia. Doctors often use these as criteria to monitor the general health conditions and recovery stages of the patients in the hospital. However, many hospitals are relying on expensive hematology analyzers to perform these tests, and these procedures are often time consuming. There is a huge demand for an automated, fast and easily used CBCs method in order to avoid redundant procedures and minimize patients' burden on costs of healthcare. In this research, we investigate a new CBC detection method by using deep neural networks, and discuss state of the art machine learning methods in order to meet the medical usage requirements. The approach we applied in this work is based on YOLOv3 algorithm, and our experimental results show the applied deep learning algorithms have a great potential for CBCs tests, promising for deployment of deep learning methods into microfluidic point-of-care medical devices. As a case of study, we applied our blood cell detector to the blood samples of COVID-19 patients, where blood cell clots are a typical symptom of COVID-19.

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis. © 2020 Owner/Author.