CCL12 induces trabecular bone loss by stimulating RANKL production in BMSCs during acute lung injury.

In the last three years, the capacity of health care systems and the public health policies of governments worldwide were challenged by the spread of SARS-CoV-2. Mortality due to SARS-CoV-2 mainly resulted from the development of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). Moreover, millions of people who survived ALI/ARDS in SARS-CoV-2 infection suffer from multiple lung inflammation-induced complications that lead to disability and even death. The lung-bone axis refers to the relationship between lung inflammatory diseases (COPD, asthma, and cystic fibrosis) and bone diseases, including osteopenia/osteoporosis. Compared to chronic lung diseases, the influence of ALI on the skeleton has not been investigated until now. Therefore, we investigated the effect of ALI on bone phenotypes in mice to elucidate the underlying mechanisms. In vivo bone resorption enhancement and trabecular bone loss were observed in LPS-induced ALI mice. Moreover, chemokine (C-C motif) ligand 12 (CCL12) accumulated in the serum and bone marrow. In vivo global ablation of CCL12 or conditional ablation of CCR2 in bone marrow stromal cells (BMSCs) inhibited bone resorption and abrogated trabecular bone loss in ALI mice. Furthermore, we provided evidence that CCL12 promoted bone resorption by stimulating RANKL production in BMSCs, and the CCR2/Jak2/STAT4 axis played an essential role in this process. Our study provides information regarding the pathogenesis of ALI and lays the groundwork for future research to identify new targets to treat lung inflammation-induced bone loss.

A comparison of Remdesivir versus gold cluster in COVID-19 animal model: A better therapeutic outcome of gold cluster.

While gold compound have been approved for Rheumatoid arthritis treatment as it well suppresses inflammatory cytokines of patients, no such treatment is currently available for COVID-19 treatment in vivo . We firstly disclose gold cluster yields better therapeutic outcome than Remdesivir in COVID-19 hamster treatments as it is armed with direct inhibition viral replication and intrinsic suppression inflammatory cytokines expression. Crystal data reveals that Au (I), released from gold cluster (GA), covalently binds thiolate of Cys145 of SARS-CoV-2 Mpro. GA directly decreases SARS-CoV-2 viral replication and intrinsically down-regulates NFκB pathway therefore significantly inhibiting expression of inflammatory cytokines in cells. The inflammatory cytokines in GA-treated COVID-19 transgenic mice are found to be significantly lower than that of control mice. When COVID-19 golden hamsters are treated by GA, the lung inflammatory cytokines levels are significantly lower than that of Remdesivir. The pathological results show that GA treatment significantly reduce lung inflammatory injuries when compared to that of Remdesivir-treated COVID-19 hamsters.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine.

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

A comparison of Remdesivir versus gold cluster in COVID-19 animal model: a better therapeutic outcome of gold cluster

While gold compound have been approved for Rheumatoid arthritis treatment as it well suppresses inflammatory cytokines of patients, no such treatment is currently available for COVID-19 treatment in vivo. We firstly disclose gold cluster yields better therapeutic outcome than Remdesivir in COVID-19 hamster treatments as it is armed with direct inhibition viral replication and intrinsic suppression inflammatory cytokines expression. Crystal data reveals that Au (I), released from gold cluster (GA), covalently binds thiolate of Cys145 of SARS-CoV-2 Mpro. GA directly decreases SARS-CoV-2 viral replication and intrinsically down-regulates NFκB pathway therefore significantly inhibiting expression of inflammatory cytokines in cells. The inflammatory cytokines in GA-treated COVID-19 transgenic mice are found to be significantly lower than that of control mice. When COVID-19 golden hamsters are treated by GA, the lung inflammatory cytokines levels are significantly lower than that of Remdesivir. The pathological results show that GA treatment significantly reduce lung inflammatory injuries when compared to that of Remdesivir-treated COVID-19 hamsters. Graphical

How should local Brick-and-Mortar retailers offer delivery service in a pandemic World? Self-building Vs. O2O platform.

The Covid-19 pandemic has dramatically changed consumer purchase behavior, and the "stay-at-home order" policy has altered the operations of brick-and-mortar (B&M) retail stores. These changes have induced local B&M retailers to start online retailing with home delivery as an added option. B&M retailers can choose to offer online retailing on their own (referred to as self-building mode) or via a third-party online-to-offline (O2O) platform (referred to as platform mode). This paper investigates how the interplay between capacity, pricing, and online retailing mode is affected by the absence/presence of the pandemic. We characterize the equilibrium between the B&M retailer and the O2O platform provider. We find that the impact of the "stay-at-home orders" on B&M retailers differs by the online retailing mode. Interestingly, we find that the "stay-at-home orders" does not necessarily lower the B&M retailer's profit if they engage in online retailing. Under self-building mode, the stay-at-home order leaves the B&M retailer with just the online channel. We identify the threshold delivery cost above (below) which the B&M retailer's profit is lower (higher) than before. Under the platform mode, the "stay-at-home order" alters the retailer's sales channel from dual channel to single channel, which mitigates the competition between the retailer and the O2O platform. The retailer's profit increases if it has sufficiently high capacity. Finally, we extend the model to examine the effect of a "reopening policy" with a government subsidy. We find that although the subsidy improves the B&M retailer's profitability, it may hurt the consumer surplus under some conditions. We suggest that governments take the B&M retailer's capacity and operations mode into account when designing subsidy policies.

Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19.

BACKGROUND: The outbreak of COVID-19 poses a major and urgent threat to global public health. CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. This study aimed to retrospectively analyze abnormal lung changes following initial computed tomography (CT) among patients with coronavirus disease 2019 (COVID-19) in Yunnan, and to evaluate the effectiveness of a chest CT-based model for the diagnosis of COVID-19. METHODS: One hundred and nine patients with COVID-19 pneumonia confirmed with the positive new coronavirus nucleic acid antibody who exhibited abnormal findings on initial CT were retrospectively analyzed. Thereafter, changes in the number, distribution, shape, and density of the lesions were observed. Further, the epidemiological, clinical, and CT imaging findings (+/-) were correlated. Following univariate and multivariate logistic regression analysis, receiver operating characteristic (ROC) curves were generated for significant factors, and models were established to evaluate the diagnostic ability of CT for COVID-19. RESULTS: Our results showed significant differences between patients with COVID-19 in epidemiological history (first, second, and third generation), clinical type (moderate, severe, and critical), and abnormal CT imaging characteristics (+/-) (P<0.05). Moreover, significant differences in abnormal CT imaging characteristics, including region, extent, and focus, were observed between the first generation and the other generations (P<0.05). For the diagnosis of COVID-19, the areas under the ROC curves for logistic regression models 1, 2, and 3 were 0.8016 (95% CI: 0.6759-0.9274), 0.9132 (95% CI: 0.8571-0.9693), and 0.9758 (95% CI: 0.9466-1), respectively. CONCLUSIONS: The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19.

Dynamic analysis of pulmonary computed tomography (CT) characteristics in cured coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: To retrospectively analyze the pulmonary computed tomography (CT) characteristics and dynamic changes in the lungs of cured coronavirus disease 2019 (COVID-19) patients at discharge and reexamination. METHODS: A total of 155 cured COVID-19 patients admitted to designated hospitals in Yunnan Province, China, from February 1, 2020, to March 20, 2020, were included. All patients underwent pulmonary CT at discharge and at 2 weeks after discharge (during reexamination at hospital). A retrospective analysis was performed using these two pulmonary CT scans of the cured patients to observe changes in the number, distribution, morphology, and density of lesions. RESULTS: At discharge, the lung CT images of 15 cured patients showed no obvious lesions, while those of the remaining 140 patients showed different degrees of residual lesions. Patients with moderate disease mostly had multiple pulmonary lesions, mainly in the lower lobes of both lungs. At reexamination, the lung lesions in the patients with moderate disease had significantly improved (P<0.05), and the lung lesions in the patients with severe disease had partially improved, especially in patients with multi-lobe involvement (χ 2 =3.956, P<0.05). At reexamination, the lung lesions of patients with severe disease did not show significant changes (P>0.05). CONCLUSIONS: The pulmonary CT manifestations of cured COVID-19 patients had certain characteristics and variation patterns, providing a reference for the clinical evaluation of treatment efficacy and prognosis of patients.

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.

BACKGROUND: In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. METHODS: A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. RESULTS: The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). CONCLUSIONS: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

Dynamic changes in chest CT findings of patients with coronavirus disease 2019 (COVID-19) in different disease stages: a multicenter study.

BACKGROUND: To investigate the dynamic changes in high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) patients with different severities in different disease stages. METHODS: We retrospectively collected the clinical and imaging data of 96 patients in Yunnan Province, China, who were diagnosed with COVID-19 between January 22 and March 15, 2020. Based on disease severity, the COVID-19 patients were classified into four types: mild (n=15), moderate (n=59), severe (n=19), and critical (n=3). Based on hospital stay and number of computed tomography (CT) scans, the clinical/disease course was divided into four stages, including stage 1 (days 0-4), stage 2 (days 5-9), stage 3 (days 10-14), and stage 4 (days 15-19). The HRCT findings, CT value, and lesion volume were analyzed for each stage and compared among the four stages of COVID-19 patients. RESULTS: CT findings were negative over the four stages for all mild COVID-19 patients. More lesions were found in the peripheral lung fields than in peripheral + central fields (P<0.05), and the number of negative patients in stage 4 were more than those in stages 1-3 (P<0.05). The left and right lower lobe were the most frequently affected lobes (P<0.05). In moderate patients, round ground glass opacities (GGOs) decreased from stage 1 to stage 4; partial consolidation peaked in stage 2 and then decreased in stages 3-4; fibrous stripes and subpleural lines increased from stage 1 and peaked in stage 4. Partial consolidation and consolidation were more common in severe patients than in moderate patients over the disease course (P<0.05). Critical patients showed significant partial consolidation and consolidation; The CT value, lesion volume and lesion volume percentage significantly decreased from stages 1-2 to stage 4 (all P<0.05). CONCLUSIONS: The dynamic changes in lung HRCT images are clinically related to the disease course of COVID-19.

Comparison of initial high-resolution computed tomography (HRCT) features of coronavirus disease 2019 (COVID-19) pneumonia and other viral pneumonias.

BACKGROUND: Multicenter retrospective comparison of the first high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) and other viral pneumonias. METHODS: We retrospectively collected clinical and imaging data from 262 cases of confirmed viral pneumonia in 20 hospitals in Yunnan Province, China, from March 1, 2015 to March 15, 2020. According to the virus responsible for the pneumonia, the pneumonias were divided into non-COVID-19 (141 cases) and COVID-19 (121 cases). The non-COVID-19 pneumonias comprised cytomegalovirus (CMV) (31 cases), influenza A virus (82 cases), and influenza B virus (20 cases). The differences in the basic clinical characteristics, lesion distribution, location and imaging signs among the four viral pneumonias were analyzed and compared. RESULTS: Fever and cough were the most common clinical symptoms of the four viral pneumonias. Compared with the COVID-19 patients, the non-COVID-19 patients had higher proportions of fatigue, sore throat, expectorant and chest tightness (all P<0.000). In addition, in the CMV pneumonia patients, the proportions of acquired immunodeficiency syndrome (AIDS) and leukopenia were high (all PP<0.000). Comparison of the imaging findings of the four viral pneumonias showed that the pulmonary lesions of COVID-19 were more likely to occur in the peripheral and lower lobes of both lungs, whereas those of CMV pneumonia were diffusely distributed. Compared with the non-COVID-19 pneumonias, COVID-19 pneumonia was more likely to present as ground-glass opacity, intralobular interstitial thickening, vascular thickening and halo sign (all PP<0.05). In addition, in the early stage of COVID-19, extensive consolidation, fibrous stripes, subpleural lines, crazy-paving pattern, tree-in-bud, mediastinal lymphadenectasis, pleural thickening and pleural effusion were rare (all PP<0.05). CONCLUSIONS: The HRCT findings of COVID-19 pneumonia and other viral pneumonias overlapped significantly, but many important differential imaging features could still be observed.

The m6A methylome of SARS-CoV-2 in host cells.

The newly identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a global health emergency because of its rapid spread and high mortality. The molecular mechanism of interaction between host and viral genomic RNA is yet unclear. We demonstrate herein that SARS-CoV-2 genomic RNA, as well as the negative-sense RNA, is dynamically N6-methyladenosine (m6A)-modified in human and monkey cells. Combined RIP-seq and miCLIP analyses identified a total of 8 m6A sites at single-base resolution in the genome. Especially, epidemic strains with mutations at these identified m6A sites have emerged worldwide, and formed a unique cluster in the US as indicated by phylogenetic analysis. Further functional experiments showed that m6A methylation negatively regulates SARS-CoV-2 infection. SARS-CoV-2 infection also triggered a global increase in host m6A methylome, exhibiting altered localization and motifs of m6A methylation in mRNAs. Altogether, our results identify m6A as a dynamic epitranscriptomic mark mediating the virus-host interaction.

Blood donor recruitment in Guangzhou, China, during the 2019 novel coronavirus (COVID-19) epidemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic affected blood collection in Guangzhou, China. STUDY DESIGN AND METHODS: This paper includes three studies. The observational study reported the trends of blood collection during the epidemic in Guangzhou, China. The cross-sectional survey investigated factors influencing blood donation during the COVID-19 epidemic, and a self-administered questionnaire was given to 1584 street whole blood donors (SWBDs) who donated during the epidemic. The randomized controlled trial involved 19 491 SWBDs who donated in 2019 but did not donate during the epidemic. Trial participants were randomly assigned to two intervention groups: Group 1 completed Questionnaire 1, which contained precautionary measures in response to COVID-19 and other messages about blood donation during the epidemic; Group 2 completed Questionnaire 2, which did not include this information. A control group did not receive any questionnaire. RESULTS: As measures were implemented, the number of blood donors increased accordingly. Both first-time and repeat SWBDs perceived the same level of blood need and donated blood because it would save lives. SWBDs who completed Questionnaire 1 expressed a greater intention to donate during the epidemic. Enabling blood donors to perceive a higher level of blood need and a lower level of COVID-19 infection risk related to blood donation mobilized experienced SWBDs to donate within 3 weeks. Intention-to-treat analyses and average-treatment-effect-on-the-treated estimations confirmed that Questionnaire 1 could motivate SWBDs to actually donate blood. CONCLUSION: Various measures could ease blood shortage during the COVID-19 epidemic. Administration of Questionnaire 1 could increase blood donations during the epidemic.