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Abstract Purpose Early detection of lifestyle factors, skin and hair color, circulating parameters, and metabolic comorbidities is crucial for personalized prevention and treatment of early age-related macular degeneration (AMD). This study aimed to assess the relationships between genetically predicted comprehensive risk factors and early AMD. Methods and results Publicly available genome-wide association study (GWAS) data were utilized to identify genetic variants significantly associated with each trait. We applied a Bonferroni-corrected significance level of P

Abstract Background Despite emerging evidence linking blood cell indices (BCIs) to sepsis mortality, the inconsistency of observational studies obscures the clarity of these associations. This study aims to clarify the causal influence of BCIs on 28-day mortality rates in sepsis patients. Methods Utilizing univariable and multivariable Mendelian randomization (MR) analyses, we examined the impact of BCIs on sepsis mortality by analyzing data from extensive genome-wide association studies. The inverse-variance weighted (IVW) method was our primary analytic tool, complemented by several robustness checks to mitigate pleiotropy, including weighted median, mode-based estimates, MR-Egger regression, and MR-PRESSO. Subsequently, we conducted a retrospective study to further explore the correlation between platelet indices and 28-day mortality of sepsis using real-world data. Results Our findings highlight a significant causal relationship between platelet distribution width (PDW) and 28-day mortality in sepsis, with the univariable Mendelian randomization approach yielding an odds ratio of 1.12 (95% CI, 1.06–1.26; P

BackgroundAcetaminophen is a commonly used medication, yet its recommendation for patients with comorbid conditions of gout and hypertension is contradictory, and the impact of its usage on clinical outcomes in real-world practical settings remains uncertain. The aim of this study was to investigate the association between acetaminophen administration and clinical outcomes in critically ill patients with gout and hypertension, utilizing real-world data.MethodsA retrospective cohort study was conducted based on the MIMIC-IV (Medical Information Mart in Intensive Care-IV) database. Adult critically ill patients with gout and hypertension were included in the analysis. The exposure was acetaminophen use during ICU stay. The primary outcome was in-hospital mortality. The secondary endpoints were frequent hospitalization, 30-day, and 60-day all-cause mortality, and incidence of hypertensive emergencies. Propensity score matching (PSM) was conducted at a 1:1 ratio. Multivariable analyses were used to adjust for confounders.ResultsThe pre-matched and propensity score-matched cohorts included 2448 and 1012 patients, respectively. In the PSM analysis, in-hospital mortality was 9.7% (49/506) in the acetaminophen use group and 12.1% (61/506) in the no use group. Acetaminophen use was associated with a decrease in-hospital mortality (hazard ratio [HR], 0.62; 95% CI, 0.41–0.92; P = 0.018). In terms of secondary endpoints, after PSM, there was no statistically significant difference for both 30-day and 60-day all-cause mortality reductions in the acetaminophen use group, and HRs were 0.78 (95% CI 0.55–1.11; P = 0.175), and 0.75 (95% CI 0.55–1.02; P = 0.069), respectively. According to the analysis of dosage and treatment group, the use of APAP within the dosage range of 2–4 g and within 3–5 days of treatment significantly reduced the mortality rate of the entire cohort and PSM cohort, with statistical differences. Subgroup analysis demonstrated that lower in-hospital mortality was consistent across different baselines (age, gender, BMI, liver disease, and renal disease), with no interactions in all subgroups (interaction p-values >0.05), thereby affirming the robustness and reliability of the findings.ConclusionAcetaminophen use was associated with lower in-hospital mortality in critically ill patients with gout and hypertension. Prospective studies are needed to verify this finding.

BackgroundSerum 25-hydroxyvitamin D level is associated with erectile dysfunction (ED) in observational studies. However, whether there is a causal association between them remains uncertain.ObjectiveConduct a two-sample Mendelian randomization (MR) analysis to investigate the causal effect between serum 25-hydroxyvitamin D level and ED risk.MethodGenome-wide association study (GWAS) data of serum 25-hydroxyvitamin D levels comprising 6,896,093 single nucleotide polymorphisms (SNP) from 496,949 people of European ancestry were regarded as exposure for the MR analysis. Additional GWAS data involving 9,310,196 SNPs of 6,175 European ED cases and 217,630 controls were used as outcome data. The MR-Egger, inverse variance weighted (IVW) method, weighted median, simple mode, and weighted mode were employed to evaluate causal effects, among which IVW was the primary MR analysis method. The stability of the MR analysis results was confirmed by a heterogeneity test, a horizontal pleiotropy test, and the leave-one-out method.ResultThere were 103 SNPs utilized as instrumental variables (p < 5 × 10−8). The results of MR analysis showed no causal effects of serum 25(OH) D concentration on ED risks (IVW; OR = 0.9516, 95% CI = 0.7994 to 1.1328, p = 0.5772). There was no heterogeneity and pleiotropy in the statistical models.ConclusionThe present MR study did not support a causal association for genetically predicted serum 25-hydroxyvitamin D concentration in the risk of ED in individuals of European descent.

Abstract The influence maximization problem is one of the most fundamental topics in social networks. However, most existing studies have focused on non-attributed networks, neglecting the consideration of users’ properties during information propagation. Additionally, specific scenarios may involve external queries that target a particular subset of users, which has not been adequately addressed in prior research. To address these limitations, this study first formulates the customized influence maximization (CIM) problem in the context of attributed social networks. The node score and influence probability are derived by fully considering the user’s attributes and the external queries. Then, we develop two algorithms to identify a group of most influential nodes in CIM. The first is a heuristic algorithm based on discounted degree, which is able to find relatively high-quality solutions in a short time. The second is a meta-heuristic algorithm, which makes several adjustments to the original ant colony algorithm to make it efficient to the CIM problem. Specifically, multiple CIM-related heuristics are derived, and a heuristic adaptation strategy is designed to automatically assign the heuristic information to ants according to the search environments and stages. Extensive experiments show the promising performance of our proposed algorithms in terms of accuracy, efficiency, and robustness.

Abstract Background Increasing evidence suggests a vital role of the pre-metastatic niche in the formation of distant metastasis of many cancers. However, how the pre-metastatic niche is formed and promotes pulmonary metastasis of hepatocellular carcinoma (HCC) remains unknown. Methods Orthotopic liver tumor models and RNA-Seq were used to identify dysregulated genes in the pre-metastatic lung. Il1b knockout (Il1b −/− ) mice and lentivirus-mediated gene knockdown/overexpression were utilized to demonstrate the role of interleukin 1 beta (IL-1β)/serum amyloid A3 (SAA3) in the pre-metastatic niche formation and pulmonary metastasis. The potential molecular mechanisms were investigated by RNA-Seq, real-time quantitative PCR (qPCR), western blotting, immunohistochemistry (IHC), flow cytometry, luciferase reporter assay, double immunofluorescent staining and H&E staining. Results Accumulation of myeloid cells and upregulation of IL-1β were observed in the pre-metastatic lung of orthotopic liver tumor models. Myeloid cells accumulation and pulmonary metastasis were suppressed in Il1b −/− mice and Il1r1-silencing mice. Mechanistically, SAA3 and matrix metallopeptidase 9 (MMP9) were identified as potential downstream targets of IL-1β. Overexpression of SAA3 in the lungs of Il1b −/− mice restored myeloid cells accumulation and pulmonary metastasis of the orthotopic HCC xenografts. Moreover, alveolar macrophages-derived IL-1β dramatically enhanced SAA3 expression in alveolar epithelial cells in an NF-κB dependent manner and increased MMP9 levels in an autocrine manner. Furthermore, SAA3 recruited myeloid cells to the lung without affecting the expression of MMP9 in myeloid cells. Conclusions Our study suggests a key role of pulmonary IL-1β and SAA3 in creating a permissive lung pre-metastatic niche by enhancing MMP9 expression and recruiting myeloid cells, respectively, thus promoting pulmonary metastasis of HCC.

Background and aims: There is currently no single model for predicting Wilson's disease (WD). We aimed to create a nomogram using daily clinical parameters to improve the accuracy of WD diagnosis in patients with abnormal liver function. Methods: Between July 2016 and December 2020, we identified 90 WD patients with abnormal liver function who had homozygous or compound heterozygous mutations in the ATP7B gene. The control group included 128 patients with similar liver function but no WD during the same time period. To create a nomogram, we screened potential predictive variables using the least absolute shrinkage and selection operator model and multivariate logistic regression. Results: We developed a nomogram for screening for WD based on six predictive factors: serum copper, direct bilirubin, uric acid, cholinesterase, prealbumin, and reticulocyte percentage. In the training cohort, the area under curve (AUC) of the nomogram reached 0.967 (95% confidence interval (CI) 0.946–0.988), while the area under the precision-recall curve was 0.961. Based on the optimal cutpoint of 213.55, our nomogram performed well, with a sensitivity of 96% and a specificity of 87%. In the validation cohort, the AUC of the nomogram was as high as 0.991 (95% CI 0.970–1.000). Conclusions: We developed a nomogram that can predict the risk of WD prior to the detection of serum ceruloplasmin or urinary copper, greatly increasing screening efficiency for patients with abnormal liver function.

Abstract Background To explore the correlation of tumor necrosis factor-α-induced protein 8-like protein 3 (TIPE3) expressions in colorectal cancer (CRC) with tumor-immune infiltration and patient prognosis. Methods Formalin-fixed paraffin-embedded tumor samples from CRC patients (n = 110) were used in this study. Immunohistochemistry staining of TIPE3 and three prognostic immune biomarkers (CD8, CD20, and CD66b) was conducted in the tumor tissues and adjacent normal tissues. A Cox regression analysis of univariate and multivariate variables was performed to assess the correlation between TIPE3 and patient prognosis. Result We found that TIPE3 was mainly expressed in the cytoplasm, with a small amount in the nucleus. The expression of TIPE3 in tumor tissues is significantly higher than in adjacent normal tissues, and it is significantly correlated with the survival rate of patients in tumor tissues (p = 0.0038) and adjacent normal tissues (p

Abstract Document clustering has long been an important research direction in intelligent system. When being applied to process Chinese documents, new challenges were posted since it is infeasible to directly split the Chinese documents using the whitespace character. Moreover, many Chinese document clustering algorithms require prior knowledge of the cluster number, which is impractical to know in real-world applications. Considering these problems, we propose a general Chinese document clustering framework, where the main clustering task is fulfilled with an adaptive encoding-based evolutionary approach. Specifically, the adaptive encoding scheme is proposed to automatically learn the cluster number, and novel crossover and mutation operators are designed to fit this scheme. In addition, a single step of K-means is incorporated to conduct a joint global and local search, enhancing the overall exploitation ability. The experiments on benchmark datasets demonstrate the superiority of the proposed method in both the efficiency and the clustering precision.

BackgroundDespite accumulating evidence revealing that Glucose-6-phosphate dehydrogenase (G6PD) is highly expressed in many tumor tissues and plays a remarkable role in cancer tumorigenesis and progression, there is still a lack of G6PD pan-cancer analysis. This study was designed to analyze the expression status and prognostic significance of G6PD in pan-cancer.MethodsG6PD expression data were obtained from multiple data resources including the Genotype-Tissue Expression, the Cancer Genome Atlas, and the Tumor Immunity Estimation Resource. These data were used to assess the G6PD expression, prognostic value, and clinical characteristics. The ESTIMATE algorithms were used to analyze the association between G6PD expression and immune-infiltrating cells and the tumor microenvironment. The functional enrichment analysis was also performed across pan-cancer. In addition, the GDSC1 database containing 403 drugs was utilized to explore the relationship between drug sensitivity and G6PD expression levels. Furthermore, we also performed clinical validation and in vitro experiments to further validate the role of G6PD in hepatocellular carcinoma (HCC) cells and its correlation with prognosis. The R software was used for statistical analysis and data visualization.ResultsG6PD expression was upregulated in most cancers compared to their normal counterparts. The study also revealed that G6PD expression was a prognostic indicator and high levels of G6PD expression were correlated with worse clinical prognosis including overall survival, disease-specific survival, and progression-free interval in multiple cancers. Furthermore, the G6PD level was also related to cancer immunity infiltration in most of the cancers, especially in KIRC, LGG, and LIHC. In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. Drug sensitivity analysis showed that IC50 values of most identified anti-cancer drugs were positively correlated with the G6PD expression. Notably, in vitro functional validation showed that G6PD knockdown attenuated the phenotypes of proliferation in HCC.ConclusionG6PD may serve as a potential prognostic biomarker for cancers and may be a potential therapeutic target gene for tumor therapy.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

Hepatitis A virus cellular receptor (HAVCR1) is a type-1 integral membrane glycoprotein that plays a key role in immunity and renal regeneration and is abnormally expressed in various tumor types. Nonetheless, the function of HAVCR1 in pan-cancer remains unknown. In this study, we comprehensively analyzed the expression and promoter methylation level of HAVCR1 and assessed the immune cell infiltration, correlation between stromal and immune cell admixture, CD (Cluster of Differentiation) and HAVCR1 expression and prognostic value of HAVCR1 mRNA expression in Liver hepatocellular carcinoma (LIHC) and Pancreatic adenocarcinoma (PAAD). Our results showed that HAVCR1 was overexpressed while the promoter methylation of HAVCR1 was decreased in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma. HAVCR1 was associated with increased infiltration of B cells, CD8 cells, macrophages, neutrophils and Dendritic cells in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma. HAVCR1 expression was positively correlated with the immune, stromal and estimate scores of Pancreatic adenocarcinoma and the stromal and estimate scores of Liver hepatocellular carcinoma. Furthermore, HAVCR1 expression was correlated with other immune molecules such as HHLA2 (Human endogenous retrovirus-H long terminal repeat-associating protein 2), CD44 and TNFRSF4 (TNF Receptor Superfamily Member 4) in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma. During Kaplan-Meier analysis, high HAVCR1 expression in Liver hepatocellular carcinoma and Pancreatic adenocarcinoma correlated with poor survival. A marginally significant p-value (p = 0.051) was obtained when the relationship between HAVCR1 expression in Liver hepatocellular carcinoma and prognosis was analyzed, attributed to the small sample size. Overall, we provided compelling evidence that HAVCR1 could be a prognostic and diagnostic marker for Liver hepatocellular carcinoma and Pancreatic adenocarcinoma.

Abstract Traffic Signal Control (TSC) is a fundamental task in modern intelligent transport systems. TSC is often formulated as a bi‐level optimization problem, comprised by the signal timing at the upper level and the Dynamic User Equilibrium (DUE) traffic assignment at the lower level. Since DUE is non‐convex, existing methods either formulate approximation models or adopt traffic simulators. However, approximation models may oversimplify the practical situations, while traffic simulators are usually time‐consuming. This paper formulates a vehicle‐based DUE (vDUE) model and proposes an agile method that can simultaneously maintain the computational simplicity and the traffic dynamics for the traffic assignment. Further, an agile TSC system is built by combining the vDUE at the lower level for the traffic assignment with an adaptive differential evolution algorithm at the upper level for the signal timing optimization. To enhance the effectiveness of optimization, the TSC problem formulation is also improved to make it better characterize the practical requirements. In the experiments undertaken, comparisons of different TSC methods are carried out on both real‐world and synthetic transportation networks. The experimental results validate the effectiveness of the proposed agile TSC system in various traffic situations.

Abstract 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.

Abstract 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

BackgroundThe host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique.MethodsThe training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA.ResultsTwo immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA.ConclusionsThe identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.

Hepatitis B virus-related acute liver failure (HBV-ALF) is a common type of liver failure, associated with high short-term mortality and morbidity rates. However, the immune landscape of HBV-ALF and its correlation with cell death are currently unknown. Based on 3 Gene Expression Omnibus data sets, infiltrated immune cells were quantified by single-sample gene set enrichment analysis method. The expression levels of immune genes and the abundance of immune cells in liver failure were compared with those in normal liver. The enrichment scores of cell death gene sets from Kyoto Encyclopedia of Genes and Genomes (KEGG) were calculated by gene set variation analysis method, and a protein-protein interaction (PPI) network was constructed using Cytoscape. Besides 21 differentially expressed immune genes, we identified 11 types of differentially infiltrated immune cells in HBV-ALF compared with normal liver. Enriched pathways of these immune genes mainly consisted of chemokine receptors, chemokine binding, interleukin-10 signaling, and TNFs bind their physiological receptors by Reactome pathway analysis. In addition, the enrichment scores of apoptosis and necroptosis pathway instead of autophagy and ferroptosis were increased in liver failure compared with normal liver. PPI network and gene cluster analysis of immune genes and apoptosis and necroptosis genes suggested that hub genes were mainly related to immune response and apoptosis. In summary, our study offers a conceptual framework to understand the immune landscape of HBV-ALF, which might help to improve prognosis.

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.

BackgroundBiliary atresia (BA) is a severe cholangiopathy of early infancy that destroys cholangiocytes, obstructs ductular pathways and if left untreated, culminates to liver cirrhosis. Mechanisms underlying the etiological heterogeneity remain elusive and few studies have attempted phenotyping BA. We applied machine learning to identify distinct subtypes of BA which correlate with the underlying pathogenesis.MethodsThe BA microarray dataset GSE46995 was downloaded from the Gene Expression Omnibus (GEO) database. Unsupervised hierarchical cluster analysis was performed to identify BA subtypes. Then, functional enrichment analysis was applied and hub genes identified to explore molecular mechanisms associated with each subtype. An independent dataset GSE15235 was used for validation process.ResultsBased on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes. Functional analysis of Subtype 1 correlated with Fc Gamma Receptor (FCGR) activation and hub gene FCGR2A, suggesting an autoimmune response targeting bile ducts. Subtype 2 was associated with immune receptor activity, cytokine receptor, signaling by interleukins, viral protein interaction, suggesting BA is associated with viral infection. Subtype 3 was associated with signaling and regulation of expression of Robo receptors and hub gene ITGB2, corresponding to embryonic BA. Moreover, Reactome pathway analysis showed Neutrophil degranulation pathway enrichment in all subtypes, suggesting it may result from an early insult that leads to biliary stasis.ConclusionsThe classification of BA into different subtypes improves our current understanding of the underlying pathogenesis of BA and provides new insights for future studies.

Low-density lipoprotein cholesterol (LDL-C) is a well-known risk factor for coronary heart disease but protects against infection and sepsis. We aimed to disclose the exact association between LDL-C and severe 2019 novel coronavirus disease (COVID-19). Baseline data were retrospectively collected for 601 non-severe COVID-19 patients from two centers in Guangzhou and one center in Shenzhen, and patients on admission were medically observed for at least 15 days to determine the final outcome, including the non-severe group (n = 460) and the severe group (severe and critical cases) (n = 141). Among 601 cases, 76 (12.65%) received lipid-lowering therapy; the proportion of patients taking lipid-lowering drugs in the severe group was higher than that in the non-severe group (22.7 vs. 9.6%). We found a U-shaped association between LDL-C level and risk of severe COVID-19 using restricted cubic splines. Using univariate logistic regression analysis, odds ratios for severe COVID-19 for patients with LDL-C ≤1.6 mmol/L (61.9 mg/dL) and above 3.4 mmol/L (131.4 mg/dL) were 2.29 (95% confidence interval 1.12–4.68; p = 0.023) and 2.02 (1.04–3.94; p = 0.039), respectively, compared to those with LDL-C of 2.81–3.40 mmol/L (108.6–131.4 mg/dL); following multifactorial adjustment, odds ratios were 2.61 (1.07–6.37; p = 0.035) and 2.36 (1.09–5.14; p = 0.030). Similar results were yielded using 0.3 and 0.5 mmol/L categories of LDL-C and sensitivity analyses. Both low and high LDL-C levels were significantly associated with higher risk of severe COVID-19. Although our findings do not necessarily imply causality, they suggest that clinicians should pay more attention to lipid-lowering therapy in COVID-19 patients to improve clinical prognosis.

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.

The field of visual search has gained significant attention recently, particularly in the context of web search engines and e-commerce product search platforms. However, the abundance of web images presents a challenge for modern image retrieval systems, as they need to find both relevant and diverse images that maximize users' satisfaction. In response to this challenge, we propose a non-dominated visual diversity re-ranking (NDVDR) method based on the concept of Pareto optimality. To begin with, we employ a fast binary hashing method as a coarse-grained retrieval procedure. This allows us to efficiently obtain a subset of candidate images for subsequent re-ranking. Fed with this initial retrieved image results, the NDVDR performs a fine-grained re-ranking procedure for boosting both relevance and visual diversity among the top-ranked images. Recognizing the inherent conflict nature between the objectives of relevance and diversity, the reranking procedure is simulated as the analytical stage of a multi-criteria decision-making process, seeking the optimal tradeoff between the two conflicting objectives within the initial retrieved images. In particular, a non-dominated sorting mechanism is devised that produces Pareto non-dominated hierarchies among images based on the Pareto dominance relation. Additionally, two novel measures are introduced for the effective characterization of the relevance and diversity scores among different images. We conduct experiments on three popular real-world image datasets and compare our re-ranking method with several state-of-the-art image search re-ranking methods. The experimental results validate that our re-ranking approach guarantees retrieval accuracy while simultaneously boosting diversity among the top-ranked images. [ABSTRACT FROM AUTHOR]

Demand side management is an efficient way of reducing the grid fluctuation and enhancing the net profit in the smart grid. As the increasing complexity of the real-world applications, e.g., NP-hard and non-polynomial problems, a distributed coevolution algorithm is proposed to cope with this challenge. In the proposed algorithm, the management system is modeled as a hierarchical structure. The user side locates at the lowest level and upper levels represent the system operators. The system operators manage users' energy consumption behavior through the real-time pricing strategy. The genetic algorithm and particle swarm optimization are modified to play the role of the smart scheduler and the smart pricing generator. The end users conduct appliance commitment using the smart scheduler which considers the users' comfort level and the electricity cost. The decision of the lower level depends directly on the real-time pricing strategy released by its adjacent upper level. The real-time pricing is developed by the smart pricing generator which considers the net profit and the grid fluctuation. Three types of experiments are conducted on a distributed platform to investigate and ascertain the performance of the proposed algorithm. For the operator side, the experimental results indicate that the proposed algorithm improves the grid fluctuation, and enhances the net profit. As for the user side, it improves the comfort level and achieves budget saving.

Background and aims: Factors associated with abnormally elevated alpha-fetoprotein (AFP) in hepatitis B virus (HBV)-infected patients remain to be studied. We aimed to identify factors associated with elevated serum AFP in patients with non-hepatocellular carcinoma (HCC) and early-stage HCC and their influences on the performance of AFP for detecting early-stage HCC. Methods: This multicenter, retrospective study was conducted in 4401 patients with chronic HBV infection, including 3680 patients with non-HCC and 721 patients with early-stage HCC. Factors associated with elevated AFP were analyzed. Diagnostic performance of AFP for early-stage HCC were compared among groups through area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: When analyzed by multivariate logistic regression, antiviral therapy was negatively associated with elevated AFP, while hepatitis B e antigen (HBeAg) and aspartate aminotransferase (AST) > 1× upper limit of normal (ULN) were positively associated with elevated AFP both in patients with non-HCC and early-stage HCC (all p < 0.05). The AUCs of AFP for detecting early-stage HCC in patients with antiviral therapy, HBV DNA (−), alanine aminotransferase (ALT) ≤ 1× ULN, and AST ≤ 1× ULN were significantly higher compared to those in non-antiviral therapy, HBV DNA (+), ALT > 1× ULN, and AST > 1× ULN groups, respectively. When categorizing patients into AST ≤ 1× ULN and > 1× ULN, AFP achieved the highest AUCs in patients with AST ≤ 1× ULN regardless of antiviral treatment (AUCs = 0.813 and 0.806, respectively). Furthermore, there were considerable differences in the cut-off values of AFP in detecting early-stage HCC in different subgroups when applying similar sensitivity and specificity. Conclusions: Antiviral therapy and serum AST might be used to help judge and select the specific cut-off values of serum AFP for HCC surveillance in different at-risk populations.

Background/Aims: The age-bilirubin-international normalized ratio-creatinine (ABIC) score, which is a predictive model commonly used for alcoholic hepatitis, has not yet been studied in acute-on-chronic hepatitis B liver failure (HBV-ACLF). We aimed to investigate the predictive value of the ABIC score in patients with HBV-ACLF. Methods: This retrospective study involved 398 patients diagnosed with HBV-ACLF, who were divided into a training cohort of 305 patients and a validation cohort of 93 patients. Univariate and multivariate Cox regression models were used to determine risk factors for mortality. Area under the receiver operating characteristic curve (AUC) was calculated to estimate and compare the predictive values of different prognostic scores. Results: The ABIC score was significantly higher in the death group of the training cohort than in its survival group. Independent risk factors for mortality identified by multivariate Cox analysis included blood urea nitrogen, ABIC score, and Chronic Liver Failure Consortium Organ Failure (CLIF-C OF) score. For predicting 1- and 3-month mortality, AUC was higher for the ABIC score than for the Model for End-stage Liver Diseases (MELD) score (0.732 vs. 0.653, P < 0.05, 0.695 vs. 0.619, P < 0.05, respectively), CLIF-C OF score (0.693, P=0.353, 0.656, P=0.341, respectively), and Child-Pugh score (0.675, P=0.189, 0.656, P=0.300, Respectively). Patients with ABIC score > 9.44 had reduced 1- and 3-month survival rates. Conclusion: ABIC score is superior to MELD score in predicting short-term survival in HBV-ACLF patients. ABIC score > 9.44 predicts high short-term mortality risk in HBV-ACLF patients.

The interferons (IFNs) are a primary defense against pathogens because of the strong antiviral activities they induce. IFNs can be classified into three groups: type I, type II and type III, according to their genetic, structural, and functional characteristics and their receptors on the cell surface. The type I IFNs are the largest group and include IFN-α, IFN-β, IFN-ε, IFN-ω, IFN-κ, IFN-δ, IFN-τ and IFN-ζ. The use of IFNs for the treatment of viral infectious diseases on their antiviral activity may become an important therapeutic option, for example, IFN-α is well known for the successful treatment of hepatitis B and C virus infections, and interest is increasing in the antiviral efficacy of other novel IFN classes and their potential applications. Therefore, in this review, we summarize the recent progress in the study of the biological activities of all the type I IFN classes and their potential applications in the treatment of infections with immunodeficiency virus, hepatitis viruses, and influenza viruses.