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Background: Despite unprecedented scientific progress that has been achieved over the years, there is no established microRNA-based model for predicting hepatocellular carcinoma (HCC) metastasis. To this end, we aimed to develop a simple model based on the expression of miRNAs to identify patients at high risk of metastatic HCC. Methods: HCC datasets with metastasis data were acquired from the Gene Expression Omnibus (GEO) database, and samples were randomly divided into training (n=169) and validation (n=72) groups. Based on the expression of miRNAs in the training group, we developed a predictive nomogram for metastatic HCC. We evaluated its performance using the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve and clinical impact curve analysis. Results: By applying the absolute shrinkage and selection operator regression (LASSO) and multivariate logistic regression, it has been found that the expressions of miR-30c, miR-185, and miR-323 were independent predictors of metastatic HCC. These miRNAs were used to construct a nomogram that yielded good performance in predicting metastasis in training (AUC 0.869 [95% CI 813-0.925], sensitivity 92.7%, specificity 57.8%) and validation groups (0.821 [CI 0.720-0.923], sensitivity 94.7%, specificity 60%). The calibration curve showed a good agreement between actual and predicted outcomes. Decision curve analysis showed a high clinical net benefit of nomogram predictions for our patients. Moreover, higher total scores of our nomogram were associated with dead patients. In addition, functional enrichment analysis showed that the predicted target genes of these 3 miRNAs correlated with tumor metastasis-associated terms, such as filopodium, and identified their related hub genes. Conclusions: Our easy-to-use nomogram could assist in identifying HCC patients at high risk of metastasis, which provides valuable information for clinical treatment.

Background: Previous studies have shown that blockade of programmed cell death-1 (PD-1) or programmed cell death- Ligand-1 (PD-L1) may improve anti-HBV responses in vitro and woodchuck models. However, clinical data was limited on the role of PD-1 inhibitors in patients with chronic HBV infection. We aimed to observe the changes ofserum HBsAgand HBV-DNA levels in cancer patients under PD-1 inhibitor-based therapies and identify the risk factors associated with HBsAg fluctuations and HBV reactivation (HBVr).Methods: A retrospective study including HBsAg-positive cancer patients who received PD-1 inhibitors between July 2019 and December 2022 was undertaken. Serum HBsAg and HBV-DNA level changes, the incidence of HBsAg loss, HBVr and immune-related adverse events (irAEs) were investigated. Univariable and multivariable analysis were performed to identify the risk factors for significant HBsAg fluctuations and HBVr.Results: 121 patients were eligible to the study. With concurrent use of antiviral agents, patients with baseline HBsAg within [50 to 500) IU/ml mostly had HBsAg decrease, viral replication was inhibited effectively. HBsAg loss, HBVr and irAEs were developed in 6 patients (4.96%), 6 patients (4.96%) and 14 patients (11.57%), respectively. Multivariable analysis showed baseline HBsAg Conclusions: PD-1 inhibitor combined with NAs may exert therapeutic potential for chronic HBV infection in cancer patients, attention also should be payed to its safety.

Background Acute-on-chronic liver failure (ACLF) is a clinic syndrome with substantial high short-term mortality. It is very important to stratify patients according to prognosis to decide management strategy. This study aimed to formulate and validate a nomogram model based on blood lipoprotein for prediction of 3-month mortality in patients with hepatitis B virus (HBV)-related ACLF. Methods Data on 393 consecutive patients who were diagnosed as HBV-related ACLF at the Third Affiliated Hospital of Sun Yat-sen University between June 1, 2013, and February 1, 2015, were prospectively collected. Of these, 260 patients who were collected in an earlier period formed the training cohort for the development of nomogram, while 133 patients who were collected thereafter formed the validation cohort for confirming the performance of nomogram. Results Multivariate analysis showed that low density lipoprotein cholesterol (LDL-C), age, prothrombin time, and creatinine were independently associated with 3-month mortality of patients with HBV-related ACLF. Kaplan–Meier survival analysis revealed that the high LDL-C (LDL-C ≥ 1.0 mmol/L, cut-off value) was significantly associated with elevated overall survival (P Conclusion This study highlighted that reduction of serum LDL-C level was an independent risk factor for the survival in patients with HBV-related ACLF, and the nomogram based on serum LDL-C was an accurate and practical model for predicting the 3-month mortality in patients with this disease.

Aim. To determine whether a decrease in HBsAg to HBsAg levels < 0.05 IU/mL, 71 were eligible for inclusion in the analysis: 31 received finite NUC therapy, and 40 received indefinite NUC therapy. In the finite therapy group, 9 patients received no NUC consolidation therapy, 6 received short-term (1 year) consolidation. The outcome measures were alanine aminotransferase (ALT), total bilirubin, albumin, hepatitis B virus DNA, and HBsAg levels. Results. Baseline parameters and characteristics at the time when HBsAg levels had fallen to

Diagnosis of significant liver fibrosis is essential to facilitate the optimal treatment decisions and improve prognosis in patients with chronic hepatitis B (CHB). We aimed to evaluate the value of inflammatory indicators and construct a nomogram that effectively predicts significant liver fibrosis among CHB patients. 563 CHB patients from two centers in China from 2014 to 2019 were divided into three cohorts (development, internal validation, and independent validation cohorts), assigned into cases with significant fibrosis (liver fibrosis stages ≥2) and those without. Multiple biochemical and serological inflammatory indicators were investigated. Inflammatory indicators, Alanine aminotransferase (ALT) and aspartate aminotransferase (AST), were significantly associated with significant liver fibrosis in CHB patients but limited predictive performance, and then we combined them with prothrombin time activity percentage (PTA) and liver stiffness measurement (LSM) were identified by multivariate logistic regression analysis. Based on these factors, we constructed the nomogram with excellent performance. The area under the receiver operating characteristic curve (AUROC) for the nomogram in the development, internal validation, and independent validation cohorts were 0.860, 0.877, and 0.811, respectively. Our nomogram based on ALT and AST that had excellent performance in predicting significant fibrosis of CHB patients were constructed.

Background: Owing to lack of predictive models for Hepatocellular carcinoma (HCC) metastasis based on the expression of miRNAs, we aimed to develop a simple model for identification of HCC patients at high risk of metastasis. Results: HCC datasets with metastasis information were acquired from the Gene Expression Omnibus (GEO), and samples were randomly divided into training group (n=169) and testing group (n=72). Based on expression of miRNAs in the training group, we developed a predictive nomogram for HCC metastasis and evaluated its performance using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve and clinical impact curve analysis. We found that the expression of miR-30c, miR-185 and miR-323 in HCC correlated with metastasis by the least absolute shrinkage and selection operator regression (LASSO) method and multivariate logistic regression. Based on these three miRNAs, we generated the nomogram for predicting metastasis in the training group (AUC 0.869 [95% CI .813-0.925], sensitivity 80.5%, specificity 78.9%); in testing group (0.821 [0.770-0.872], 48.5%, 92.3%). The calibration curve showed a good agreement between actual observation and prediction by nomogram. The nomogram represented high clinical net benefits using decision curve. Moreover, total scores calculated by nomogram were higher in dead patients than that in alive patients. In addition, the predicted target genes of these 3 miRNAs correlated with tumor metastasis by functional enrichment analysis, such as filopodium and we identified their related hub genes. Conclusions: Our easy-to-use nomogram could assist in identifying HCC patients at high risk of metastasis, which offer valuable information for clinical treatment.

ObjectiveTo analyze the epidemiological history, clinical symptoms, laboratory testing parameters of patients with mild and severe COVID-19 infection, and provide a reference for timely judgment of changes in the patients’ conditions and the formulation of epidemic prevention and control strategies.MethodsA retrospective study was conducted in this research, a total of 90 patients with COVID-19 infection who received treatment from January 21 to March 31, 2020 in the Ninth People’s Hospital of Dongguan City were selected as study subject. We analyzed the clinical characteristics of laboratory-confirmed patients with COVID-19, used the oversampling method (SMOTE) to solve the imbalance of categories, and established Lasso-logistic regression and random forest models.ResultsAmong the 90 confirmed COVID-19 cases, 79 were mild and 11 were severe. The average age of the patients was 36.1 years old, including 49 males and 41 females. The average age of severe patients is significantly older than that of mild patients (53.2 years old vs 33.7 years old). The average time from illness onset to hospital admission was 4.1 days and the average actual hospital stay was 18.7 days, both of these time actors were longer for severe patients than for mild patients. Forty-eight of the 90 patients (53.3%) had family cluster infections, which was similar among mild and severe patients. Comorbidities of underlying diseases were more common in severe patients, including hypertension, diabetes and other diseases. The most common symptom was cough [45 (50%)], followed by fever [43 (47.8%)], headache [7 (7.8%)], vomiting [3 (3.3%)], diarrhea [3 (3.3%)], and dyspnea [1 (1.1%)]. The laboratory findings of patients also included leukopenia [13(14.4%)] and lymphopenia (17.8%). Severe patients had a low level of creatine kinase (median 40.9) and a high level of D-dimer. The median NLR of severe patients was 2.82, which was higher than that of mild patients. Logistic regression showed that age, phosphocreatine kinase, procalcitonin, the lymphocyte count of the patient on admission, cough, fatigue, and pharynx dryness were independent predictors of COVID-19 severity. The classification of random forest was predicted and the importance of each variable was displayed. The variable importance of random forest indicates that age, D-dimer, NLR (neutrophil to lymphocyte ratio) and other top-ranked variables are risk factors.ConclusionThe clinical symptoms of COVID-19 patients are non-specific and complicated. Age and the time from onset to admission are important factors that determine the severity of the patient’s condition. Patients with mild illness should be closely monitored to identify those who may become severe. Variables such as age and creatine phosphate kinase selected by logistic regression can be used as important indicators to assess the disease severity of COVID-19 patients. The importance of variables in the random forest further complements the variable feature information.

Objectives: The pathogenesis of heterogeneity in gastric cancer (GC) is not clear and presents as a significant obstacle in providing effective drug treatment. We aimed to identify subtypes of GC and explore the underlying pathogenesis.Methods: We collected two microarray datasets from GEO (GSE84433 and GSE84426), performed an unsupervised cluster analysis based on gene expression patterns, and identified related immune and stromal cells. Then, we explored the possible molecular mechanisms of each subtype by functional enrichment analysis and identified related hub genes.Results: First, we identified three clusters of GC by unsupervised hierarchical clustering, with average silhouette width of 0.96, and also identified their related representative genes and immune cells. We validated our findings using dataset GSE84426. Subtypes associated with the highest mortality (subtype 2 in the training group and subtype C in the validation group) showed high expression of SPARC, COL3A1, and CCN. Both subtypes also showed high infiltration of fibroblasts, endothelial cells, hematopoietic stem cells, and a high stromal score. Furthermore, subtypes with the best prognosis (subtype 3 in the training group and subtype A in the validation group) showed high expression of FGL2, DLGAP1-AS5, and so on. Both subtypes also showed high infiltration of CD4+ T cells, CD8+ T cells, NK cells, pDC, macrophages, and CD4+ T effector memory cells.Conclusion: We found that GC can be classified into three subtypes based on gene expression patterns and cell composition. Findings of this study help us better understand the tumor microenvironment and immune milieu associated with heterogeneity in GC and provide practical information to guide personalized treatment.

BackgroundOwing to lack of predictive models for HCC metastasis based on the expression of miRNAs, we aimed to develop a simple model for identification of HCC patients at high risk of metastasis. MethodsHCC datasets with metastasis information were acquired from the Gene Expression Omnibus (GEO), and samples were randomly divided into training group (n=169) and testing group (n=72). Based on expression of miRNAs in the training group, we developed a predictive nomogram for HCC metastasis and evaluated its performance using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve and clinical impact curve analysis.ResultsWe found that the expression of miR-30c, miR-185 and miR-323 in HCC correlated with metastasis by the least absolute shrinkage and selection operator regression (LASSO) method and multivariate logistic regression. Based on these three miRNAs, we generated the nomogram for predicting metastasis in the training group (AUC 0.869 [95% CI .813-0.925], sensitivity 80.5%, specificity 78.9%); in testing group (0.821 [0.770-0.872], 48.5%, 92.3%). The calibration curve showed a good agreement between actual observation and prediction by nomogram. The nomogram represented high clinical net benefits using decision curve and clinical impact curve analysis. Moreover, total scores calculated by nomogram were higher in dead patients than that in alive patients. In addition, the predicted target genes of these 3 miRNAs correlated with tumor metastasis by functional enrichment analysis, such as filopodium. Our easy-to-use nomogram could assist in identifying HCC patients at high risk of metastasis, which offer valuable information for clinical treatment.

The inappropriate cessation of nucleos(t)ide analog (NA) therapy may lead to acute exacerbations of chronic hepatitis B virus (HBV) infection, acute-on-chronic liver failure (ACLF), and even death. This study aims to elucidate the association between inappropriate NA cessation and prognosis in patients with HBV-ACLF. A total of 901 patients with ACLF were enrolled and stratified into inappropriate NA cessation and non-NA cessation group. Clinical characteristics and prognosis between the two groups were compared. The association between inappropriate NA cessation and the prognosis of patients with HBV-ACLF was evaluated using Cox proportional hazard models after propensity score matching (PSM). NA cessation was identified in 132 patients (NA cessation group), while 769 patients were triggered by other factors (non-NA cessation group). The 28- and 90-day liver transplant-free survival rates were higher in patients with non-NA cessation than in those with NAs cessation (78.3 % vs. 62.1 %, P

Background: Chronic hepatitis B patients with severe acute exacerbation is at progression stage of acute-on-chronic liver failure (ACLF). We aimed to develop and validate an accurate risk model to early identify the patients at high-risk of liver failure and predict patient’s survival. Methods: We selected the best variable combination using recursive feature elimination algorithm to develop and validate a multivariate logistic regression risk model as well as online application on cloud server from the training cohort with a total of 342 patients and two external cohorts with a sample size of 96 and 65 patients, respectively. Findings: An excellent prediction model called PATA model including four predictors, prothrombin time, age, total bilirubin and alanine aminotransferase could achieve AUROC of 0.959 (95% CI 0.941-0.977) in the development set and AUROC of 0.932 (95% CI 0.876-0.987) and 0.905 (95% CI 0.826-0.984) in two external validation cohorts, respectively. The C-index of the model was 0.720 (95% CI 0.675-0.765) in prognostic stratification of 90-days mortality for training set patients, while the end-stage liver disease score model had a C-index of 0.549 (95% CI 0.506-0.592). Interpretation: The highly-predictive risk model and easy-to-use online application can accurately predict the risk of ACLF and patient survival, which is expected to become a widely accepted prediction model and guide therapeutic options. Funding This work was supported by a grant for National Key RD National Natural Science Foundation of China; Major Science and Technology Projects in 13th Five-Year and Natural Science Foundation of Guangdong. Declaration of Interest: The authors declare that they have no competing interests. Ethical Approval: This study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University ([2015]2-206 No.1). This study was conducted according to the Declaration of Helsinki. All adult participants provided written informed consent.

Additional file 1: Supplementary Table 1. Gene ontology analysis of differentially expressed genes in HBV-HCC The original results of GO analysis DEGs, enrichment of BP (biological process), MF (molecular function), and CC (cellular component) by using DAVID with FDR

BACKGROUND: Respiratory epithelium expressing angiotensin-converting enzyme 2 (ACE2) is the entry for novel coronavirus (SARS-CoV-2), pathogen of the COVID-19 pneumonia outbreak, although a few recent studies have found different ACE2 expression in lung tissue of smokers. The effect of smoking on ACE2 expression and COVID-19 is still not clear. So, we did this research to determine the effect of smoking on ACE2 expression pattern and its relationship with the risk and severity of COVID-19. METHODS: The clinical data of COVID-19 patients with smoking and non-smoking were analyzed, and ACE2 expression of respiratory and digestive mucosa epithelia from smoker and non-smoker patients or healthy subjects were detected by immunohistochemical (IHC) staining. RESULTS: Of all 295 laboratory-confirmed COVID-19 patients, only 24 (8.1%) were current smokers with moderate smoking or above, which accounted for 54.2% of severe cases with higher mortality than non-smokers (8.3% vs. 0.4%, p = 0.018). Data analysis showed the proportion of smokers in COVID-19 patients was lower than that in general population of China (Z = 11.65, P < 0.001). IHC staining showed ACE2 expression in respiratory and digestive epithelia of smokers were generally downregulated. CONCLUSIONS: The proportion of smokers in COVID-19 patients was lower, which may be explained by ACE2 downregulation in respiratory mucosa epithelia. However, smoking COVID-19 patients accounted for a higher proportion in severe cases and higher mortality than for non-smoking COVID-19 patients, which needs to be noted.

Background Hepatocellular carcinoma (HCC) is a common cancer and the leading cause is persistent hepatitis B virus (HBV) infection. We aimed to identify some core genes and pathways for HBV-related HCC. Methods Gene expression profiles of GSE62232, GSE121248, and GSE94660 were available from Gene Expression Omnibus (GEO). The GSE62232 and GSE121248 profiles were the analysis datasets and GSE94660 was the validation dataset. The GEO2R online tool and Venn diagram software were applied to analyze commonly differentially expressed genes between HBV-related HCC tissues and normal tissues. Then, functional enrichment analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Gene and Genome (KEGG) as well as the protein-protein interaction (PPI) network was conducted. The overall survival rates and the expression levels were detected by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA). Next, gene set enrichment analysis (GSEA) was performed to verify the KEGG pathway analysis. Furthermore, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed to validate the levels of these three core genes in tumor tissues and adjacent non-tumor liver tissues from 12 HBV related HCC patients, HBV-associated liver cancer cell lines and normal liver cell lines, and HepG2 with p53 knockdown or deletion, respectively. Results Fifteen highly expressed genes associated with significantly worse prognoses were selected and CCNB1, CDK1, and RRM2 in the p53 signaling pathway were identified as core genes. GSEA results showed that samples highly expressing three core genes were all enriched in the p53 signaling pathway in a validation dataset (P < 0.0001). The expression of these three core genes in tumor tissue samples was higher than that in relevant adjacent non-tumor liver tissues (P < 0.0001). Furthermore, we also found that the above genes were highly expressed in liver cancer cell lines compared with normal liver cells. In addition, we found that the expression of these three core genes in p53 knockdown or knockout HCC cell lines was lower than that in negative control HCC cell lines (P < 0.05). Conclusions CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway and could be potential biomarkers and therapeutic targets for HBV-related HCC.

B cell response plays a critical role against SARS-CoV-2 infection. However, little is known about the diversity and frequency of the paired SARS-CoV-2 antigen-specific BCR repertoire after SARS-CoV-2 infection. Here, we performed single-cell RNA sequencing and VDJ sequencing using the memory and plasma B cells isolated from five convalescent COVID-19 patients, and analyzed the spectrum and transcriptional heterogeneity of antibody immune responses. Via linking BCR to antigen specificity through sequencing (LIBRA-seq), we identified a distinct activated memory B cell subgroup (CD11chighCD95high) had a higher proportion of SARS-CoV-2 antigen-labeled cells compared with memory B cells. Our results revealed the diversity of paired BCR repertoire and the non-stochastic pairing of SARS-CoV-2 antigen-specific immunoglobulin heavy and light chains after SARS-CoV-2 infection. The public antibody clonotypes were shared by distinct convalescent individuals. Moreover, several antibodies isolated by LIBRA-seq showed high binding affinity against SARS-CoV-2 receptor-binding domain (RBD) or nucleoprotein (NP) via ELISA assay. Two RBD-reactive antibodies C14646P3S and C2767P3S isolated by LIBRA-seq exhibited high neutralizing activities against both pseudotyped and authentic SARS-CoV-2 viruses in vitro. Our study provides fundamental insights into B cell response following SARS-CoV-2 infection at the single-cell level.

Background: The Melanoma Antigen Gene (MAGE) family is a large, highly conserved group of proteins that share a common MAGE homology domain. Multiple MAGEs are aberrantly expressed in a variety of cancers. However, the function of distinct MAGE genes in hepatocellular carcinoma (HCC) is largely unclear. Our study aimed to comprehensively analyze the MAGE family as prognostic and diagnostic markers for HCC. Methods: In this research, all HCC data were obtained from NCBI GEO DataSets, The Cancer Genome Atlas (TCGA) and our clinical center. UALCAN was used to reveal the expression profile of distinct MAGEs in HCC and further evaluate the association between the expression of MAGEs and the clinicopathological characteristics of HCC patients, including clinical stage, tumor grade. Kaplan-Meier Plotter (KM-Plot) was used to evaluate the correlation between MAGEs expression and the overall survival (OS) of HCC patients. qRT-PCR was used to detection the expression levels of MAGEs in HCC samples. cBioPortal was used to analyze the genetic alterations in MAGEs and their associations with OS of HCC patients. Gene Set Variation Analysis (GSVA) algorithm was used to do functional enrichment analysis of MAGE genes in HCC to reveal the molecular mechanisms of MAGEs functioned in HCC. Results: Our research showed that many MAGE genes were dysregulated in HCC and most of them were highly expressed. Among them, MAGEA1、MAGEC2、MAGED1、MAGED2、MAGEF1 and MAGEL2 were significantly correlated with clinical stage and differentiation of HCC patients. MAGED1、MAGED2、MAGEA6、MAGEA12、MAGEA10、MAGEB4、MAGEL2 and MAGEC3 had significant correlation with HCC prognosis. Further functional enrichment analysis suggested the dysregulated MAGEs may play important roles in signal transduction. Conclusions: Taken together, our research revealed that multiple MAGEs were dysregulated in HCC and they might play important roles in the development of HCC and can be exploited as useful biomarkers for diagnosis and treatment for HCC.

Background. Preventing liver fibrosis from progressing to cirrhosis and even liver cancer is a key step in the treatment of chronic hepatitis B (CHB). This study is aimed at constructing and validating a new nomogram for predicting significant liver fibrosis (S≥2) in CHB patients. Methods. The nomogram was based on a retrospective study of 252 CHB patients. The predictive accuracy and discriminative ability of the nomogram were evaluated by the area under receiver operating characteristic curve (AUROC), decision curves, and calibration curve compared with the fibrosis 4 score (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI). The results were validated using bootstrap resampling and an external set of 168 CHB patients. Results. A total of 420 CHB patients were enrolled based on liver biopsy results. Independent factors predicting significant liver fibrosis were laminin (LN), procollagen type III N-terminal peptide (PIIINP), and blood platelet count (PLT) in a multivariate analysis, and these factors were selected to construct the nomogram. The calibration curve for the probability of significant liver fibrosis showed optimal agreement between the prediction from the nomogram and actual observation. The prediction from the nomogram was more consistent with the results of liver biopsy than FIB-4 and APRI. The AUROC of the nomogram was higher than that of FIB-4 and APRI for predicting significant liver fibrosis. These results were confirmed in the validation set. Furthermore, the decision curve analysis suggested that the most net benefits were provided by the nomogram. Conclusions. We found the proposed nomogram resulted in a more accurate prediction of significant liver fibrosis in CHB patients and could provide the most net benefits. We recommend this noninvasive assessment for patients with liver fibrosis to avoid the risk of liver biopsy and earlier intervention to prevent the development of cirrhosis or liver cancer.

Fibulin-1, a component of the extracellular matrix (ECM), its prognostic, pathophysiologic and diagnostic role in hepatocellular carcinoma (HCC) is still unexplored. We first found that either Fibulin-1 messenger RNA (mRNA) or protein level was highly elevated in HCC tissues compared with normal tissues. Fibulin-1 correlated with poor overall survival, and it was an independent prognostic predictor (p = 0.001). Furthermore, Overexpression or inhibition of Fibulin-1 reduced or sensitized HCC cells to apoptotic signals, and Fibulin-1 silencing suppressed the ability of HCC cells to form tumors in vivo. Moreover, Fibulin-1 inhibited apoptosis via the Notch pathway while Fibulin-1 silencing had no obvious effect on p-MAPK, p-c-jun and p-stat3 expression, and both Mcl-1 and Bcl-xL are targets of Fibulin-1. Furthermore, the stromal and immune score was elevated in high Fibulin-1 tissues, and FBLN1 expression was associated with increased infiltrating macrophages using xCell, TIMER and TISDIB tool based on TCGA HCC database. Importantly, the circulating cell-free RNA (cfRNA) level of Fibulin-1 in the serum were significantly increased in patients with HCC compared with those in healthy controls, individuals with chronic hepatitis B and patients with HBV-induced liver cirrhosis. The area under receiver operating characteristic curves (AUC) was 0.791 for Fibulin-1, 0.640 for α-fetoprotein and 0.868 for the combination of the two tumor markers. Our findings indicate that Fibulin-1 may be a potential prognostic indicator, a promising serum biomarker and a therapeutic target in patients with HCC.

Background: Growing evidence indicates that activated hepatic stellate cells (aHSCs) play unexpected roles in regulating immune cells’ function during liver fibrosis. Macrophages feature with inducible plasticity according to the circumstances while patrol for potential pathogens. However, studies seldom investigate whether and how the aHSCs regulate the phenotype and function of macrophages during liver fibrosis. Methods: 96 patients with different stages of liver fibrosis were involved in our study. Metavir score system was used to evaluate the degree of fibrosis. The expression of hepatic CCL2 and CD163 were detected by immunohistochemistry, and the relationship among CD163, CCL2, and fibrosis scores were explored. We co-cultured the aHSCs and THP-1-derived M0-type macrophages (M0MФ) to observe whether aHSCs modulate the expression of CD163 on macrophages in vitro. Furthermore, we treated the M0MФ with aHSCs’ supernatant and investigated the production of CCL2 in aHSCs by ELISA and immunofluorescence assay. To explore whether CCL2/CCR2 axis plays a crucial role in macrophage phenotypic changes during liver fibrosis, we used recombinant CCL2 and its specific receptor antagonist. Moreover, we employed LX2 system to confirm our results. Results: We found that hepatic M2 macrophages (CD163+) infiltration increased in different stages of liver fibrosis (F0-1: 34.95±18.12; F2-3: 77.57±32.48; F4: 99.62±40.84, F0-1 vs. F2-3, P

BackgroundCoronavirus disease 2019 (COVID-19) has become a global health problem. We aim to investigate the changes in the results of viral nucleic acid tests on pharyngeal swabs and feces of patients with COVID-19 and CT imaging of lungs as the disease progresses.MethodsSeven patients with COVID-19 in the third affiliated hospital of Sun Yat-sen University Yuedong Hospital were retrospectively enrolled with clinical features, including imaging staging, and performance characteristics of viral nucleic acid test results of pharyngeal swabs and feces. The dynamic changes of these features were observed during hospitalization, and therapeutic effect and prognosis of patients were evaluated.ResultsThe results of seven cases with COVID-19 were positive for viral nucleic acid tests on pharyngeal swabs early after the onset of symptoms, and then turned negative; while the results of viral nucleic acid tests on feces were persistently positive in the mid-term clinical treatment and recovery period. And the viral nucleic acid test results were capricious in three cases. Pulmonary CT imaging showed characteristic changes in early, advanced and recovery phases.ConclusionThe application of viral nucleic acid detection and pulmonary CT imaging can be used for screening of suspected cases. Fecal nucleic acid test should be recommended as the reference of discharge standard, in order to minimize the risk of transmission from digestive tract.

Background Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) that began in Wuhan and rapidly spread globally. The speed and scope of spread of COVID-19 makes urgent of the defining clinical characteristics, serological and radiological changes of the affected patients. Method 7 patients with laboratory-confirmed COVID-19 who admitted to the Third affiliated hospital of Sun Yat-sen university Yuedong hospital from January 2020 to March 2020 were retrospectively enrolled and their clinical features, serological and radiological longitudinal changes were analyzed. Results Among the 7 patients, all (100%) had a clear epidemiological history. The most common symptoms were respiratory symptoms 6 (85.7%), and only 2 (28.6%) of the patients had fever at their first visit. The cohort included 4 (57.1%) common types and 3 (42.9%) severe types. Two (28.6%) common types patients developed to severe type in a short time. All of the 7 patients (100%) had abnormal liver function, normal renal function and normal procalcitonin. The detection time of specific antibody in 7 patients was 5~13d after symptoms. Before the specific antibody could be detected, the absolute value of lymphocytes decreased in 2 (28.6%) common type cases transferred to severe type cases accompanied with obvious progress in pulmonary imaging, and the phenomenon of decreased albumin and elevated globulin occurred in 6 patients (85.7%). The predominant pattern of lung lesions observed was bilateral (71.4%) and mainly near the pleura at the first diagnosis. Bilateral pulmonary involvement occurred in 6 cases (85.7%) during the course of disease. In 4 cases (57.1%) with obvious pulmonary lesions, the absolute value of lymphocytes decreased, albumin decreased and globulin increased during the course of the disease. Conclusion Serum specific antibodies can be detected within 2 weeks of onset. Close observation of the dynamic changes of absolute value of blood lymphocytes, serum albumin and globulin which were related to pulmonary imaging changes in patients will contribute to assessment of COVID-19.

Background Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) that began in Wuhan and rapidly spread globally. The speed and scope of spread of COVID-19 makes urgent of the defining clinical characteristics, serological and radiological changes of the affected patients.Method 7 patients with laboratory-confirmed COVID-19 who admitted to the Third affiliated hospital of Sun Yat-sen university Yuedong hospital from January 2020 to March 2020 were retrospectively enrolled and their clinical features, serological and radiological longitudinal changes were analyzed.Results Among the 7 patients, all (100%) had a clear epidemiological history. The most common symptoms were respiratory symptoms 6 (85.7%), and only 2 (28.6%) of the patients had fever at their first visit. The cohort included 4 (57.1%) common types and 3 (42.9%) severe types. Two (28.6%) common types patients developed to severe type in a short time. All of the 7 patients (100%) had abnormal liver function, normal renal function and normal procalcitonin. The detection time of specific antibody in 7 patients was 5~13d after symptoms. Before the specific antibody could be detected, the absolute value of lymphocytes decreased in 2 (28.6%) common type cases transferred to severe type cases accompanied with obvious progress in pulmonary imaging, and the phenomenon of decreased albumin and elevated globulin occurred in 6 patients (85.7%). The predominant pattern of lung lesions observed was bilateral (71.4%) and mainly near the pleura at the first diagnosis. Bilateral pulmonary involvement occurred in 6 cases (85.7%) during the course of disease. In 4 cases (57.1%) with obvious pulmonary lesions, the absolute value of lymphocytes decreased, albumin decreased and globulin increased during the course of the disease. Conclusion Serum specific antibodies can be detected within 2 weeks of onset. Close observation of the dynamic changes of absolute value of blood lymphocytes, serum albumin and globulin which were related to pulmonary imaging changes in patients will contribute to assessment of COVID-19.

Background The outbreak of coronavirus disease 2019 (COVID-19) has aroused global public health concerns. Multiple clinical features relating to host profile but not for virus have been identified as the risk factors for illness severity and/or the outcomes in COVID-19. Methods The clinical features obtained from a cohort of 195 laboratory-confirmed, nasopharynx-sampled patients with COVID-19 in Guangdong, China from January 13 to February 29, 2020 were enrolled to this study. The differences in clinical features among 4 groups (mild, moderate, severe, and critical) and between 2 groups (severe vs nonsevere) were compared using one-way analysis of variance and Student’s t test, respectively. Principal component analysis and correlation analysis were performed to identify the major factors that account for illness severity. Results In addition to the previously described clinical illness severity-related factors, including older age, underlying diseases, higher level of C-reactive protein, D-dimer and aspartate aminotransferase, longer fever days and higher maximum body temperature, larger number of white blood cells and neutrophils but relative less lymphocytes, and higher ratio of neutrophil to lymphocytes, we found that the initial viral load is an independent factor that accounts for illness severity in COVID-19 patients. Conclusions The initial viral load of severe acute respiratory syndrome coronavirus 2 is a novel virological predictor for illness severity of COVID-19.

BackgroundBecause there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19.MethodsIn this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance.ResultsThe training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846–.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790–.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit.ConclusionsOur nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

Background: With CRISPR-Cas9 genome editing tool in gene therapy, identifying aberrantly expressed genes is of great value across various cancer types. A large number of patients may benefit from molecular targeted gene therapy. The purpose of this study was to identify aberrantly expressed genes across various cancer type, analyze prospective mechanism and theirs correlation with survival outcomes. Results NCAPG was highly expressed in The Cancer Genome Atlas (TCGA) database, which includes the transcriptomes of 6647 cancer and 647 normal tissue samples from 16 cancer types. Furthermore, a predicted NCAPG overexpression rate was also observed at the protein level in 16 tumor types. Importantly, high NCAPG level was significantly associated with unfavorable survival not only in HCC, breast, lung or ovarian cancer with TCGA database but also in HCC with an extra Gene Expression Omnibus Datasets (GEO) dataset. The multivariate analyses demonstrated that NCAPG, TNM and Barcelona Clinic Liver Cancer staging were independent risk factors for mortality of patients with HCC. Moreover, functional and pathway enrichment analysis suggested that NCAPG was overrepresented in the pathways of cell cycle, cellular senescence, mismatch repair and p53 signaling pathway. By weighted gene co-expression network analysis (WGCNA), we identified NCAPG as a hub gene in the turquoise module mostly related to the survival time of HCC samples. Conclusion To our knowledge, this study represents the first comprehensive RNA-Seq analysis of several types of tumor revealing NCAPG as a promising molecular target and NCAPG overexpression may play important roles in carcinogenesis and progression of tumors through regulating tumor-related pathways, thereby broadening the understanding of the pathogenic mechanisms and highlighting the possibility of developing novel targeted therapeutics.

Background Severe cases of coronavirus disease 2019 (COVID-19) rapidly develop acute respiratory distress leading to respiratory failure, with high short-term mortality rates. At present, there is no reliable risk stratification tool for non-severe COVID-19 patients at admission. We aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. Methods SARS-CoV-2 infected patients from one center in Wuhan city and two centers in Guangzhou city, China were included retrospectively. All patients with non-severe COVID-19 during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. We compared the demographic, clinical, and laboratory data between severe and non-severe group. Based on baseline data, least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression model were used to construct a nomogram for risk prediction in the train cohort. The predictive accuracy and discriminative ability of nomogram were evaluated by area under the curve (AUC) and calibration curve. Decision curve analysis (DCA) and clinical impact curve analysis (CICA) were conducted to evaluate the clinical applicability of our nomogram. Findings The train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19 and 107 (28.76%) patients had one of the following basic disease, including hypertension, diabetes, coronary heart disease, chronic respiratory disease, tuberculosis disease. We found one demographic and six serological indicators (age, serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width (RDW), blood urea nitrogen, albumin, direct bilirubin) are associated with severe COVID-19. Based on these features, we generated the nomogram, which has remarkably high diagnostic accuracy in distinguishing individuals who exacerbated to severe COVID-19 from non-severe COVID-19 (AUC 0.912 [95% CI 0.846-0.978]) in the train cohort with a sensitivity of 85.71 % and specificity of 87.58% ; 0.853 [0.790-0.916] in validation cohort with a sensitivity of 77.5 % and specificity of 78.4%. The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. DCA and CICA further indicated that our nomogram conferred significantly high clinical net benefit. Interpretation Our nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19. And this risk stratification tool will enable better centralized management and early treatment of severe patients, and optimal use of medical resources via patient prioritization and thus significantly reduce mortality rates. The RDW plays an important role in predicting severe COVID-19, implying that the role of RBC in severe disease is underestimated.

BACKGROUND: Coronavirus disease (COVID-19) has affected more than 100 countries worldwide and the discharge criteria of patients with COVID-19 vary across different countries. In China, patients with two negative respiratory viral RNA tests taken at least one day apart can be discharged with no further quarantine required. Currently, PCR testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in fecal sample is not routinely performed. METHODS: We present a patient with COVID-19, whose respiratory swabs became negative but fecal sample remained positive for SARS-CoV-2 RNA. RESULTS: Stool sample collected on 27th of February was still positive for SARS-CoV-2 RNA, 24 days after the first negative respiratory swab. CONCLUSIONS: Based on the experience from the 2003 SARS epidemic, we recommend that fecal RNA testing of SARS-CoV-2 should be incorporated into the discharge criteria to minimize the risk of transmission from the gastrointestinal tract.

Background: Coronavirus disease 2019 (COVID-19) has spread outside the initial epicenter of Wuhan We compared cases in Guangzhou and Wuhan to illustrate potent