Comparison of different preoperative objective nutritional indices for evaluating 30-d mortality and complications after liver transplantation.

Background: The nutritional status is closely related to the prognosis of liver transplant recipients, but few studies have reported the role of preoperative objective nutritional indices in predicting liver transplant outcomes.

Aim: To compare the predictive value of various preoperative objective nutritional indicators for determining 30-d mortality and complications following liver transplantation (LT).

Methods: A retrospective analysis was conducted on 162 recipients who underwent LT at our institution from December 2019 to June 2022.

Risk Score Based on Two microRNAs as a Prognostic Marker of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network.

Purpose: Liver transplantation (LT) currently yields the best outcomes for hepatocellular carcinoma (HCC). However, tumor recurrence still occurs in some patients. Identifying markers that predict HCC recurrence after LT is an unmet medical need.

mTOR-Dependent Suppression of Remnant Liver Regeneration in Liver Failure After Massive Liver Resection in Rats.

Background And Aims: Massive hepatectomy often leads to fatal liver failure because of a small remnant liver volume. The aim of this study was to investigate the potential mechanisms leading to liver failure.

Methods: Sprague-Dawley rats had performed a sham operation, 85 % partial hepatectomy (PH) or 90 % PH, and all had free access to water with or without supplemented glucose.

Identifying and prioritizing disease-related genes based on the network topological features.

Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes.

Identifying functions of protein complexes based on topology similarity with random forest.

Elucidating the functions of protein complexes is critical for understanding disease mechanisms, diagnosis and therapy. In this study, based on the concept that protein complexes with similar topology may have similar functions, we firstly model protein complexes as weighted graphs with nodes representing the proteins and edges indicating interaction between proteins. Secondly, we use topology features derived from the graphs to characterize protein complexes based on the graph theory.

Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm.

In the post-genome era, one of the most important and challenging tasks is to identify the subcellular localizations of protein complexes, and further elucidate their functions in human health with applications to understand disease mechanisms, diagnosis and therapy. Although various experimental approaches have been developed and employed to identify the subcellular localizations of protein complexes, the laboratory technologies fall far behind the rapid accumulation of protein complexes. Therefore, it is highly desirable to develop a computational method to rapidly and reliably identify the subcellular localizations of protein complexes.

Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach.

A proteome-wide network approach was performed to characterize significant patterns of influenza A virus (IAV)-human interactions, and to further identify potentially valuable targets for prophylactic and therapeutic interventions. Topological analysis demonstrated a strong tendency for IAV to interplay with highly connected and central proteins located in sparsely connected sub-networks. Additionally, functional analysis based on biological process revealed a number of functional groups overrepresented for IAV interactions, in which regulation of cell death and apoptosis, and phosphorus metabolic process is the most highly enriched.

Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features.

In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance.