Identification and quantitative analysis of bioactive components from Potentilla kleiniana Wight et Arn with anti HIV-1 proteases activity.

Potentilla kleiniana Wight et Arn（PK, 'Wu Pi Feng' in Chinese） was recorded as Miao ethnic medicine for treatment of fever, cough, ulcer, and erysipelas for thousands years. This study aimed to evaluate the antiviral activity of four PK extracts and seven compounds by using HIV-1 protease (HIV-1 PR). In addition, Ultra-High Performance Liquid Chromatography and High Resolution Mass Spectrometry (UPLC-HRMS) was employed to identify the bioactive components. The toxicity assessment of the extracts was done before antiviral screening using a highly specific human aspartyl protease, renin protease by fluorimetric method. As a result, seven compounds and four extracts of PK inhibited HIV-1 PR with IC50 range from 0.009 to 0.36 mg/mL, and did not appreciably inhibit the general human protease renin. This study first demonstrated that four PK extracts, ellagic acid and ursolic acid potent inhibit HIV-1 protease, could be used as an efficacious drug candidate to treat SARS-CoV-2 infection.

A sampling scheme for estimating the prevalence of a pandemic

The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilizes the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and diagnosed cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verify its efficiency. [ FROM AUTHOR] Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd 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 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 . (Copyright applies to all s.)

Pulmonary fibrosis: A short- or long-term sequelae of severe COVID-19?

The pandemic of coronavirus disease 2019 (COVID‑19), caused by a novel severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), has caused an enormous impact on the global healthcare. SARS-CoV-2 infection primarily targets the respiratory system. Although most individuals testing positive for SARS-CoV-2 present mild or no upper respiratory tract symptoms, patients with severe COVID-19 can rapidly progress to acute respiratory distress syndrome (ARDS). ARDS-related pulmonary fibrosis is a recognized sequelae of COVID-19. Whether post-COVID-19 lung fibrosis is resolvable, persistent, or even becomes progressive as seen in human idiopathic pulmonary fibrosis (IPF) is currently not known and remains a matter of debate. With the emergence of effective vaccines and treatments against COVID-19, it is now important to build our understanding of the long-term sequela of SARS-CoV-2 infection, to identify COVID-19 survivors who are at risk of developing chronic pulmonary fibrosis, and to develop effective anti-fibrotic therapies. The current review aims to summarize the pathogenesis of COVID-19 in the respiratory system and highlights ARDS-related lung fibrosis in severe COVID-19 and the potential mechanisms. It envisions the long-term fibrotic lung complication in COVID-19 survivors, in particular in the aged population. The early identification of patients at risk of developing chronic lung fibrosis and the development of anti-fibrotic therapies are discussed.

Pulmonary fibrosis: A short- or long-term sequelae of severe COVID-19?

The pandemic of coronavirus disease 2019 (COVID­19), caused by a novel severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), has caused an enormous impact on the global healthcare. SARS-CoV-2 infection primarily targets the respiratory system. Although most individuals testing positive for SARS-CoV-2 present mild or no upper respiratory tract symptoms, patients with severe COVID-19 can rapidly progress to acute respiratory distress syndrome (ARDS). ARDS-related pulmonary fibrosis is a recognized sequelae of COVID-19. Whether post-COVID-19 lung fibrosis is resolvable, persistent, or even becomes progressive as seen in human idiopathic pulmonary fibrosis (IPF) is currently not known and remains a matter of debate. With the emergence of effective vaccines and treatments against COVID-19, it is now important to build our understanding of the long-term sequela of SARS-CoV-2 infection, to identify COVID-19 survivors who are at risk of developing chronic pulmonary fibrosis, and to develop effective anti-fibrotic therapies. The current review aims to summarize the pathogenesis of COVID-19 in the respiratory system and highlights ARDS-related lung fibrosis in severe COVID-19 and the potential mechanisms. It envisions the long-term fibrotic lung complication in COVID-19 survivors, in particular in the aged population. The early identification of patients at risk of developing chronic lung fibrosis and the development of anti-fibrotic therapies are discussed.

Expression and Immunogenicity of SARS-CoV-2 Virus-Like Particles based on Recombinant Truncated HEV-3 ORF2 Capsid Protein.

COVID-19 is an emerging disease that poses a severe threat to global public health. As such, there is an urgent demand for vaccines against SARS-CoV-2, the virus that causes COVID-19. Here, we describe a virus-like nanoparticle candidate vaccine against SARS-CoV-2 produced by an E. coli expression system. The fusion protein of a truncated ORF2-encoded protein of aa 439~608 (p170) from hepatitis E virus CCJD-517 and the receptor-binding domain of the spike protein from SARS-CoV-2 were expressed, purified and characterized. The antigenicity and immunogenicity of p170-RBD were evaluated in vitro and in Kunming mice. Our investigation revealed that p170-RBD self-assembled into approximately 24 nm virus-like particles, which could bind to serum from vaccinated people (p < 0.001) and receptors on cells. Immunization with p170-RBD induced the titer of IgG antibody vaccine increased from 14 days post-immunization and was significantly enhanced after a booster immunization at 28 dpi, ultimately reaching a peak level on 42 dpi with a titer of 4.97 log10. Pseudovirus neutralization tests showed that the candidate vaccine induced a strong neutralizing antibody response in mice. In this research, we demonstrated that p170-RBD possesses strong antigenicity and immunogenicity and could be a potential candidate for use in future SARS-CoV-2 vaccine development.

Recombinase Polymerase Amplification Combined with Fluorescence Immunochromatography Assay for On-Site and Ultrasensitive Detection of SARS-CoV-2.

This study established a portable and ultrasensitive detection method based on recombinase polymerase amplification (RPA) combined with high-sensitivity multilayer quantum dot (MQD)-based immunochromatographic assay (ICA) to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The RPA-MQD-based ICA method is reported for the first time and has the following advantages: (i) RPA is free from the constraints of instruments and can be promoted in point-of-care testing (POCT) scenarios, (ii) fluorescence ICA enhances the portability of detection operation so that the entire operation time is controlled within 1 h, and (iii) compared with common colorimetric-based RPA-ICA, the proposed assay used MQD to provide strong and quantifiable fluorescence signal, thus enhancing the detection sensitivity. With this strategy, the proposed RPA-MQD-based ICA can amplify and detect the SARS-CoV-2 nucleic acid on-site with a sensitivity of 2 copies/reaction, which is comparable to the sensitivity of commercial reverse transcription quantitative polymerase chain reaction (RT-qPCR) kits. Moreover, the designed primers did not cross-react with other common respiratory viruses, including adenovirus, influenza virus A, and influenza virus B, suggesting high specificity. Thus, the established portable method can sensitively detect SARS-CoV-2 nucleic acid without relying on equipment, having good application prospects in SARS-CoV-2 detection scenarios under non-lab conditions.

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch.

(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) Methods: Based on the competitive state model and spatial mismatch index, this paper uses GIS and Geodetector spatial analysis methods and three typical indicators of hospitals, doctors, and beds to conduct an empirical study on the evolutionary characteristics and degree of mismatch in the geographic pattern of health resources in China from 2010 to 2020 (the data are from official publications issued by the National Bureau of statistics in China), in two dimensions of resource supply (economic carrying capacity) and demand (potential demand or need of residents). (3) Results: The spatial pattern of health resources at the provincial level in China has been firmly established for a long time, and the children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30-40% of the factor pairs are in a nonlinear enhancement relationship. Hospital, doctor, and bed evolution trends and the magnitude and speed of their changes vary widely in spatial differentiation, but all are characterized by a high level of geographic agglomeration, heterogeneity, and gradient. Dynamic matching is the mainstream of development, while the geographical distribution of negative and positive mismatch shows strong spatial agglomeration and weak spatial autocorrelation. The cold and hot spots with evolution trend and space mismatch are highly clustered, shaping a center-periphery or gradient-varying spatial structure. (4) Conclusions: Despite the variability in the results of the analyses by different dimensions and indicators, the mismatch of health resources in China should not be ignored. According to the mismatch types and change trend, and following the geographic differentiation and spatial agglomeration patterns, this paper constructs a policy design framework of "regionalized governance-classified management", in line with the concept of spatial adaptation and spatial justice, in order to provide a decision making basis for the government to optimize the allocation of health resources and carry out health spatial planning.

Isolation in the control of epidemic.

Among many epidemic prevention measures, isolation is an important method to control the spread of infectious disease. Scholars rarely study the impact of isolation on disease dissemination from a quantitative perspective. In this paper, we introduce an isolation ratio and establish the corresponding model. The basic reproductive number and its biological explanation are given. The stability conditions of the disease-free and endemic equilibria are obtained by analyzing its distribution of characteristic values. It is shown that the isolation ratio has an important influence on the basic reproductive number and the stability conditions. Taking the COVID-19 in Wuhan as an example, isolating more than 68% of the population can control the spread of the epidemic. This method can provide precise epidemic prevention strategies for government departments. Numerical simulations verify the effectiveness of the results.

Megakaryocytes in pulmonary diseases.

Megakaryocytes (MKs) are typical cellular components in the circulating blood flowing from the heart into the lungs. Physiologically, MKs function as an important regulator of platelet production and immunoregulation. However, dysfunction in MKs is considered a trigger in various diseases. It has been described that the lung is an important site of platelet biogenesis from extramedullary MKs, which may play an essential role in various pulmonary diseases. With detailed studies, there are different degrees of numerical changes of MKs in coronavirus disease 2019 (COVID-19), acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), lung cancer, pulmonary fibrosis (PF), and other pulmonary diseases. Also, MKs inhibit or promote the development of pulmonary diseases through various pathways. Here, we summarize the current knowledge of MKs in pulmonary diseases, highlighting the physiological functions and integrated molecular mechanisms. We aim to shine new light on not only the subsequent study of MKs but also the diagnosis and treatment of pulmonary diseases.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

Development of an Adeno-Associated Virus-Vectored SARS-CoV-2 Vaccine and Its Immunogenicity in Mice.

Owing to the outbreak of the novel coronavirus (SARS-CoV-2) worldwide at the end of 2019, the development of a SARS-CoV-2 vaccine became an urgent need. In this study, we developed a type 9 adeno-associated virus vectored vaccine candidate expressing a dimeric receptor binding domain (RBD) of the SARS-CoV-2 spike protein (S protein) and evaluated its immunogenicity in a murine model. The vaccine candidate, named AAV9-RBD virus, was constructed by inserting a signal peptide to the N-terminus of two copies of RBD, spaced by a linker, into the genome of a type 9 adeno-associated virus. In vitro assays showed that HeLa cells infected by the recombinant AAV virus expressed high levels of the recombinant RBD protein, mostly found in the cell culture supernatant. The recombinant AAV9-RBD virus was cultured and purified. The genome titer of the purified recombinant AAV9-RBD virus was determined to be 2.4 × 1013 genome copies/mL (GC/mL) by Q-PCR. Balb/c mice were immunized with the virus by intramuscular injection or nasal drip administration. Eight weeks after immunization, neutralizing antibodies against the new coronavirus pseudovirus were detected in the sera of all mice; the mean neutralizing antibody EC50 values were 517.7 ± 292.1 (n=10) and 682.8 ± 454.0 (n=10) in the intramuscular injection group and nasal drip group, respectively. The results of this study showed that the recombinant AAV9-RBD virus may be used for the development of a SARS-CoV-2 vaccine.

Prevalence and clinical characteristics of COVID-19 in inpatients with schizophrenia in Wuhan, China.

BACKGROUND: In contrast to many Western countries, China has maintained its large psychiatric hospitals. The prevalence and clinical characteristics of coronavirus disease 2019 (COVID-19) in inpatients with schizophrenia (SCZ) are unclear. AIM: To assess the prevalence of COVID-19 among inpatients with SCZ and compare the infected to uninfected SCZ patients in a Wuhan psychiatric hospital. METHODS: We retrospectively collected demographic characteristics and clinical profiles of all SCZ patients with COVID-19 at Wuhan's Youfu Hospital. RESULTS: Among the 504 SCZ patients, 84 had COVID-19, and we randomly sampled 174 who were uninfected as a comparison group. The overall prevalence of COVID-19 in SCZ patients was 16.7%. Among the 84 SCZ patients with confirmed COVID-19, the median age was 54 years and 76.2% were male. The most common symptom was fever (82%), and less common symptoms were cough (31%), poor appetite (20%), and fatigue (16%). Compared with SCZ patients without COVID-19, those with COVID-19 were older (P = 0.006) and significantly lighter (P = 0.002), and had more comorbid physical diseases (P = 0.001). Surprisingly, those infected were less likely to be smokers (< 0.001) or to be treated with clozapine (P = 0.03). Further logistic regression showed that smoking [odds ratio (OR) = 5.61], clozapine treated (OR = 2.95), and male (OR = 3.48) patients with relatively fewer comorbid physical diseases (OR = 0.098) were at a lower risk for COVID-19. SCZ patients with COVID-19 presented primarily with fever, but only one-third had a cough, which might otherwise be the most common mode of transmission between individuals. CONCLUSION: Two unexpected protective factors for COVID-19 among SCZ inpatients are smoking and clozapine treatment.

Multiple early factors anticipate post-acute COVID-19 sequelae.

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Role of perceived family support in psychological distress for pregnant women during the COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused major public panic in China. Pregnant women may be more vulnerable to stress, which may cause them to have psychological problems. AIM: To explore the effects of perceived family support on psychological distress in pregnant women during the COVID-19 pandemic. METHODS: A total of 2232 subjects were recruited from three cities in China. Through the online surveys, information on demographic data and health status during pregnancy were collected. Insomnia severity index, generalized anxiety disorder 7-item scale, patient health questionnaire-9, somatization subscale of the symptom check list 90 scale, and posttraumatic stress disorder checklist were used to assess the psychological distress. RESULTS: A total of 1015 (45.4%) women reported having at least one psychological distress. The women who reported having inadequate family support were more likely to suffer from multiple psychological distress (≥ 2 psychological distress) than women who received adequate family support. Among the women who reported less family support, 41.8% reported depression, 31.1% reported anxiety, 8.2% reported insomnia, 13.3% reported somatization and 8.9% reported posttraumatic stress disorder (PTSD), which were significantly higher than those who received strong family support. Perceived family support level was negatively correlated with depressive symptoms (r = -0.118, P < 0.001), anxiety symptoms (r = -0.111, P < 0.001), and PTSD symptoms (r = -0.155, P < 0.001). CONCLUSION: Family support plays an important part on pregnant women's mental health during the COVID-19 pandemic. Better family support can help improve the mental health of pregnant women.

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

Dyslipidemia Is Related to Mortality in Critical Patients With Coronavirus Disease 2019: A Retrospective Study.

Background: It has been reported that dyslipidemia is related to coronavirus-related diseases. Critical patients with coronavirus disease 2019 (COVID-19) who suffered from multiple organ dysfunctions were treated in the intensive care unit (ICU) in Wuhan, China. Whether the lipids profile was associated with the prognosis of COVID-19 in critical patients remained unclear. Methods: A retrospective study was performed in critical patients (N=48) with coronavirus disease 2019 in Leishenshan hospital between February and April 2020 in Wuhan. The parameters including lipid profiles, liver function, and renal function were collected on admission day, 2-3days after the admission, and the day before the achievement of clinical outcome. Results: Albumin value and creatine kinase (ck) value were statistically decreased at 2-3 days after admission compared with those on admission day (P<0.05). Low density lipoprotein (LDL-c), high density lipoprotein (HDL-c), apolipoprotein A (ApoA), and apolipoprotein A (Apo B) levels were statistically decreased after admission (P<0.05). Logistic regression showed that HDL-c level both on admission day and the day before the achievement of clinical outcome were negatively associated with mortality in critical patients with COVID-19. Total cholesterol (TC) level at 2-3days after admission was related to mortality in critical patients with COVID-19. Conclusions: There were lipid metabolic disorders in the critical patients with COVID-19. Lower levels of HDL-c and TC were related to the progression of critical COVID-19.

Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.

Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China.

Babaodan controls excessive immune responses and may represent a cytokine-targeted agent suitable for COVID-19 treatment.

It has become evident that the actions of pro-inflammatory cytokines and/or the development of a cytokine storm are responsible for the occurrence of severe COVID-19 during SARS-CoV-2 infection. Although immunomodulatory mechanisms vary among viruses, the activation of multiple TLRs that occurs primarily through the recruitment of adapter proteins such as MyD88 and TRIF contributes to the induction of a cytokine storm. Based on this, controlling the robust production of pro-inflammatory cytokines by macrophages may be applicable as a cellular approach to investigate potential cytokine-targeted therapies against COVID-19. In the current study, we utilized TLR2/MyD88 and TLR3/TRIF co-activated macrophages and evaluated the anti-cytokine storm effect of the traditional Chinese medicine (TCM) formula Babaodan (BBD). An RNA-seq-based transcriptomic approach was used to determine the molecular mode of action. Additionally, we evaluated the anti-inflammatory activity of BBD in vivo using a mouse model of post-viral bacterial infection-induced pneumonia and seven severely ill COVID-19 patients. Our study reveals the protective role of BBD against excessive immune responses in macrophages, where the underlying mechanisms involve the inhibition of the NF-κB and MAPK signaling pathways. In vivo, BBD significantly inhibited the release of IL-6, thus resulting in increased survival rates in mice. Based on limited data, we demonstrated that severely ill COVID-19 patients benefited from BBD treatment due to a reduction in the overproduction of IL-6. In conclusion, our study indicated that BBD controls excessive immune responses and may thus represent a cytokine-targeted agent that could be considered to treating COVID-19.