Targeting integrin pathways: mechanisms and advances in therapy.

Integrins are considered the main cell-adhesion transmembrane receptors that play multifaceted roles as extracellular matrix (ECM)-cytoskeletal linkers and transducers in biochemical and mechanical signals between cells and their environment in a wide range of states in health and diseases. Integrin functions are dependable on a delicate balance between active and inactive status via multiple mechanisms, including protein-protein interactions, conformational changes, and trafficking. Due to their exposure on the cell surface and sensitivity to the molecular blockade, integrins have been investigated as pharmacological targets for nearly 40 years, but given the complexity of integrins and sometimes opposite characteristics, targeting integrin therapeutics has been a challenge. To date, only seven drugs targeting integrins have been successfully marketed, including abciximab, eptifibatide, tirofiban, natalizumab, vedolizumab, lifitegrast, and carotegrast. Currently, there are approximately 90 kinds of integrin-based therapeutic drugs or imaging agents in clinical studies, including small molecules, antibodies, synthetic mimic peptides, antibody-drug conjugates (ADCs), chimeric antigen receptor (CAR) T-cell therapy, imaging agents, etc. A serious lesson from past integrin drug discovery and research efforts is that successes rely on both a deep understanding of integrin-regulatory mechanisms and unmet clinical needs. Herein, we provide a systematic and complete review of all integrin family members and integrin-mediated downstream signal transduction to highlight ongoing efforts to develop new therapies/diagnoses from bench to clinic. In addition, we further discuss the trend of drug development, how to improve the success rate of clinical trials targeting integrin therapies, and the key points for clinical research, basic research, and translational research.

Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms

The ability to estimate current GDP growth before official data are released, known as "nowcasting”, is crucial for the Chinese government to effectively implement economic policy and manage economic uncertainties;however, there is limited research on nowcasting China's GDP in a data-rich environment. We evaluate the performance of various machine learning algorithms, dynamic factor models, static factor models, and MIDAS regressions in nowcasting the Chinese annualised real GDP growth rate in pseudo out-of-sample exercise, using 89 macroeconomic variables from years 1995 to 2020. We find that some machine learning methods outperform the benchmark dynamic factor model. The machine learning method that deserves more attention is ridge regression, which dominates all other models not only in terms of nowcast error but also in effective recognition of the impacts of the Global Financial Crisis and Covid-19 shocks. Policy-wise, our study guides practitioners in selecting appropriate nowcasting models for China's macroeconomy.

Potential Anionic Substances Binding to Platelet Factor 4 in Vaccine-Induced Thrombotic Thrombocytopenia of ChAdOx1-S Vaccine for SARS-CoV-2

Recent reports of rare ChAdOx1-S vaccine-related venous thrombosis led to the suspension of its usage in several countries. Vaccine-induced thrombotic thrombocytopenia (VITT) is characterized by thrombocytopenia and thrombosis in association with anti-platelet factor 4 (PF4) antibodies. Herein, we propose five potential anionic substances of the ChAdOx1-S vaccine that can combine with PF4 and trigger VITT, including (1) the proteins on the surface of adenovirus, e.g., negative charged glycoprotein, (2) the adjuvant components of the vaccine, e.g., Tween 80, (3) the DNA of adenovirus, (4) the S protein antigen expressed by the vaccine, and (5) the negatively charged impurity proteins expressed by the vaccine, e.g., adenovirus skeleton proteins. After analysis of each case, we consider the most possible trigger to be the negatively charged impurity proteins expressed by the vaccine. Then, we display the possible extravascular route and intravascular route of the formation of PF4 autoantibodies triggered by the negatively charged impurity proteins, which is accordant with the clinical situation. Accordingly, the susceptible individuals of VITT after ChAdOx1-S vaccination may be people who express negatively charged impurity proteins and reach a certain high titer.

Venous thromboembolism in patients with COVID-19. A prevalent and a preventable complication of the pandemic.

Coronavirus disease 2019 or most commonly known as COVID-19 is a trending global infectious disease which a few months ago was affirmed as a global health emergency or a pandemic by the WHO Emergency Committee. The common symptoms manifested in this pandemic disease are high grade fever, cough, fatigue, shortness of breath and flu like symptom which can evolve into severe respiratory disorders such as pneumonia, acute respiratory distress syndrome (ARDS) and/or end-organ failure. Factors that contribute to the severity or high mortality rate in COVID-19 include old age, comorbidities like hypertension, diabetes, hyperlipidaemia, neutrophilia, and organ and coagulation dysfunction. Disseminated intravascular coagulation and other various coagulopathies including Venous thromboembolism have known to become a major contributing factor to high mortality rate. Venous thromboembolism is a disease which is a combination of deep vein thrombosis and pulmonary embolism. Prophylactic anticoagulation in patients prone to or with a pre-existing history of venous thromboembolism is associated with decreased mortality in severe COVID-19 pneumonia. This review article focuses upon COVID-19 and increased incidence of venous thromboembolism in patients infected by COVID-19 along with the role it has in high mortality rate in COVID-19 patients.

CLEC4s as Potential Therapeutic Targets in Hepatocellular Carcinoma Microenvironment.

Immunosuppressive tumor microenvironment in hepatocellular carcinoma (HCC) is critical in tumor development. C-type (Ca2+ -dependent) lectin (CLEC) receptors, essential in innate pattern recognition, have potential regulatory effects on immune cell trafficking and modulatory effects on cancer cell activity. However, information on the expression and prognostic value of CLECs in HCC is scanty. Herein, we explored the potential role of CLECs in HCC based on TCGA, ONCOMINE, GEPIA, UALCAN, cBioPortal, Metascape, TRRUST, and TIMER databases. Results demonstrated a significantly higher mRNA level of CLEC4A and CLEC4L in HCC tissues than normal liver tissues. Contrarily, we found significantly low CLEC4G/H1/H2/M expression in HCC tissues. The IHC analysis revealed the following: Absence of CLEC4A/J/K/M in normal and liver cancer tissues; high CLEC4C expression in HCC tissues; low expression and zero detection of CLEC4D/E/H1/H2/L in HCC tissues and normal tissues, respectively. And the HepG2 and LX-2 were used to verify the expression level of CLEC4s via qRT-PCR in vitro. Furthermore, the expression of CLEC4H1 (ASGR1) and CLEC4H2 (ASGR2) exhibited a significant relation to clinical stages. However, the expression of CLEC4A, CLEC4D, CLEC4E, CLEC4J (FCER2), CLEC4K (CD207), CLEC4G, CLEC4H1, CLEC4M, and CLEC4H2 decreased with tumor progression. Patients expressing higher CLEC4H1/H2 levels had longer overall survival than patients exhibiting lower expression. Moreover, CLEC4A/D/E/J/K/G/H1/M/H2 had significant down-regulated levels of promoter methylation. The expression level of CLEC4s was correlated with the infiltration of B cells, CD8 + T cells, CD4 + T cells, macrophage cells, neutrophil cells, and dendritic cells. Functional analysis revealed the potential role of CLECL4s in virus infection, including COVID-19. Also, hsa-miR-4278 and hsa-miR-324-5p, two potential miRNA targets of CLEC4s, were uncovered. This article demonstrates that CLEC4 is crucial for the development of HCC and is associated with infiltration of various immune cells, providing evidence for new immunotherapy targets in HCC.

Ad5-spike COVID-19 vaccine does not aggravate heart damage after ischemic injury (preprint)

Hopes for a COVID-19 vaccine are now a reality. The spike protein of SARS-CoV-2, which majorly binds to the host receptor ACE2 for cell entry, is used by most of the COVID-19 vaccine candidates as a choice of antigen. ACE2 is highly expressed in the heart and is known to be protective in multiple organs. Interaction of spike with ACE2 has been reported to reduce ACE2 expression and affect ACE2-mediated signal transduction in the heart. However, whether a spike-encoding vaccine will aggravate myocardial damage after a heart attack via affecting ACE2 remains unclear. Therefore, for patients with or at risk of heart diseases, questions arise around the safety of the spike-based vaccines. Here, we demonstrate that ACE2 is up-regulated and protective in the injured mouse heart after myocardial ischemia/reperfusion (I/R). Infecting human cardiomyocyte, smooth muscle cells, endothelial cells, and cardiac fibroblasts with a recombinant adenovirus type-5 vectored COVID-19 vaccine expressing the spike protein (AdSpike) does not affect cell survival and cardiomyocyte function, whether the cells are subjected to hypoxia-reoxygenation injury or not. This observation is further confirmed in human engineered heart tissues. Furthermore, AdSpike vaccination does not aggravate heart damage in wild-type or humanized ACE2 mice after I/R injury, even at a dose that is ten-fold higher as used in human. This study represents the first systematic evaluation of the safety of a leading COVID-19 vaccine under a disease context and may provide important information to ensure maximal protection from COVID-19 in patients with or at risk of heart diseases.

Simeprevir Potently Suppresses SARS-CoV-2 Replication and Synergizes with Remdesivir.

The outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global threat to human health. Using a multidisciplinary approach, we identified and validated the hepatitis C virus (HCV) protease inhibitor simeprevir as an especially promising repurposable drug for treating COVID-19. Simeprevir potently reduces SARS-CoV-2 viral load by multiple orders of magnitude and synergizes with remdesivir in vitro. Mechanistically, we showed that simeprevir not only inhibits the main protease (Mpro) and unexpectedly the RNA-dependent RNA polymerase (RdRp) but also modulates host immune responses. Our results thus reveal the possible anti-SARS-CoV-2 mechanism of simeprevir and highlight the translational potential of optimizing simeprevir as a therapeutic agent for managing COVID-19 and future outbreaks of CoV.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS (preprint)

Background: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists with CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 ( P  = 0.03) for clinical model, and 0.69 ( P  = 0.008) or 0.82 ( P  = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS (preprint)

Objectives: To develop and validate a CT radiomics signature for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS).Methods: This two-center retrospective study enrolled 115 laboratory-confirmed COVID-19 patients with 1127 lesions and 435 non-COVID-19 pneumonia patients with 842 lesions. In study 1, a radiomics signature and a clinical model was developed and validated in the training and internal validation cohorts (patient/lesion [n] = 379/1325, n = 131/505) for identifying COVID-19 pneumonia. In study 2, the developed radiomics signature was tested in another independent cohort including all viral pneumonia (n = 40/139), compared with clinical model and CO-RADS approach. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results: Twenty-three texture features were selected to construct the radiomics model. Radiomics model outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the internal validation cohort. Radiomics model also performed better in the testing cohort to distinguish COVID-19 from other viral pneumonia with an AUC of 0.96 compared with 0.75 (P=0.007) for clinical model, and 0.69 (P=0.002) or 0.82 (P=0.04) for two trained radiologists using CO-RADS approach. The sensitivity and specificity of radiomics model can be improved to 0.90 and 1.00. The DCA confirmed the clinical utility of radiomics model. Conclusions: The proposed radiomics signature outperformed clinical model and CO-RADS approach for diagnosing COVID-19, which can facilitate rapid and accurate detection of COVID-19 pneumonia.