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Background: Acute-on-chronic liver failure (ACLF) is a comprehensive syndrome characterized by an acute deterioration of liver function and high short-term mortality rates in patients with chronic liver disease. Whether plasma soluble urokinase plasminogen activator receptor (suPAR) is a suitable biomarker for the prognosis of patients with ACLF remains unknown.Method: A prospective cohort of 282 patients with ACLF from three hospitals in China was included. 88.4% of the group was hepatitis B virus-related ACLF (HBV-related ACLF). Cox regression was used to assess the impact of plasma suPAR and other factors on 30- and 90-day mortality. Reactive oxygen species (ROS) production were detected to explore the role of suPAR in regulating neutrophil function in HBV-related ACLF.Result: There was no difference in plasma suPAR levels between HBV-related and non-HBV-related ACLF. Patients with clinical complications had higher suPAR levels than those without these complications. A significant correlation was also found between suPAR and prognostic scores, infection indicators and inflammatory cytokines. Cox's regression multivariate analysis identified suPAR ≥ 14.7 ng/mL as a predictor for both day 30 and 90 mortality (Area under the ROC curve: 0.751 and 0.742 respectively), independent of the MELD and SOFA scores in patients with ACLF. Moreover, we firstly discovered suPAR enhanced neutrophil ROS production under E.coli stimulation in patients with HBV-related ACLF.Conclusions: suPAR was a useful independent biomarker of short-term outcomes in patients with ACLF and might play a key role in the pathogenesis. Trial registration CNT, NCT02965560. [ABSTRACT FROM AUTHOR]

Recently, long noncoding RNAs (lncRNAs) have attracted great attention from researchers. LncRNAs are non-protein-coding RNAs of more than 200 nucleotides in length. Multiple studies have been published on the relationship between lncRNA expression and the progression of human diseases. LncRNA small nucleolar RNA host gene 4 (SNHG4), a member of the lncRNA SNHG family, is abnormally expressed in a variety of human diseases, including gastric cancer, renal cell carcinoma, glioblastoma, neuroblastoma, prostate cancer, colorectal cancer, osteosarcoma, cervical cancer, liver cancer, lung cancer, non-small-cell lung cancer, neonatal pneumonia, diabetic retinopathy, neuropathic pain, acute cerebral infarction, acute myeloid leukaemia, and endometriosis. In this paper, the structure of SNHG4 is first introduced, and then studies in humans, animal models and cells are summarized to highlight the expression and function of SNHG4 in the above diseases. In addition, the specific mechanism of SNHG4 as a competing endogenous RNA (ceRNA) is discussed. The findings indicate that SNHG4 can be used as a biomarker for disease prognosis evaluation and as a potential target for disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

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A fast local neighbourhood search (FLNS) algorithm is proposed in this paper to minimise the total flow time in the no-wait flow shop scheduling problem, which is known to be NP-hard for more than two machines. In this work, an unscheduled job sequence is constructed firstly according to the total processing time and standard deviation of jobs on the machines. This job sequence is undergone an initial optimisation using basic neighbourhood search algorithm. Then, an innovative local neighbourhood search scheme is designed to search for the partial neighbourhood in each iterative processing and calculate the neighbourhood solution with an objective increment method. This not only improves the solution quality significantly, but also speeds up the convergence of the solution of the algorithm. Moreover, a probabilistic acceptance criterion is adopted to help our method escape from the local optima. Based on Taillard’s benchmarks, the experimental results show that the proposed FLNS algorithm is superior to major existing algorithms (IHA, IBHLS, GA-VNS and DHS) in terms of both quality and robustness, and can provide best upper bounds. The in-depth statistical analysis demonstrates that the promising performance of our proposed algorithm is also statistically significant. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. Methods: HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. Results: Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. Conclusions: The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction. [ABSTRACT FROM AUTHOR]

Interferon-alpha (IFN-α) and nucleot(s)ide analogs (NAs) are first-line drugs for the treatment of chronic hepatitis B virus (HBV) infections. Generally, NAs target the reverse transcription of HBV pregenomic RNA, but they cannot eliminate covalently-closed-circular DNA (cccDNA). Although effective treatment with NAs can dramatically decrease HBV proteins and DNA loads, and even promote serological conversion, cccDNA persists in the nucleus of hepatocytes due to the lack of effective anti-cccDNA drugs. Of the medications currently available, only IFN-α can potentially target cccDNA. However, the clinical effects of eradicating cccDNA using IFN-α in the hepatocytes of patients with HBV are not proficient as well as expected and are not well understood. Herein, we review the anti-HBV mechanisms of IFN-α involving cccDNA modification as the most promising approaches to cure HBV infection. We expect to find indications of promising areas of research that require further study to eliminate cccDNA of HBV in patients. [ABSTRACT FROM AUTHOR]