SARS-CoV-2 infection aggravates chronic comorbidities of cardiovascular diseases and diabetes in mice.

Background: Cardiovascular diseases (CVDs) and diabetes mellitus (DM) are top two chronic comorbidities that increase the severity and mortality of COVID-19. However, how SARS-CoV-2 alters the progression of chronic diseases remain unclear. Methods: We used adenovirus to deliver h-ACE2 to lung to enable SARS-CoV-2 infection in mice. SARS-CoV-2's impacts on pathogenesis of chronic diseases were studied through histopathological, virologic and molecular biology analysis. Results: Pre-existing CVDs resulted in viral invasion, ROS elevation and activation of apoptosis pathways contribute myocardial injury during SARS-CoV-2 infection. Viral infection increased fasting blood glucose and reduced insulin response in DM model. Bone mineral density decreased shortly after infection, which associated with impaired PI3K/AKT/mTOR signaling. Conclusion: We established mouse models mimicked the complex pathological symptoms of COVID-19 patients with chronic diseases. Pre-existing diseases could impair the inflammatory responses to SARS-CoV-2 infection, which further aggravated the pre-existing diseases. This work provided valuable information to better understand the interplay between the primary diseases and SARS-CoV-2 infection.

SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation.

Insufficient annotated data and minor lung lesions pose big challenges for computed tomography (CT)-aided automatic COVID-19 diagnosis at an early outbreak stage. To address this issue, we propose a Semi-Supervised Tri-Branch Network (SS-TBN). First, we develop a joint TBN model for dual-task application scenarios of image segmentation and classification such as CT-based COVID-19 diagnosis, in which pixel-level lesion segmentation and slice-level infection classification branches are simultaneously trained via lesion attention, and individual-level diagnosis branch aggregates slice-level outputs for COVID-19 screening. Second, we propose a novel hybrid semi-supervised learning method to make full use of unlabeled data, combining a new double-threshold pseudo labeling method specifically designed to the joint model and a new inter-slice consistency regularization method specifically tailored to CT images. Besides two publicly available external datasets, we collect internal and our own external datasets including 210,395 images (1,420 cases versus 498 controls) from ten hospitals. Experimental results show that the proposed method achieves state-of-the-art performance in COVID-19 classification with limited annotated data even if lesions are subtle, and that segmentation results promote interpretability for diagnosis, suggesting the potential of the SS-TBN in early screening in insufficient labeled data situations at the early stage of a pandemic outbreak like COVID-19.

Striving for the bottom line: the impact of supervisor bottom-line mentality on employees' work effort and helping behavior

Purpose>This study aims to advance the bottom-line mentality (BLM) literature by drawing on goal-setting theory to examine the positive effects of supervisor BLM on employees' behavior.Design/methodology/approach>The authors collected survey data from 291 full-time employees from various Chinese organizations at three different points in time.Findings>The authors found that supervisor BLM and employees' collectivism orientation interacted to influence employees' bottom-line goal commitment such that the positive relationship between supervisor BLM and employees' bottom-line goal commitment was stronger when employees' collectivism orientation was high rather than low. Furthermore, they found that employees' collectivism orientation moderated the positive indirect effects of supervisor BLM on employees' work effort and helping behavior via bottom-line goal commitment such that the indirect effects were stronger when employees had a high rather than a low collectivism orientation.Originality/value>The authors explored the “bridge side” of supervisor BLM on employees' behavior, especially after being moderated by collectivism orientation. Our results can help managers develop a comprehensive understanding of BLM.

Sequential immunizations confer cross-protection against variants of SARS-CoV-2, including Omicron in Rhesus macaques.

Variants of concern (VOCs) like Delta and Omicron, harbor a high number of mutations, which aid these viruses in escaping a majority of known SARS-CoV-2 neutralizing antibodies (NAbs). In this study, Rhesus macaques immunized with 2-dose inactivated vaccines (Coronavac) were boosted with an additional dose of homologous vaccine or an RBD-subunit vaccine, or a bivalent inactivated vaccine (Beta and Delta) to determine the effectiveness of sequential immunization. The booster vaccination significantly enhanced the duration and levels of neutralizing antibody titers against wild-type, Beta, Delta, and Omicron. Animals administered with an indicated booster dose and subsequently challenged with Delta or Omicron variants showed markedly reduced viral loads and improved histopathological profiles compared to control animals, indicating that sequential immunization could protect primates against Omicron. These results suggest that sequential immunization of inactivated vaccines or polyvalent vaccines could be a potentially effective countermeasure against newly emerging variants.

Landscapes and dynamic diversifications of B-cell receptor repertoires in COVID-19 patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the pandemic of coronavirus disease 2019 (COVID-19). Great international efforts have been put into the development of prophylactic vaccines and neutralizing antibodies. However, the knowledge about the B cell immune response induced by the SARS-CoV-2 virus is still limited. Here, we report a comprehensive characterization of the dynamics of immunoglobin heavy chain (IGH) repertoire in COVID-19 patients. By using next-generation sequencing technology, we examined the temporal changes in the landscape of the patient's immunological status and found dramatic changes in the IGH within the patient's immune system after the onset of COVID-19 symptoms. Although different patients have distinct immune responses to SARS-CoV-2 infection, by employing clonotype overlap, lineage expansion, and clonotype network analyses, we observed a higher clonotype overlap and substantial lineage expansion of B cell clones 2-3 weeks after the onset of illness, which is of great importance to B-cell immune responses. Meanwhile, for preferences of V gene usage during SARS-CoV-2 infection, IGHV3-74 and IGHV4-34, and IGHV4-39 in COVID-19 patients were more abundant than those of healthy controls. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development as well as mechanistic research.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs) may be safe for COVID-19 patients.

BACKGROUND: To investigate the effects of angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blockers (ARBs) administration to hypertension patients with the coronavirus disease 2019 (COVID-19) induced pneumonia. METHODS: We recorded the recovery status of 67 inpatients with hypertension and COVID-19 induced pneumonia in the Raytheon Mountain Hospital in Wuhan during February 12, 2020 and March 30, 2020. Patients treated with ACEI or ARBs were categorized in group A (n = 22), while patients who were not administered either ACEI or ARBs were categorized into group B (n = 45). We did a comparative analysis of various parameters such as the pneumonia progression, length-of-stay in the hospital, and the level of alanine aminotransferase (ALT), serum creatinine (Cr), and creatine kinase (CK) between the day when these patients were admitted to the hospital and the day when the treatment ended. RESULTS: These 67 hypertension cases counted for 33.17% of the total COVID-19 patients. There was no significant difference in the usage of drug treatment of COVID-19 between groups A and B (p > 0.05). During the treatment, 1 case in group A and 3 cases in group B progressed from mild pneumonia into severe pneumonia. Eventually, all patients were cured and discharged after treatment, and no recurrence of COVID-2019 induced pneumonia occurred after the discharge. The length of stays was shorter in group A as compared with group B, but there was no significant difference (p > 0.05). There was also no significant difference in other general parameters between the patients of the groups A and B on the day of admission to the hospital (p > 0.05). The ALT, CK, and Cr levels did not significantly differ between groups A and B on the day of admission and the day of discharge (p > 0.05). CONCLUSIONS: To treat the hypertension patients with COVID-19 caused pneumonia, anti-hypertensive drugs (ACEs and ARBs) may be used according to the relative guidelines. The treatment regimen with these drugs does not need to be altered for the COVID-19 patients.

Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology.

BACKGROUND: COVID-19 has caused a global pandemic, and there is no wonder drug for epidemic control at present. However, many clinical practices have shown that traditional Chinese medicine has played an important role in treating the outbreak. Among them, ephedra-bitter almond is a common couplet medicine in anti-COVID-19 prescriptions. This study aims to conduct an exploration of key components and mechanisms of ephedra-bitter almond anti-COVID-19 based on network pharmacology. MATERIAL AND METHODS: We collected and screened potential active components of ephedra-bitter almond based on the TCMSP Database, and we predicted targets of the components. Meanwhile, we collected relevant targets of COVID-19 through the GeneCards and CTD databases. Then, the potential targets of ephedra-bitter almond against COVID-19 were screened out. The key components, targets, biological processes, and pathways of ephedra-bitter almond anti-COVID-19 were predicted by constructing the relationship network of herb-component-target (H-C-T), protein-protein interaction (PPI), and functional enrichment. Finally, the key components and targets were docked by AutoDock Vina to explore their binding mode. RESULTS: Ephedra-bitter almond played an overall regulatory role in anti-COVID-19 via the patterns of multi-component-target-pathway. In addition, some key components of ephedra-bitter almond, such as ß-sitosterol, estrone, and stigmasterol, had high binding activity to 3CL and ACE2 by molecular docking simulation, which provided new molecular structures for new drug development of COVID-19. CONCLUSION: Ephedra-bitter almonds were used to prevent and treat COVID-19 through directly inhibiting the virus, regulating immune responses, and promoting body repair. However, this work is a prospective study based on data mining, and the findings need to be interpreted with caution.

Associations between air pollution and COVID-19 epidemic during quarantine period in China.

The coronavirus disease (COVID-19) has become a global public health threaten. A series of strict prevention and control measures were implemented in China, contributing to the improvement of air quality. In this study, we described the trend of air pollutant concentrations and the incidence of COVID-19 during the epidemic and applied generalized additive models (GAMs) to assess the association between short-term exposure to air pollution and daily confirmed cases of COVID-19 in 235 Chinese cities. Disease progression based on both onset and report dates as well as control measures as potential confounding were considered in the analyses. We found that stringent prevention and control measures intending to mitigate the spread of COVID-19, contributed to a significant decline in the concentrations of air pollutants except ozone (O3). Significant positive associations of short-term exposure to air pollutants, including particulate matter with diameters ≤2.5 µm (PM2.5), particulate matter with diameters ≤10 µm (PM10), and nitrogen dioxide (NO2) with daily new confirmed cases were observed during the epidemic. Per interquartile range (IQR) increase in PM2.5 (lag0-15), PM10 (lag0-15), and NO2 (lag0-20) were associated with a 7% [95% confidence interval (CI): (4-9)], 6% [95% CI: (3-8)], and 19% [95% CI: (13-24)] increase in the counts of daily onset cases, respectively. Our results suggest that there is a statistically significant association between ambient air pollution and the spread of COVID-19. Thus, the quarantine measures can not only cut off the transmission of virus, but also retard the spread by improving ambient air quality, which might provide implications for the prevention and control of COVID-19.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Efficacy and safety of Jinhua Qinggan granules for coronavirus disease 2019 (COVID-19): A protocol of a systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome (SARS)-COV2 and represents the causative agent of a potentially fatal disease. Jinhua Qinggan granules has definite effect in treating COVID-19 patients, but it has not been systematically evaluated for efficacy and safety. METHODS: Retrieved the database, including the China National Knowledge Infrastructure (CNKI), Chinese Biomedical literature Database (CBM), Chinese Scientific and Journal Database (VIP), Wan Fang database, PubMed, and EMBASE. Evaluate methodological quality and judge risk of bias through the Cochrane manual. RevMan 5.3 and STATA 16.0 software were used to perform the meta-analysis. RESULTS: This study will provide high-quality evidence of Jinhua Qinggan granules for COVID-19. CONCLUSION: The conclusion of this study will provide evidence to determine whether Jinhua Qinggan granules is an effective treatment for COVID-19. PROSPERO REGISTRATION NUMBER: CRD42020182373.

Therapeutic efficacy of Qingfei Paidu decoction combined with antiviral drugs in the treatment of corona virus disease 2019: A protocol for systematic review and meta analysis.

BACKGROUND: The corona virus disease 2019 (COVID-19) has caused a global pandemic, there are no specific drugs and vaccines for epidemic control at present. More and more clinical practice shows that traditional Chinese medicine has played an important role in the outbreak. Among them, Qingfei Paidu decoction (QPD) combined with antiviral drugs can enhance the therapeutic efficacy for COVID-19. However, there is still a lack of comprehensive and systematic evidence, which urgently requires us to verify its therapeutic efficacy. Hence, we provide a protocol for systematic review and meta-analysis. METHODS: We will search the studies in MEDLINE/PubMed, China National Knowledge Infrastructure, Wanfang database, VIP database, the Cochrane Library, Chinese Biomedical Database and Chinese Science Citation Database. Searches are limited to clinical studies published in Chinese and English. Next, the quality of each study is assessed according to the criteria of the Cochrane Handbook for Systematic Reviews of Interventions. Then, the outcome data are recorded and pooled by Review Manager 5.3 and STATA 16.0 software. RESULTS: The systematic review and meta-analysis aims to review and pool current clinical outcomes of QPD combined with antiviral drugs for the treatment of COVID-19. CONCLUSION: This study will provide a high-quality evidence of QPD for the treatment on COVID-19 patients. PROSPERO REGISTRATION NUMBER: CRD42020182409.