Clinical Characteristics of COVID-19 Reinfection: A Retrospective Study in China (preprint)

Purpose The number of patients with COVID-19 reinfection is gradually increasing.In this study, we aimed to investigate the clinical characteristics of individuals who experienced COVID-19 reinfection.Methods A retrospective data analysis was conducted involving patients diagnosed with COVID-19 between April 1, 2023, and June 20, 2023. The patients were categorized into two groups: the observation group, consisting of individuals with reinfection, and the control group, comprising those with primary infection.Results A Total 905(905/1025) patients were included in the study,with 407 in the observation group and 498 in the control group. The top three clinical symptoms in both groups were fever, cough with expectoration, and dizziness with fatigue (p < 0.001). The clinical classification of patients in the observation group primarily consisted of non-severe cases, (p<0.001). The proportion of hospitalized patients was lower in the observation group than in the control group (p < 0.001). The observation group exhibited a shorter clinical symptom recovery time than that of the control group (median, 5 d vs. 7 d,p < 0.001).Conclusion Patients experiencing COVID-19 reinfection were primarily classified as non-severe cases, with lower proportions of occurrence of severe and rare critical conditions. The severity was milder compared to that in patients with primary COVID-19 infections.

Identification and analysis of co-disease genes between COVID-19 and osteoarthritis (preprint)

Background: The molecular mechanisms of corona virus disease 2019 (COVID-19) and osteoarthritis (OA) are unclear, and there is an urgent need to identify new biomarkers and explore their potential molecular mechanisms in COVID-19 and OA. Methods: The GSE57218, GSE157103 training sets and the GSE82107, GSE171110 validation sets were acquired via gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between disease and normal samples in the GSE57218 and GSE157103 training sets were respectively sifted out by differential expression analysis. The modules with the highest correlation with OA and normal, COVID-19 and non COVID-19 were gained by weighted gene co-expression network analysis (WGCNA), individually. Then, OA-DEGs were intersected with the module genes that had significant correlation with OA, and COVID-19-DEGs were intersected with the module genes which were dramatically correlated with COVID-19 to yield OA-intersected genes and COVID-19 intersected genes, respectively. The OA-intersected genes and COVID-19 intersected genes were intersected to yield candidate genes, and they were analyzed for function enrichment analysis. Next, the seven algorithms (Closeness, MCC, Degree, MNC, Radality, Stress and EPC) were performed on candidate genes to sift out biomarkers. Finally, we constructed the competing endogenous RNA (ceRNA), transcription factor (TF)/miRNA-mRNA and drug-target regulatory networks. Results:There were 1135 OA-DEGs and 4336 COVID-19-DEGs between disease and normal samples in the GSE57218 and GSE157103 training sets, respectively. The pink, blue and brown modules had significant correlations with OA in the GSE57218 training set, while in the GSE157103 training set, the pink and brown modules were notably correlated with COVID-19. We finally yield 715 OA-intersected genes and 2282 COVID-19-intersected genes. After intersecting the above two intersected genes, we gained 106 candidate genes, and they were involved in ADP metabolic process, nucleoside diphosphate phosphorylation, etc.. The 7 biomarkers, namely AK1, APP, ENO1, TPI1, HSP90B1, HSPB1 and ESR1, were acquired based on seven algorithms. Finally, we successfully constructed the ceRNA, TF/miRNA-mRNA and drug-target networks. Conclusion: Through bioinformatic methods, we explored the biomarkers (AK1, APP, ENO1, TPI1, HSP90B1, HSPB1 and ESR1) of COVID-19 combined OA, providing new ideas for studies related to molecular mechanisms and treatment of comorbidity.

Bridging the research-practice gap in supply chain risks induced by the COVID-19

PurposeThis study aims to explore the gap between research and practice on supply chain risks due to COVID-19 by exploring the changes in global emphasis on supply chain risk research.Design/methodology/approachThis work designed a research framework to compare the research of supply chain risks before and during the COVID-19 pandemic based on machining learning and text clustering and using the relevant publications of the web of science database.FindingsThe results show that scholars' attention to supply chain crisis has increased in the wake of the COVID-19 outbreak, but there are differences among countries. The United Kingdom, India, Australia, the USA and Italy have greatly increased their emphasis on risk research, while the supply chain risk research growth rate in other countries, including China, has been lower than the global level. Compared with the pre-pandemic period, the research of business finance, telecommunications, agricultural economics policy, business and public environmental occupational health increased significantly during the pandemic. The hotspots of supply chain risk research have changed significantly during the pandemic, focusing on routing problem, organizational performance, food supply chain, dual-channel supply chain, resilient supplier selection, medical service and machine learning.Research limitations/implicationsThis study has limitations in using a single database.Social implicationsThis work compared the changes in global and various countries' supply chain risk research before and during the pandemic. On the one hand, it helps to judge the degree of response of scholars to the global supply chain risk brought about by COVID-19. On the other hand, it is beneficial for supply chain practitioners and policymakers to gain an in-depth understanding of the relationship between the COVID-19 pandemic and supply chain risk, which might provide insights into not only addressing the supply chain risk but also the recovery of the supply chain.Originality/valueThe initial exploration of the changing extent of supply chain risk research in the context of COVID-19 provided in this paper is a unique and earlier attempt that extends the findings of the existing literature. Secondly, this research provides a feasible analysis strategy for supply chain risk research, which provides a direction and paradigm for exploring more effective supply chain research to meet the challenges of COVID-19.

Controlling effect of TiO2 carrier on formed vanadium species for effective catalytic oxidization of chlorobenzene

Catalytic oxidization of the dioxins is critical to the atmosphere since medical-waste incineration is globally increased due to COVID-19. Vanadium supported TiO2 is the most-widely investigated catalyst. However, previous reports usually investigated V-Ti relationships after vanadate was loaded on TiO2 and an activation was completed. In comparison, this work emphasizes on the influence of TiO2 surface chemistries on properties of formed vanadium species during a thermal activation. The vanadate loaded TiO2 are detailedly analyzed by both experimental characterizations and theoretical calculations. As a result, TiO2 with more Ti4+ is inclined to be reductive, and promotes the formation of more low-valence vanadium and surface vacancies. On the contrary, TiO2 with more Ti3+ is inclined to be oxidative, and promotes the formation of more lattice oxygens and high-valence vanadium. When they were compared in catalytic oxidization of chlorobenzene, the Ti4+-induced catalyst attains a conversion of 98.0 % at 250 °C, far bigger than that (11.3 %) of the Ti3+-induced catalyst. The main result of this work helps readers to understand the process of the catalyst preparation, which is in favor of producing a more effective catalyst by regulating the catalyst carrier.

Does the COVID-19 pandemic derail US-China collaboration on carbon neutrality research? A survey

The COVID-19 pandemic has seriously impacted scientific research activities, especially international cooperation in scientific research. Using bibliometric methods and scientific knowledge graph software, and by calculating collaboration indicators such as international collaboration rates, this work conducts a comprehensive review of carbon neutrality publications in the Web of Science database before and during the COVID-19 pandemic, aiming to explore whether the COVID-19 pandemic derail China-U.S. collaboration on carbon neutrality research. The results show that (i) During the COVID-19 pandemic, more extensive research on carbon neutrality was carried out around the world, with China and the United States leading the way in carbon neutrality scientific output. (ii) Following the outbreak of the COVID-19, the global center of global carbon neutrality shifted from the United States to China. (iii) During the COVID-19 pandemic, research ties between China and the United States strengthened. The number of joint publications on carbon neutrality between China and the United States has greatly increased during the COVID-19 pandemic compared to those before. (iv) The proportion of China-U.S. cooperation in China's international cooperation has decreased, while it is the opposite for the United States. At the end of the article, we put forward relevant suggestions for realizing the sustainable development goals of climate change in the post-epidemic era for policymakers' reference. This paper provides offers important insights into the theoretical research of scholars in the field of carbon neutrality.

SARS-CoV-2 Omicron Variants Reduce Antibody Neutralization and Acquire Usage of Mouse ACE2

Striking number of mutations found in the spike protein of recently emerged SARS-CoV-2 Omicron subvariants BA.1, BA.2, BA.3 and BA.4/5 has raised serious concerns regarding the escape from current antibody therapies and vaccine protection. Here, we conducted comprehensive analysis on the extent of two major Omicron lineages BA.1/BA.1.1 and BA.2 to escape neutralization from the therapeutic antibodies approved by the regulatory authorities and convalescent plasma from SARS-CoV-2 patients infected during initial wave of pandemic in early 2020. We showed that Omicron BA.1/BA.1.1 were the most resistant in both magnitude and breadth against antibodies and convalescent plasma, followed by Beta, BA.2, Gamma, Delta and Alpha. While the majority of therapeutic antibodies lost binding and neutralization to Omicron variants, BRII combo (BRII-196 + BRII-198), S309, and AZ combo (COV2-2196 + COV2-2130) maintained neutralization despite of reduction due to either conserved epitope or combinational effect between the two designated antibodies. A single intraperitoneal injection of BRII combo as a prophylactic treatment protected animals from Omicron infection. Treated animals manifested normal body weight, survived infection up to 14 days, undetectable levels of infectious viruses in the lungs, and reduced lung pathology compared to the controls. Analyzing ACE2 from diverse host species showed that Omicron variants acquired ability to use mouse ACE2 for entry. These results demonstrate major antigenic shifts and potentially broadening the host range of two major Omicron lineages BA.1/BA.1.1 and BA.2, posing serious challenges to current antibody therapies and vaccine protection as well as increasing danger of spillover into the wildlife.

Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate (preprint)

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM) between 2.5 $\mu$m and 10 $\mu$m aerodynamic diameter, as inferred by the difference between PM10 and PM2.5 observations. This coarse PM averages 47 $\mu$g m$^{-3}$ in the North China Plain (NCP) region in 2015-2019 and 21 $\mu$g m$^{-3}$ in the Seoul Metropolitan Area (SMA). It is dominantly contributed by urban fugitive dust, rather than by natural dust as is often presumed. Concentrations decreased by 21% from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. This anthropogenic coarse PM is generally not included in air quality models but acts as a sink of nitric acid (HNO3), thus affecting fine particulate nitrate which is a major air quality concern in China and South Korea. GEOS-Chem model simulation of surface and aircraft observations from the KORUS-AQ campaign over the SMA in May-June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of PM1 nitrate. Anthropogenic coarse PM in the model increases the sensitivity of PM2.5 nitrate to ammonia (NH3) emission in winter. In summer, anthropogenic coarse PM directly affects PM2.5 nitrate by HNO3 uptake, and we find that the decrease of anthropogenic coarse PM over 2015-2019 offset the PM2.5 nitrate reductions expected from decreasing NOx and NH3 emissions.

Wearable Aiegens-Based Lateral Flow Test Strip for Rapid Detection of SARS-CoV-2 N Protein and BRD Protein (preprint)

\Accurate and rapid detection of SARS-CoV-2 is significant for early tracing, isolating and treating the infected patients, which will efficiently prevent the virus large-scale spread from human to human. In this paper, two kinds of novel quantitative lateral flow test strip for N and RBD antigens of SARS-CoV-2 were established with high sensitivity, which utilize AIE luminogens (AIEgens) as reporter. Because of the high brightness and resistance of quenching property in aqueous of the AIEgens, the limit of detection of 7.2 ng/mL for N protein and 6.9 ng/mL for RBD protein could be achieved with the AIEgens-based lateral flow test strip. Furthermore, it was negative for other protein or antigen samples assay, which demonstrated the great specificity of the test strategy. A N95 mask equipped with the test strip was designed to employ as the antigen collector with excellent enrichment effect. Compared with the other two test strips based on the Au nanoparticle and FITC, the well-designed AIEgens-based lateral flow test strip presented high sensitivity and excellent anti-interference capacity in complex bio-samples. Furthermore, the AIEgens-based lateral flow test strip assay could be built as a promising platform for the emergency usage at pandemic.Funding: This work was supported by the NSFC (51961160730, 51873092, and 81921004), the National Key R&D Program of China (Intergovernmental Cooperation Project, 2017YFE0132200), the Fundamental Research Funds for the Central Universities, and the Tianjin Science Fund for Distinguished Young Scholars (19JCJQJC61200).Declaration of Interests: The authors declare no competing interests.

Experience for Glaucoma Patients During the COVID-19 Outbreak (preprint)

Purpose: To reviewed our measures and other experiences associated with the treatment of glaucoma patients during the 2019 novel coronavirus disease (COVID-19) outbreak . Methods: : Measures consistent with the clinical situation and the type of glaucoma were implemented in each case. Results: : Consultation for treatment was provided through a WeChat glaucoma patient group. In our survey, 58.5%patients indicated an increase in negative emotions during the outbreak, and psychological counseling was provided frequently. Difficulties in purchasing eye drops were reported by 79.2% of glaucoma patients, so an online medicine supply system was set up and eyedrops from the pharmacy or hospital pharmacy were mailed to the patients. Phacoemulsification with goniosynechialysis was performed in7acute primary angle-closure glaucomapatients (11eyes) who experienced sustained high IOP(onset time, 12-65d). The IOP decreased significantly after the operation(t=7.895，P<0.01) without showing serious complications or infections. In addition, lectureson glaucoma prevention were promoted online. Conclusion: Effective measures can relieve thepsychological pressure and intraocular symptoms in glaucoma patientsin epidemic outbreak areas.

Prediction of Second Wave of COVID-19 in India using the Modified SEIR Model (preprint)

Background: The second wave of the coronavirus disease 2019 (COVID-19) epidemic in India was caused by the COVID-19 Delta variant. However, the epidemiological characteristics and transmission mechanism of the Delta variant remain unclear. To explore whether the epidemic trend will change after effective isolation measures were taken and what is the minimum number of individuals who need to be vaccinated to end the epidemic. Methods: We used actual data from March 5 to April 15, 2021, of daily updates confirmed cases and deaths, to estimate the parameters of the model and predict the severity of possible infection in the coming months. The classical Susceptible-Exposed-Infected-Removed (SEIR) model and extended models [Susceptible-Exposed-Infected-Removed-Quarantine (SERIQ) model and Susceptible-Exposed-Infected-Removed- medicine (SERIM) model] were developed to simulate the development of epidemic under the circumstances of without any measures, after effective isolation measures were taken and after being fully vaccinated. Results: The result demonstrated good accuracy of the classic model. The SEIRQ model showed that after isolation measures were taken, the infections will decrease by 99.61% compared to the actual number of infections by April 15. And the SEIRQ model demonstrated that if the vaccine efficative rate was 90%, when the vaccination rate was 100%, the number of existing cases would reach a peak of 529,723 cases on the 52nd day. Conclusion: Effective quarantine measures and COVID-19 vaccination from official are critical prevention measures to help end the COVID-19 pandemic.

The study on the anxiety level of college students in emergencies based on fuzzy rough set over two universes

In 2020, the spread of the COVID-19 epidemic has attracted global attention. As a large-scale group that is receiving higher education, college students also show greater mood swings. How to reduce the psychological harm of anxiety to college students is a problem that needs to be solved urgently. Based on this, this paper proposes an evaluation model for the anxiety level of college students in different regions under the influence of COVID-19. First of all, the general influence index of college student’s anxiety level is obtained by correlation analysis. Secondly, the logical OR of the double quantization variable precision fuzzy set model and the degree fuzzy rough set model is used to establish the evaluation model of the anxiety level of college students under the influence of COVID-19. Finally, used big data, the idea of fuzzy upper and lower approximation, combined with the principle of maximum membership in fuzzy set theory, achieved the quantitative ranking of the anxiety levels of college students in different areas. The research shows that when the accuracy of decision-making is 45%, the anxiety level of the township college students group and the provincial capital or municipality college students group is higher. When the accuracy of decision-making is 65%, the anxiety level of the provincial capital or municipality college students group is higher than others. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A Graphical Pressure Sensor Array With Multilayered Structure: Written Through Graphene Ink on Cellulose Paper Substrate (preprint)

Paper based flexible pressure sensors have received extensive attention due to their recoverability and accessibility. In this paper, we proposed graphical pressure sensors array with multilayered structure. A simple writing method was adopted to achieve the adsorption of sensitive materials on the fiber structure of cellulose paper. Pressure sensors with 1, 3, 5 and 7 stacked layers were fabricated and compared, respectively. The results show that the 7layers sensor combined high sensitivity (44 kPa-1) and fast time response (150 ms). The highly sensitive stacked paper-based sensor array realizes the pressure detection of objects and special-shaped surfaces. A pressure sensor base on commercial corrugated box was also fabricated to compare. The corrugated carton array was made to switch reminder devices for its convenience and accessibility. Since there are many scenarios that require a safe distance, especially under the influence of the COVID-19, the writable paper-based sensor array was used to realize graphical distance perception and warning.

Global Gridded Daily CO2 Emissions (preprint)

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance of achieving the carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emission Datasets (called GRACED) from fossil fuel and cement production with a global spatial-resolution of 0.1{\deg} by 0.1{\deg} and a temporal-resolution of 1-day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near real time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Carbon Grid (GID), Emission Database for Global Atmospheric Research (EDGAR) and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors between 2019 and 2020. This help us to give insights on the relative contributions of various sectors and provides a fast and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g. COVID-19) and other disturbances of human activities than any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will allow policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt

Value of medical imaging artificial intelligence in the diagnosis and treatment of new coronavirus pneumonia

Abstract The development of computer technology is becoming more and more mature. The application of artificial intelligence to the medical field has made important contributions to medical diagnosis and auxiliary detection. Using the powerful computing power of computers to replace humans in the automatic diagnosis of complex diseases has been enthusiastic by the majority of scientific researchers. In the current medical imaging diagnosis system, the location of the lesion is mainly found by observing the two-dimensional medical image sequence, and the signs of biological information cannot be accurately displayed. Through medical image analysis technology, two-dimensional images will be divided into three-dimensional models after image segmentation, image recognition, and three-dimensional imaging. In this way, it seems that the doctor can ?hold the image volume data,? which can greatly improve the scientific and accurate diagnosis. Sex?this article applies artificial intelligence to medical imaging, combined with embedded technology, RFID technology and signal processing technology, and applies the new coronavirus pneumonia image to the artificial intelligence environment after processing, assisting doctors in diagnosis of the disease, and providing relevant information about patients record and manage the diagnosis and diagnosis, save and accumulate the experience and knowledge of famous doctors through the expert system, and then perform corresponding operations and analysis. Through the medical image intelligent analysis system, the safety risk of medical imaging artificial intelligence diagnosis is reduced from 81% to 11%, which greatly reduces the hidden safety hazards for doctors and patients, reduces the workload of doctors, and also reduces the cost of medical care by 79% It is reduced to 20%, which reduces the waiting time of patients and achieves the purpose of improving the accuracy of diagnosis.

Quantifying Effects of Non-Pharmacological Interventions on COVID-19 Transmission With Modeling (preprint)

Background: The initial outbreak of coronavirus disease 2019 (COVID-19) pandemic has rapidly extended globally, which brought huge detrimental to the whole society. While some countries and regions are currently experiencing another outbreak of COVID-19.Methods: In this study, by using the epidemic data from January 2 to February 11, 2021 in city Shijiazhuang, an extended SEIR model was established to evaluate the effectiveness of emergency response, nucleic acid testing (NAT) and stay-at-home order for all individuals, and to simulate the impact of delayed interventions.Findings: We estimated an initial effective reproduction number (Rt) was 4·70, and the Rt value gradually decreased with the implementation of government interventions. Meanwhile, the final cumulative number of confirmed cases decreased by 99·99% (898 cases), and peak of current confirmed cases decreased by 99·99% (763 cases) compared with that without intervention. With fewer interventions, the number of infected individuals will continue to increase. Delayed interventions were simulated which would show at least 80% (1614 cases), 218% (2854 cases), 464% (5061 cases), and 879% (8787 cases) more infections can be attained for Shijiazhuang city, if the intervention was delayed by 2, 4, 6, and 8 days, respectively.Interpretation: The Shijiazhuang government has effectively controlled the spread of the epidemic by adopting a series of comprehensive non-pharmacological interventions in time.Funding Information: The study was supported by grants from the National Social Science Foundation of China (20BRK041).Declaration of Interests: The authors declare that they have no competing interests.

Toll-Like Receptor Agonist R848 Protects Mice from Lethal SARS-CoV-2 Infections (preprint)

Background: An ideal animal model to study SARS-coronavirus 2 (SARS-CoV-2) pathogenesis and evaluate therapies and vaccines should reproduce SARS-CoV-2 infection and recapitulate lung disease like those seen in humans. The angiotensin-converting enzyme 2 (ACE2) is a functional receptor for SARS-CoV-2, but mice are resistant to the infection because their ACE2 is incompatible with the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Methods: We generated a mouse-adapted strain SARS-CoV-2 by serial passages in the lung of BALB/c mice. Complete genome deep sequencing of different generations of viruses was performed to characterize the dynamics of the adaptive mutations in SARS-CoV-2. Indirect immunofluorescence analysis and Biolayer interferometry experiments demonstrated that two mutations in RBD significantly increased its binding affinity towards mouse ACE2. Significantly, TLR7/8 agonist Resiquimod block SARS-CoV-2 in vitro and in vivo. Findings: We adapted a wild-type SARS-CoV-2 by serial passages in the lung of BALB/c mice. The mouse-adapted strain WBP-1 showed increased infectivity in BALB/c mice and led to severe interstitial pneumonia. We characterized the dynamics of the adaptive mutations in SARS-CoV-2 and demonstrated that Q493K and Q498H in RBD significantly increased its binding affinity towards mouse ACE2. Additionally, The TLR7/8 agonist Resiquimod was able to protect mice against WBP-1 challenge, demonstrating this mouse-adapted strain is a useful tool to investigate COVID-19 and develop new therapies. Interpretation: We found for the first time that the Q493K and Q498H mutations in the RBD of WBP-1 enhanced its interactive affinities with mACE2. The mouse-adapted SARS-CoV-2 provides a valuable tool for the evaluation of novel antiviral and vaccine strategies, especially in determining the immunopathological consequences of any intervention. This study also verified the antiviral activity of TLR7/8 agonist Resiquimod against SARS-CoV-2 in vitro and in vivo.Funding Statement: This research was funded by Emergency Science and Technology Project of Hubei Province（2020FCA046）and Independent Science and Technology Innovation Fund of Huazhong Agricultural University in 2020 (2662020PY002).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The animal experiments were approved by the Research Ethics Committee, Huazhong Agricultural University, Hubei, China (HZAUMO-2020-0007). All the animal experiments were conducted in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals from the Research Ethics Committee, Huazhong Agricultural University, Hubei, China.

Kinetic changes in virology, specific antibody response and imaging during the clinical course of COVID-19: A descriptive study (preprint)

Background: To explore the kinetic changes in virology, specific antibody response and imaging during the clinical course of COVID-19. Methods: : This observational study enrolled 20 patients with COVID-19, who were hospitalized between January 20-April 6, 2020, in the two COVID-19 designated hospitals of Zhoushan, Zhejiang and Rushan, Shandong, China, The laboratory findings, imaging, serum response to viral infection, and viral RNA level in the throat and stool samples were assessed from onset to recovery phase in patients with COVID-19. Results: : SARS-COV-2 RNA was positive as early as day four. It remained positive until day 55 post-onset in the sputum-throat swabs and became negative in most cases (55%) within 14 days after onset. Lymphocytopenia occurred in 40% (8/20) of patients during the peak infection period and returned to normal at week five. The most severe inflammation in the lungs appeared in week 2 or 3 after onset, and this was completely absorbed between week 6 and 8 in 85.7% of patients. All patients had detectable antibodies to the receptor binding domain (RBD), and 95% of these patients had IgG to viral N proteins. The antibody titer peaked at week four. Anti-S IgM was positive in 7 of 20 patients after week three. Conclusions: : All COVID-19 patients in this study were self-limiting and recovered well though it may take as long as 6-8 weeks. Our findings on the kinetic changes in imaging, serum response to viral infection and viral RNA level may help understand pathogenesis and define clinical course of COVID-19.

Dynamic changes of liver function parameters in patients with coronavirus disease 2019: A multicenter, retrospective study (preprint)

Background: Liver injuries in patients with coronavirus disease 2019 (COVID-19) have been reported, however, the clinical role played by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is obscure.  Methods: In this multicenter, retrospective study, the parameters of liver function tests in COVID-19 inpatients were compared between various timepoints referred to SARS-CoV-2 shedding, and 3 to 7 days before first detection of viral shedding was regarded as reference baseline.Results: Totally, 70 COVID-19 inpatients were enrolled. Twenty-two (31.4%) cases had self-medications history after illness. At baseline, 10 (14.3%), 7 (10%), 9 (12.9%), 2 (2.9%), 15 (21.4%), and 4 (5.7%) patients already had abnormal rates of alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), albumin, and total bilirubin (TBIL), respectively. ALT and AST abnormal rates and levels did not show any significantly dynamic change during the full period of viral shedding (all P > 0.05). GGT abnormal rate (P = 0.008) and level (P = 0.033) significantly increased on day 10 of viral shedding. Meanwhile, no simultaneously significant increases of ALP abnormal rates and levels were observed. TBIL abnormal rates and levels significantly increased on day 1 and 5 of viral shedding (all P < 0.05). Albumin abnormal decrease rates increased and levels decreased consistently from baseline to SARS-CoV-2 clearance day (all P < 0.05). Thirteen (18.6%) patients had chronic liver diseases, two of them died. The ALT and AST abnormal rates and levels did not increase in patients with chronic liver diseases during SARS-CoV-2 shedding.Conclusions: The SARS-CoV-2 does not directly lead to elevations of ALT and AST, but may result in elevations of GGT and TBIL, the albumin decreased extraordinarily even SARS-CoV-2 shedding discontinued.