Scaling law of COVID-19 cases and city size and its evolution over time

Urban scaling law quantifies the disproportional growth of urban indicators with urban population size, which is one of the simple rules behind the complex urban system. Infectious diseases are closely related to social interactions that intensify in large cities, resulting in a faster speed of transmission in large cities. However, how this scaling relationship varies in an evolving pandemic is rarely investigated and remains unclear. Here, taking the COVID- 19 epidemic in the United States as an example, we collected daily added cases and deaths from January 2020 to June 2022 in more than three thousand counties to explore the scaling law of COVID- 19 cases and city size and its evolution over time. Results show that COVID- 19 cases super- linearly scaled with population size, which means cases increased faster than population size from a small city to a large city, resulting in a higher morbidity rate of COVID- 19 in large cities. Temporally, the scaling exponent that reflects the scaling relationship stabilized at around 1.25 after a fast increase from less than one. The scaling exponent gradually decreased until it was close to one. In comparison, deaths caused by the epidemic did not show a super-linear scaling relationship with population size, which revealed that the fatality rate of COVID-19 in large cities was not higher than that in small or medium-sized cities. The scaling exponent of COVID- 19 deaths shared a similar trend with that of COVID- 19 cases but with a lag in time. We further estimated scaling exponents in each wave of the epidemic, respectively, which experienced the common evolution process of first rising, then stabilizing, and then decreasing. We also analyzed the evolution of scaling exponents over time from regional and provincial perspectives. The northeast, where New York State is located, had the highest scaling exponent, and the scaling exponent of COVID- 19 deaths was higher than that of COVID-19 cases, which indicates that large cities in this region were more prominently affected by the epidemic. This study reveals the size effect of infectious diseases based on the urban scaling law, and the evolution process of scaling exponents over time also promotes the understanding of the urban scaling law. The mechanism behind temporal variations of scaling exponents is worthy of further exploration. © 2023 Science Press. All rights reserved.

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

Effective multi-step ahead container throughput forecasting under the complex context

Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi-input and multi-output) strategy to conduct multi-step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo-Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making. © 2023 John Wiley & Sons Ltd.

Antiviral agents for COVID-19.

In December 2019, the endemic of COVID-19 broke out in Wuhan, China. The disease is highly contagious and quickly spreads at home and abroad, causing great concern. However, there are no definite effective antiviral drugs in clinical use. Given the urgency of the COVID-19 outbreak, based on the diagnosis and treatment recommendation and relavant researches, this article describes the optional antiviral drugs such as remdesivir, oseltamivir, arbidol, lopinavir/ritonavir, ribavirin, and interferon-alpha to provide a reference for treatment of COVID-19.Copyright © 2020 by the Chinese Medical Association.

Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development

This study employs the time-varying vector parameter autoregression model and Diebold-Yilmaz (2012, 2014) spillover approach to explore the static, net, dynamic and directional spillover effects between China's traditional energy and emerging green markets and the impact of the COVID-19 outbreak on spillover effects. Spillover networks are constructed to observe structural changes in the directional spillover of each target financial market before and after the pandemic's outbreak. Changes in hedging indicators of portfolios composed of two types of markets before and after the outbreak of COVID-19 are compared to provide directional guidance for investors to choose portfolios in the post-pandemic era. We found that the outbreak of the pandemic had a considerable impact on the volatility of various spillover effects of the studied markets. The total spillover level of the system increased rapidly by 18% in the early stages of the pandemic. Green bond was the largest net recipient of volatility spillovers in the whole system, followed by crude oil, while new energy was the largest net contributor of volatility spillovers in the whole system, followed by clean energy. After the outbreak, the hedging effectiveness of portfolios with long positions in traditional energy markets and short positions in emerging green markets improved significantly. In particular, a portfolio with long positions in the crude oil market and short positions in the green bond market is the best risk-hedging portfolio. © 2023 Elsevier Ltd

Racial framing of pandemic outcomes has conditional indirect effects on support for COVID-19 mitigation policies: Examining moral and threat-based mediating mechanisms

Two studies explored the intersection between the COVID-19 pandemic and the continuing fight for racial justice. The pandemic has exacerbated existing racial inequalities in the United States in terms of public health and economic outcomes, and it is well-established that individuals higher in racial bias are less likely to support social safety net programs such as those meant to improve public health and reduce poverty. This is particularly true among individuals who perceive racial minorities as overbenefitting from safety net programs. Accordingly, the primary focus of the current studies was to examine whether framing the pandemic in terms of its disproportionate impact on minorities would reduce support for pandemic mitigation policies. In addition, we examine whether such effects were mediated through psychological mechanisms of moral outrage and perceptions of realistic and symbolic threat, and moderated by participants' racial bias. Participants' belief in a just world was included as a covariate given its established role in predicting many related social outcomes. Results suggested that racial framing interacts with participants' racial bias to affect policy support indirectly through multiple mechanisms. Broad implications regarding the relationship between racial bias and public support for a strong social safety net are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Antiviral agents for COVID-19.

In December 2019, the endemic of COVID-19 broke out in Wuhan, China. The disease is highly contagious and quickly spreads at home and abroad, causing great concern. However, there are no definite effective antiviral drugs in clinical use. Given the urgency of the COVID-19 outbreak, based on the diagnosis and treatment recommendation and relavant researches, this article describes the optional antiviral drugs such as remdesivir, oseltamivir, arbidol, lopinavir/ritonavir, ribavirin, and interferon-alpha to provide a reference for treatment of COVID-19.Copyright © 2020 by the Chinese Medical Association.

A review of pooled-sample strategy: Does complexity lead to better performance?

The outbreak of coronavirus disease 2019 (COVID-19, caused by SARS-Cov-2) is a big challenge for global health systems and the economy. Rapid and accurate tests are crucial at early stages of this pandemic. Reverse transcription-quantitative real-time polymerase chain reaction is the current gold standard method for detection of SARS-Cov-2. It is impractical and costly to test individuals in large-scale population screens, especially in low- and middle-income countries due to their shortage of nucleic acid testing reagents and skilled staff. Accordingly, sample pooling, such as for blood screening for syphilis, is now widely applied to COVID-19. In this paper, we survey and review several different pooled-sample testing strategies, based on their group size, prevalence, testing number, and sensitivity, and we discuss their efficiency in terms of reducing cost and saving time while ensuring sensitivity. © 2022 The Authors. VIEW published by Shanghai Fuji Technology Consulting Co., Ltd, authorized by Professional Community of Experimental Medicine, National Association of Health Industry and Enterprise Management (PCEM) and John Wiley & Sons Australia, Ltd.

Farmer Households' Livelihood Resilience in Ethnic Tourism Villages: A Case Study of the Wuling Mountain Area, China

Farmer households in tourist villages have been severely impacted by the COVID-19 pandemic, and the recovery of livelihood is proving difficult. In order to improve farmer households' ability to cope with external shocks, we have applied the theoretical framework of resilience to study farmer households' livelihood in ethnic tourism villages. Based on the survey data of 480 farmer households from 10 ethnic tourism villages in the Wuling Mountain area, this study constructs a livelihood resilience evaluation index system from three of the following dimensions: buffer capacity, adaptive capacity, and transformation capacity. These households are classified into three types: government-led, company-led, and community-led. In addition, the livelihood resilience and its influencing factors of each type is quantitatively assessed. The results show that the livelihood resilience of farmer households administered by the government, companies, and communities is 0.2984, 0.3250, and 0.2442, respectively. Government-led farmer households have the greatest transformation capacity, company-led farmer households have the largest buffer capacity and adaptive capacity, and community-led farmer households have the least capacity across the board. The results indicated that the company-led management of tourism development is currently the most appropriate mode of management for the local area. Four factors, namely, the number of family members engaged in tourism, the training opportunities for the development of professional skills, the education level of core family members, and the type of assistance subsidy available to a family, are the dominant obstacle factors with respect to the livelihood resilience of different types of farmer households. Finally, some recommendations are made to improve the farmer households' livelihood resilience in ethnic tourism villages based on two aspects of organization management and farmer households' behavior. The findings of this study can be used as a theoretical foundation for future research on farmer households' resilience to poverty in underdeveloped ethnic tourism villages. © 2022 by the authors.

Influences of obstacle factors on the transmission trends of respiratory infectious diseases in indoor public places

Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space. © 2022 Elsevier Ltd

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict”approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch”RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases. Author

Exploring the construction of clinical trial research unions in China

A clinical trial is a key step in the process of pharmaceutical research and development (R & D). It is a key indicator of the innovative potential of the pharmaceutical industry. ClinicalTrials.gov shows that the average annual growth rate of the total number of clinical trials globally in the past decade (2012-2021) was 20.7%, mainly distributed in Europe and the United States, and the total number of clinical trials registered in China accounted for 6% of the world. According to the statistics of IQVIA Institute for Human Data Science, the global proportion of early-stage R & D pipelines from China-headquartered companies increased from 1% in 2005 to 12% in 2020, which was still far behind that of European and American companies. Among 871 new active substances approved for marketing globally in the past two decades, 522 were for marketing in China, with the high number driven by regulatory acceleration mechanisms from National Medical Products Administration, such as breakthrough and orphan designations and priority reviews. Considering the gap in clinical research strength between China and developed countries such as Europe and the United States, the clinical trial research capacity and level should be improved to assist China in the R & D of innovative drugs. According to the Registration and Information Disclosure Platform for Drug Clinical Studies and the clinical trial institution filing management information platform in China, in the last five years (2017-2021), the average annual growth rates of the total number of new drug clinical trial registration and clinical trial units in China reached 26.9% and 12.6%, respectively. However, clinical trial resources are mainly concentrated in major institutions, municipalities, or provincial capital cities in the eastern and central regions of China, with distributions becoming increasingly polarised. Under the background of China's new medical reforms, the strategic direction of national political support is to motivate equitable access to high-quality clinical trial resources and cross-regional collaborative development of medical institutions by means of medical unions, national clinical medical research centres, Chinese national major projects for new drug innovation and so on. In the context of this background, clinical trial research unions (CTRUs) have been built in China. A CTRU is defined as a consortium formed by medical institutions, sponsors and third-party service institutions of various levels and functions, led by a national clinical medical research centre or clinical trial medical institutions undertaking major national science and technology projects or supporting projects of national key R & D plans, radiating and driving the improvement of clinical trial research capacity in multiple regions. CTRUs develop a multi-level clinical research centre system and collaborative network by vertically and horizontally combining multi-level medical institutions, sponsors and third-party service institutions. All participants of CTRUs are crucial. The leading clinical trial research medical institution, as the core, is responsible for establishing institutional system standards in CTRUs, designing and leading high-quality multi-centre clinical trials, central ethics and personality training. The major clinical trial research medical institutions are responsible for implementing high-quality, multi-centre, complex and high-risk clinical trials. The other member institutions are responsible for implementing basic clinical trials. The sponsors, contract research organizations (CROs), site management organizations (SMOs) and other enterprises are responsible for funding, supporting and promoting the construction of a clinical trial centre system and collaborative network. The specific construction contents of CTRUs include building a clinical trial research resource sharing platform, homogenising clinical trial quality management, constructing a rapid clinical trial process platform, diversified multi-level and multi-form talent training, information intercon ection, deepening strategic cooperation and designing and leading high-level trials. CTRUs have established the selection criteria and assessment exit mechanism and conducted work performance assessments from the dimensions of organisation and implementation, division of labour and cooperation, the connection of clinical trial resources from top to bottom, efficiency and benefit and sustainable development to ensure their good and sustainable development. Through CTRUs, we can achieve high-quality clinical trial resource sharing, improve the clinical trial research capability of member institutions, cultivate high-level skills, such as principal investigators (PIs), sub-investigators, institutional managers and clinical research coordinators (CRCs), and promote the development of clinical trials, economically and with high-quality. Through the trial operation with one member of CTRUs, it was found that the key points should be strengthened in deepening resource sharing, implementing central ethics review, interconnecting information platforms and leading high-quality clinical trials. The construction of CTRUs is an effective means for China's clinical trial research to solve the current problems and change from a `follow-on pattern' to leading high-level and high-quality international multi-centre trials. However, the construction of CTRUs is a complex systematic project. In addition to performing an excellent job in the top-level design and overall planning, CTRUs' specific implementation process and measures need to be continuously explored in the practice process.

PERCEIVED ROLES IN MEDICATION SAFETY: PATIENTS' PERSPECTIVES

Community-dwelling older adults are vulnerable to medication safety-related harms. Prevention of medication-related harms in the outpatient setting starts with thorough and thoughtful medication reconciliation at each patient encounter. Comprehensive medication reconciliation is challenging for prescribers to provide in busy time-pressured practices. Older adults currently taking five or more daily prescription medications were recruited for this qualitative study. From the participants' perspective, we explored the role of the prescriber, pharmacist, and patient in medication safety. During the COVID-19 pandemic, interviews were conducted from October 2020 to June 2021. Results from these interviews suggest that older adults recognized their role in medication safety supersedes just taking the pills as prescribed. Older adults understand that they must play an essential role in the coproduction of quality health services. Subthemes that emerged from the patient's perceived role were "taking fewer medications,” "locking them up,” "keeping appointments,” and "reading the label.” Pharmacists were expected to inform participants of any changes in their medications, such as the color, shape, or dosage, and ensure no drug interactions. Primary care providers are expected to coordinate care between all specialists treating their patients and any medication prescribed by those specialists. There was a high level of trust in the provider's knowledge, skill, and experience, along with a low level of patient engagement in decision-making around deprescribing. Among older adults, self-perceptions of their role in medication safety varied widely. Educating prescribers and pharmacists about the role expectations of this vulnerable population can help improve medication safety.

Surveillance and Analysis of Canine Respiratory Coronavirus Infection in Beijing

This study aimed to analyse the pathogenic characteristics and the epidemiological situation of canine respiratory coronavirus (CRCoV) in Beijing. From December 2015 to March 2017, Pharynx nasal swabs from 487 dogs were collected and reverse transcriptional polymerase chain reactions was used to detect CRCoV. Meanwhile, some of the samples were also used to detected canine parainfluenza virus (CPIV), canine adenovirus type 2 (CAV-2) and canine distemper virus (CDV) for exploring the situation of mixed infection. The results showed that: 1) The positive rates of CRCoV was 21.36% (104/487). The mixed infection rates of CRCoV and CPIV, CAV-2, CDV were 3.43% (11/321), 0% (0/156) and 3.85% (6/156) respectively. 2) Among all 455 cases which had respiratory symptom records, dogs which had non-respiratory symptom accounting for 27.19% of all dogs without respiratory symptoms, dogs which had mild respiratory symptoms (coughing and nasal discharging) accounting for 19.73% of all dogs with mild respiratory symptoms, dogs which had moderate to severe respiratory symptoms (pneumonia) accounting for 14.28% of all dogs with moderate to severe respiratory symptoms. 3) From November 2016 to March 2017, the mixed infection between CRCoV and CPIV, CAV-2, CDV in 146 cases was explored. The mixed infection rates of the dogs which were tested CRCoV positive and had non-respiratory symptom was 12.50%. The mixed infection rates of the dogs which were tested CRCoV positive and had mild respiratory symptoms was 41.18%. The mixed infection rates of the dogs which were tested CRCoV positive and had moderate to severe respiratory symptoms was 100.00%. 4) The infection rates of CRCoV varied from 15.56% to 22.97% in different age groups among 454 samples. Except for the high positive rates in July, the positive rates in cold season is higher than warm season. These results indicated that the pathogenicity of CRCoV is rather weak. Dogs suffer pure infection of this virus often show non-respiratory symptom or mild respiratory symptoms. Dogs which are CRCoV positive suffer mixed infection often show mild respiratory symptoms or moderate to severe respiratory symptoms. This virus is more likely to be prevalent in winter and spring. The infection rates has no significant relationship with age. Copyright © 2022 Editorial Board, Institute of Animal Science of the Chinese Academy of Agricultural Sciences. All rights reserved.

Identification of virus-specific B-cell epitopes by convalescent plasma from COVID-19 patients.

Identification of immunologic epitopes against SARS-CoV-2 is crucial for the discovery of diagnostic, therapeutic, and preventive targets. In this study, we used a pan-coronavirus peptide microarray to screen for potential B-cell epitopes and validated the results with peptide-based ELISA. Specifically, we identified three linear B-cell epitopes on the SARS-CoV-2 proteome, which were recognized by convalescent plasma from COVID-19 patients. Interestingly, two epitopes (S 809-823 and R1ab 909-923) strongly reacted to convalescent plasma collected at the early phase (< 90 days) of COVID-19 symptom onset, whereas one epitope (M 5-19) reacted to convalescent plasma collected > 90 days after COVID-19 symptom onset. Neutralization assays using antibody depletion with the identified spike (S) peptides revealed that three S epitopes (S 557-571, S 789-803, and S 809-823) elicited neutralizing antibodies in COVID-19 patients. However, the levels of virus-specific antibody targeting S 789-803 only positively correlated with the neutralizing rates at the early phase (<60 days) after disease onset, and the antibody titers diminished quickly with no correlation to the neutralizing activity beyond two months after recovery from COVID-19. Importantly, stimulation of peripheral blood mononuclear cells from COVID-19-recovered patients with these SARS-CoV-2 S peptides resulted in poor virus-specific B cell activation, proliferation, differentiation into memory B cells, and production of immunoglobulin G (IgG) antibodies, despite the B-cells being functionally competent as demonstrated by their response to non-specific stimulation. Taken together, these findings indicate that these newly identified SARS-CoV-2-specific B-cell epitopes can elicit neutralizing antibodies, with titers and/or neutralizing activities declining significantly within 2-3 months in the convalescent plasma of COVID-19 patients.

Sequential Vaccine Allocation with Delayed Feedback

In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies. In particular, we show that with our allocation method, we can reduce the number of required vaccination by at least 50% in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%. We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it reduces uncertainty, hence results in better and more interpretable communication). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

Roles of Transmembrane Serine Protease TMPRSS2 in the Infection of SARS-CoV-2 to Human

Transmembrane serine protease 2 (TMPRSS2) is an androgen-dependent serine protease, and it had previously been reported that it had important pathological functions in tumor metastasis and invasion and virus infection. The entry of coronavirus into host cells is the prerequisite for its transmission and pathogenicity. TMPRSS2 can mediate the invasion of coronavirus into host cells by activating the spike glycoprotein of coronavirus, so it was considered as a potential target for the intervention of coronavirus infection.Current reported effective inhibitors of TMPRSS2 are broad-spectrum drugs targeting the serine protease family, suggesting urgency for exploring and developing novel TMPRSS2-specific inhibitory molecules. The biological characteristics and pathological functions of TMPRSS2 were summarized, with emphasis on the universal function of TMPRSS2 in human pathogenic coronavirus infection and the latest research trends of TMPRSS2 inhibitors in this paper, to highlight the potential of targeting TMPRSS2 as a novel strategy to prevent and limit early infection and transmission of novel coronavirus.

Plasma biomarkers for systemic inflammation in COVID-19 survivors.

BACKGROUND: While the majority of COVID-19 patients fully recover from the infection and become asymptomatic, a significant proportion of COVID-19 survivors experience a broad spectrum of symptoms lasting weeks to months post-infection, a phenomenon termed "post-acute sequelae of COVID-19 (PASC)." The aim of this study is to determine whether inflammatory proteins are dysregulated and can serve as potential biomarkers for systemic inflammation in COVID-19 survivors. METHODS: We determined the levels of inflammatory proteins in plasma from 22 coronavirus disease 2019 (COVID-19) long haulers (COV-LH), 22 COVID-19 asymptomatic survivors (COV-AS), and 22 healthy subjects (HS) using an Olink proteomics assay and assessed the results by a beads-based multiplex immunoassay. RESULTS: Compared to HS, we found that COVID-19 survivors still exhibited systemic inflammation, as evidenced by significant changes in the levels of multiple inflammatory proteins in plasma from both COV-LH and COV-AS. CXCL10 was the only protein that significantly upregulated in COV-LH compared with COV-AS and HS. CONCLUSIONS: Our results indicate that several inflammatory proteins remain aberrantly dysregulated in COVID-19 survivors and CXCL10 might serve as a potential biomarker to typify COV-LH. Further characterization of these signature inflammatory molecules might improve the understanding of the long-term impacts of COVID-19 and provide new targets for the diagnosis and treatment of COVID-19 survivors with PASC.

Privacy Guarantees of BLE Contact Tracing for COVID-19 and Beyond: A Case Study on COVIDWISE

Google and Apple jointly introduced a digital contact tracing technology and an API called "exposure notification,'' to help health organizations and governments with contact tracing. The technology and its interplay with security and privacy constraints require investigation. In this study, we examine and analyze the security, privacy, and reliability of the technology with actual and typical scenarios (and expected typical adversary in mind), and quite realistic use cases. We do it in the context of Virginia's COVIDWISE app. This experimental analysis validates the properties of the system under the above conditions, a result that seems crucial for the peace of mind of the exposure notification technology adopting authorities, and may also help with the system's transparency and overall user trust. © 2022 Owner/Author.