Clinical Course and Risk Factors for Liver Injury of Severe and Critical Patients with COVID-19.

Introduction: Information regarding the clinical course of COVID-19 patients with liver injury is very limited, especially in severe and critical patients. The objective of this study was to describe the characteristics and clinical course of liver function in patients admitted with severe and/or critical SARS-CoV-2 infection, as well as explore the risk factors that affect liver function in the enrolled COVID-19 patients. Methods: Information on clinical characteristics of 63 severe and critical patients with confirmed COVID-19 was collected. Data on patients' demographics, laboratory characteristics, laboratory examination, SARS-CoV-2 RNA results and liver test parameters were acquired and analyzed. Results: The incidence of abnormal aspartate aminotransferase, alanine aminotransferase, and total bilirubin in the critical group was significantly higher than in the severe group (respectively 81.48%, 81.49%, 62.67%, and 45.71%, 63.88%, 22.86%, p < 0.05). The time for liver function parameters to reach their extremes was approximately 2-3 weeks after admission. The independent factors associated with liver injury were patients with invasive ventilators, decreased percentages of neutrophils, lymphocytes and monocytes, and sequential organ failure assessment (SOFA) score ≥2 (p < 0.05). Conclusion: Abnormal liver tests are commonly observed in severe and critical patients with COVID-19. Severe patients infected by SARS-CoV-2 should be closely observed and monitored the liver function parameters, particularly when they present with independent risk factors for liver injury.

Amomum tsao-ko Crevost & Lemarié: a comprehensive review on traditional uses, botany, phytochemistry, and pharmacology.

Tsaoko Fructus, the dried ripe fruit of Amomum tsao-ko Crevost & Lemarié, is used as both medicinal material and food additive. This review summarized the traditional uses, botany, phytochemistry, and pharmacological progress on Tsaoko Fructus. One classical prescription and the other 11 representative prescriptions containing Tsaoko Fructus were reviewed. The indications of these prescriptions are major in treating spleen and stomach disorders and epidemic febrile diseases including malaria. At least 209 compounds have been isolated and identified from Tsaoko Fructus, most of which belong to terpenoids, phenylpropanoids, and organic acids. Essential oil, crude extract, and some compounds were observed to have pharmacological activities such as anti-biotics, anti-inflammation, antioxidant, mostly via in vitro experiments. However, the mechanism of its medicinal uses remains unclear. This review provides a comprehensive understanding of Tsaoko Fructus, which will be beneficial to exploring the mechanism and potential medicinal applications of Tsaoko Fructus, as well as developing a rational quality control system for Tsaoko Fructus as a medicinal material in the future. Supplementary Information: The online version contains supplementary material available at 10.1007/s11101-021-09793-x.

Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection.

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users' location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users' location information.

Molecular Detection of Parvovirus in Captive Siberian Tigers and Lions in Northeastern China From 2019 to 2021.

The fact that wild felines are carriers of pernicious infectious viruses should be a major concern due to the potential cross-species transmission between the felines and human or domestic animals. However, studies on the virus in the captive wild felines, especially in tigers, are thin on the ground. In this study, we screened four infectious viruses, namely, feline parvovirus (FPV), feline coronavirus (FCoV), canine distemper virus (CDV), and influenza A virus (IAV), in the blood samples of 285 captive Siberian tigers (Panthera tigris altaica) and in the spleen samples of two deceased lions (Panthera leo), which were collected from 2019 to 2021 in three Siberian Tiger Parks from the northeast of China. Nucleic acids isolated from the blood samples collected from tigers and the spleen samples collected from two deceased lions were positive for FPV by PCR, and the positive rate was 4.6% (13/285) in tigers. Furthermore, the VP2 gene of FPV was amplified by nested PCR, and the sequences of the VP2 gene from these six FPV positive strains shared 98.3-99.9% homology with the reference. The key amino acid sites of VP2 protein were consistent with that of FPV reference strains. Phylogenetic analysis based on the VP2 gene showed that in this study, FPV-positive strains were grouped within the FPV clade and closely related to the Asian strains clade. The results of this study showed that FPV circulated in the captive Siberian tigers and lions in northeastern China and provided valuable information for the study of FPV epidemiology in wild felines. Therefore, we suggest that regular antibody monitoring and booster immunization for tigers should be performed.

Factors Associated with SARS-CoV-2 Repeat Positivity - Beijing, China, June-September 2020.

INTRODUCTION: Repeat positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) following COVID-19 initial viral clearance (re-positivity) poses a public health management challenge. The objective was to determine factors associated with neutralizing antibody (Nab) level and re-positivity among patients infected with a single strain SARS-CoV-2. METHODS: During a single strain SARS-CoV-2 cluster in Beijing, China, longitudinal individual clinical, virological, and immunological data were collected from 368 infections from June 13 to September 22, 2020. Factors associated with Nab level and re-positivity were analyzed using generalized estimating equations. RESULTS: A total of 353 (96%) SARS-CoV-2 infections had demographic, clinical, and laboratory data available. Among the 353 infections, 55 (15.5%) were re-positive, and blood draws were taken from 346 individuals (98.0%) during hospitalization and/or during the follow-up period. Symptoms were milder for the second-time admission for the re-positives, although 36.4% of re-positives presented with radiographic appearance of pneumonia manifestation. Compared to non-re-positive patients, NAb titers were lower among re-positives; NAb was positively associated with clinical severity. Samples from the lower respiratory tract manifested higher viral load than that from the upper respiratory tract. Multivariable analysis showed re-positivity was positively associated with being female [odd ratio (OR)=1.7, 95% confidence interval (CI) 1.1-2.8] and being aged <18 years (OR=5.2, 95% CI 1.5-18.1); having initially asymptomatic infection (OR=13.7, 95% CI 1.6-116.3); and negatively associated with a higher NAb level (OR=0.9, 95% CI 0.5-1.7). CONCLUSIONS: NAb may be important for sustained viral clearance. Lower respiratory tract infection was associated with higher viral load among all infections when compared to upper respiratory tract infection. Continuous lower respiratory and intermittent upper respiratory viral shedding among COVID-19 infections may occur.

The characteristics of overseas imported COVID-19 cases and the effectiveness of screening strategies in Beijing, China.

BACKGROUND: In March 2020, the WHO declared the novel coronavirus outbreak a global pandemic. While great success in coronavirus disease 2019 (COVID-19) control has been achieved in China, imported cases have become a major challenge. This study aimed to describe the epidemiological and clinical characteristics of imported COVID-19 cases and to assess the effectiveness of screening strategies in Beijing, China. METHODS: This retrospective study included all imported cases transferred to Beijing Ditan Hospital from 29 February to 20 March 2020 who were screened by both chest computed tomography (CT) and reverse-transcriptase-polymerase chain reaction (RT-PCR) at the initial presentation. Demographic, clinical and laboratory data, in addition to chest CT imaging, were collected and analysed. RESULTS: In total, 2545 cases were included, among which 71 (2.8%) were finally diagnosed with laboratory-confirmed COVID-19. The majority 63 (88.7%) were from Europe. The most common initial symptoms were cough and fever, which accounted for 49.3% and 42.3%, respectively. Only four cases (5.6%) had lymphocytopenia, and thirteen cases (18.3%) demonstrated elevated levels of C-reactive protein (CRP). All cases had normal serum levels of procalcitonin (PCT). At initial presentation, among the 71 confirmed cases, 59 (83.1%) had a positive RT-PCR assay, and 35 (49.3%) had a positive chest CT. Twelve (16.9%) had a negative RT-PCR assay but a positive chest CT. CONCLUSIONS: A combination of RT-PCR and chest CT is an effective strategy for the screening of imported COVID-19 cases. Our findings provide important information and clinical evidence about the infection control of imported COVID-19 cases.

Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19.

Exosomes represent a subtype of extracellular vesicle that is released through retrograde transport and fusion of multivesicular bodies with the plasma membrane1. Although no perfect methodologies currently exist for the high-throughput, unbiased isolation of pure plasma exosomes2,3, investigation of exosome-enriched plasma fractions of extracellular vesicles can confer a glimpse into the endocytic pathway on a systems level. Here we conduct high-coverage lipidomics with an emphasis on sterols and oxysterols, and proteomic analyses of exosome-enriched extracellular vesicles (EVs hereafter) from patients at different temporal stages of COVID-19, including the presymptomatic, hyperinflammatory, resolution and convalescent phases. Our study highlights dysregulated raft lipid metabolism that underlies changes in EV lipid membrane anisotropy that alter the exosomal localization of presenilin-1 (PS-1) in the hyperinflammatory phase. We also show in vitro that EVs from different temporal phases trigger distinct metabolic and transcriptional responses in recipient cells, including in alveolar epithelial cells, which denote the primary site of infection, and liver hepatocytes, which represent a distal secondary site. In comparison to the hyperinflammatory phase, EVs from the resolution phase induce opposing effects on eukaryotic translation and Notch signalling. Our results provide insights into cellular lipid metabolism and inter-tissue crosstalk at different stages of COVID-19 and are a resource to increase our understanding of metabolic dysregulation in COVID-19.

Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19).

BACKGROUND: The emergence of coronavirus disease 2019 (COVID-19) is a major healthcare threat. The current method of detection involves a quantitative polymerase chain reaction (qPCR)-based technique, which identifies the viral nucleic acids when present in sufficient quantity. False-negative results can be achieved and failure to quarantine the infected patient would be a major setback in containing the viral transmission. We aim to describe the time kinetics of various antibodies produced against the 2019 novel coronavirus (SARS-CoV-2) and evaluate the potential of antibody testing to diagnose COVID-19. METHODS: The host humoral response against SARS-CoV-2, including IgA, IgM, and IgG response, was examined by using an ELISA-based assay on the recombinant viral nucleocapsid protein. 208 plasma samples were collected from 82 confirmed and 58 probable cases (qPCR negative but with typical manifestation). The diagnostic value of IgM was evaluated in this cohort. RESULTS: The median duration of IgM and IgA antibody detection was 5 (IQR, 3-6) days, while IgG was detected 14 (IQR, 10-18) days after symptom onset, with a positive rate of 85.4%, 92.7%, and 77.9%, respectively. In confirmed and probable cases, the positive rates of IgM antibodies were 75.6% and 93.1%, respectively. The detection efficiency by IgM ELISA is higher than that of qPCR after 5.5 days of symptom onset. The positive detection rate is significantly increased (98.6%) when combining IgM ELISA assay with PCR for each patient compared with a single qPCR test (51.9%). CONCLUSIONS: The humoral response to SARS-CoV-2 can aid in the diagnosis of COVID-19, including subclinical cases.

Bacterial and fungal co-infections among COVID-19 patients in intensive care unit.

This study aimed to investigate the frequency and characteristics of respiratory co-infections in COVID-19 patients in the intensive care unit (ICU). In this retrospective observational study, pathogens responsible for potential co-infections were detected by the bacterial culture, real-time polymerase chain reaction (RT-PCR), or serological fungal antigen tests. Demographic and clinical characteristics, as well as microbial results, were analyzed. Bacterial culture identified 56 (58.3%) positive samples for respiratory pathogens, with the most common bacteria being Burkholderia cepacia (18, 18.8%). RT-PCR detected 38 (76.0%) and 58 (87.9%) positive results in the severe and critical groups, respectively. Most common pathogens detected were Stenotrophomonas maltophilia (28.0%) and Pseudomonas aeruginosa (28.0%) in the severe group and S. maltophilia (45.5%) in the critical group. P. aeruginosa was detected more during the early stage after ICU admission. Acinetobacter baumannii and Staphylococcus aureus were more frequently identified during late ICU admission. Fungal serum antigens were more frequently positive in the critical group than in the severe group, and the positive rate of fungal serum antigens frequency increased with prolonged ICU stay. A high frequency of respiratory co-infections presented in ICU COVID-19 patients. Careful examinations and necessary tests should be performed to exclude these co-infections.

The kinetics of humoral response and its relationship with the disease severity in COVID-19.

Coronavirus Disease 2019 (COVID-19) has caused a global pandemic. Here we profiled the humoral response against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by measuring immunoglobulin (Ig) A, IgM, and IgG against nucleocapsid and spike proteins, along with IgM and IgG antibodies against receptor-binding domain (RBD) of the spike protein and total neutralizing antibodies (NAbs). We tested 279 plasma samples collected from 176 COVID-19 patients who presented and enrolled at different stages of their disease. Plasma dilutions were optimized and based on the data, a single dilution of plasma was used. The mean absorbance at 450 nm was measured for Ig levels and NAbs were measured using geometric mean titers. We demonstrate that more severe cases have a late-onset in the humoral response compared to mild/moderate infections. All the antibody titers continue to rise in patients with COVID-19 over the disease course. However, these levels are mostly unrelated to disease severity. The appearance time and titers of NAbs showed a significant positive correlation to the antibodies against spike protein. Our results suggest the late onset of antibody response as a risk factor for disease severity, however, there is a limited role of antibody titers in predicting disease severity of COVID-19.

Differentiating Between 2019 Novel Coronavirus Pneumonia and Influenza Using a Nonspecific Laboratory Marker-Based Dynamic Nomogram.

BACKGROUND: There is currently a lack of nonspecific laboratory indicators as a quantitative standard to distinguish between the 2019 coronavirus disease (COVID-19) and an influenza A or B virus infection. Thus, the aim of this study was to establish a nomogram to detect COVID-19. METHODS: A nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) in China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza. RESULTS: Our nomogram predicted probabilities of COVID-19 with an area under the receiver operating characteristic curve of 0.913 (95% confidence interval [CI], 0.883-0.937), greater than that of the lymphocyte:monocyte ratio (0.849; 95% CI, 0.812-0.880; P = .0007), lymphocyte percentage (0.808; 95% CI, 0.768-0.843; P < .0001), monocyte count (0.780; 95% CI, 0.739-0.817; P < .0001), or age (0.656; 95% CI, 0.610-0.699; P < .0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves. CONCLUSIONS: We found that age, lymphocyte percentage, and monocyte count are risk factors for the early-stage prediction of patients infected with the 2019 novel coronavirus. As such, our research provides a useful test for doctors to differentiate COVID-19 from influenza.

Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a public health emergency. The widely used reverse transcription-polymerase chain reaction (RT-PCR) method has limitations for clinical diagnosis and treatment. METHODS: A total of 323 samples from 76 COVID-19-confirmed patients were analyzed by droplet digital PCR (ddPCR) and RT-PCR based 2 target genes (ORF1ab and N). Nasal swabs, throat swabs, sputum, blood, and urine were collected. Clinical and imaging data were obtained for clinical staging. RESULTS: In 95 samples that tested positive by both methods, the cycle threshold (Ct) of RT-PCR was highly correlated with the copy number of ddPCR (ORF1ab gene, R2 = 0.83; N gene, R2 = 0.87). Four (4/161) negative and 41 (41/67) single-gene positive samples tested by RT-PCR were positive according to ddPCR with viral loads ranging from 11.1 to 123.2 copies/test. The viral load of respiratory samples was then compared and the average viral load in sputum (17 429 ±â€…6920 copies/test) was found to be significantly higher than in throat swabs (2552 ±â€…1965 copies/test, P < .001) and nasal swabs (651 ±â€…501 copies/test, P < .001). Furthermore, the viral loads in the early and progressive stages were significantly higher than that in the recovery stage (46 800 ±â€…17 272 vs 1252 ±â€…1027, P < .001) analyzed by sputum samples. CONCLUSIONS: Quantitative monitoring of viral load in lower respiratory tract samples helps to evaluate disease progression, especially in cases of low viral load.