Integrating bioinformatics and experimental validation to unveil disulfidptosis-related lncRNAs as prognostic biomarker and therapeutic target in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis­related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC. METHODS: The data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan-Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman's rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2). RESULTS: We identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells. CONCLUSIONS: The DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.

Construction and evaluation of an aging-associated genes-based model for pancreatic adenocarcinoma prognosis and therapies.

Objectives: Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor. Despite extensive research, the precise role of aging-related genes in the initiation, microenvironment regulation, and progression of PAAD remains unclear.Methods: Patients with PAAD were selected from the International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA) cohorts and the cell senescence-associated genes were obtained from CellAge. ConsensusClusterPlus was utilized for cluster identification. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a prognosis prediction model.Results: We identified three clusters (C1, C2, and C3) based on aging-associated gene profiles. The C1 cluster had a shorter overall survival time, advanced clinical grades, lower immune ESTIMATE score, and tumor immune dysfunction and exclusion (TIDE) score than the C3 subgroup. Moreover, signaling pathways for cell cycle activation were enriched in the C1 cluster. We also identified eight hub genes and constructed a risk model. The high cellular senescence-related signature (CSRS) score subtype exhibited poor prognosis, advanced clinical grades, M2 macrophage infiltration, higher immune checkpoint gene expression, and lower immunotherapeutic benefits.Conclusion: Our risk score model shows high prediction accuracy and survival prediction ability in individual clinical prognosis and pre-immunotherapy evaluation.

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC.

Hepatocellular carcinoma (HCC), which has become one of the most significant malignancies causing cancer-related mortality, presents genetic and phenotypic heterogeneity that makes predicting prognosis challenging. Aging-related genes have been increasingly reported as significant risk factors for many kinds of malignancies, including HCC. In this study, we comprehensively dissected the features of transcriptional aging-relevant genes in HCC from multiple perspectives. We applied public databases and self-consistent clustering analysis to classify patients into C1, C2, and C3 clusters. The C1 cluster had the shortest overall survival time and advanced pathological features. Least absolute shrinkage and selection operator (LASSO) regression analysis was adopted to build the prognostic prediction model based on six aging-related genes (HMMR, S100A9, SPP1, CYP2C9, CFHR3, and RAMP3). These genes were differently expressed in HepG2 cell lines compared with LO2 cell lines measured by the mRNA expression level. The high-risk score group had significantly more immune checkpoint genes, higher tumor immune dysfunction and exclusion score, and stronger chemotherapy response. The results indicated that the age-related genes have a close correlation with HCC prognosis and immune characteristics. Overall, the model based on six aging-associated genes demonstrated great prognostic prediction ability.

Identification of a necroptosis-related gene signature as a novel prognostic biomarker of cholangiocarcinoma.

Background: Cholangiocarcinoma (CHOL) is the most prevalent type of malignancy and the second most common form of primary liver cancer, resulting in high rates of morbidity and mortality. Necroptosis is a type of regulated cell death that appears to be involved in the regulation of several aspects of cancer biology, including tumorigenesis, metastasis, and cancer immunity. This study aimed to construct a necroptosis-related gene (NRG) signature to investigate the prognosis of CHOL patients using an integrated bioinformatics analysis. Methods: CHOL patient data were acquired from the Gene Expression Omnibus (GEO) (GSE89748, GSE107943) and The Cancer Genome Atlas (TCGA) databases, with NRGs data from the necroptosis pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Univariate and multivariate regression analyses were performed to establish the NRG signatures. Kaplan-Meier (KM) curves were used to evaluate the prognosis of patients with CHOL. Functional enrichment analysis was performed to identify key NRG-associated biological signaling pathways. We also applied integrative multi-omics analysis to the high- and low-risk score groups. Spearman's rank correlation was used to clarify the relationship between the NRG signature and immune infiltration. Results: 65 differentially expressed (DE) NRGs were screened, five of which were selected to establish the prognostic signature of NRGS based on multivariate Cox regression analysis. We observed that low-risk patients survived significantly longer than high-risk patients. We found that patients with high-risk scores experienced higher immune cell infiltration, drug resistance, and more somatic mutations than patients with low-risk scores. We further found that sensitivities to GW843682X, mitomycin C, rapamycin, and S-trityl-L-cysteine were significantly higher in the low-risk group than in the high-risk group. Finally, we validated the expression of five NRGs in CHOL tissues using the TCGA database, HPA database and our clinical data. Conclusion: These findings demonstrate that the five-NRG prognostic signature for CHOL patients is reasonably accurate and valid, and it may prove to be of considerable value for the treatment and prognosis of CHOL patients in the future.

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq.

Background: Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics. Methods: From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed. Results: Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO-Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability. Conclusion: To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

A hypoxia-related prognostic model predicts overall survival and treatment response in hepatocellular carcinoma.

Hypoxia and hypoxia-related genes regulate tumor initiation and progression. However, the exact roles that hypoxia plays in hepatocellular carcinoma (HCC) remain unclear. In the present study, we calculated the hypoxia score of each sample in the GSE14520 training set by single-sample gene set enrichment analysis (ssGSEA). Then, weighted gene coexpression network analysis (WGCNA) was utilized to identify gene modules most correlated with hypoxia. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized to further compress the candidate genes. We constructed the hypoxia-related prognostic risk score (HPRS) model based on the genes' corresponding Cox regression coefficients. Univariate and multivariate Cox analyses of the hypoxia score and clinicopathological characteristics showed that the hypoxia score and stage were the main risk factors affecting the overall survival of patients. Based on WGCNA, we identified 41 key hypoxia-related gene modules and screened out nine core genes to construct the HPRS model. Importantly, high-HPRS patients have a worse prognosis, while low-HPRS patients have a better prognosis. Further research showed that various immune cells, such as CD8 T cells, cytotoxic cells, and DCs, were significantly enriched in the low-HPRS group compared with the high-HPRS group. Notably, patients in the low-HPRS group were less likely to benefit from immunotherapy and chemotherapy than those in the high-HPRS group. In summary, we identified and validated a hypoxia-derived gene model that could serve as a potential biomarker to predict prognosis and therapeutic response in HCC.

Risk Assessment of Liver Metastasis in Pancreatic Cancer Patients Using Multiple Models Based on Machine Learning: A Large Population-Based Study.

Background: A more accurate prediction of liver metastasis (LM) in pancreatic cancer (PC) would help improve clinical therapeutic effects and follow-up strategies for the management of this disease. This study was to assess various prediction models to evaluate the risk of LM based on machine learning algorithms. Methods: We retrospectively reviewed clinicopathological characteristics of PC patients from the Surveillance, Epidemiology, and End Results database from 2010 to 2018. The logistic regression, extreme gradient boosting, support vector, random forest (RF), and deep neural network machine algorithms were used to establish models to predict the risk of LM in PC patients. Specificity, sensitivity, and receiver operating characteristic (ROC) curves were used to determine the discriminatory capacity of the prediction models. Results: A total of 47,919 PC patients were identified; 15,909 (33.2%) of which developed LM. After iterative filtering, a total of nine features were included to establish the risk model for LM based on machine learning. The RF showed the most promising results in the prediction of complications among the models (ROC 0.871 for training and 0.832 for test sets). In risk stratification analysis, the LM rate and 5-year cancer-specific survival (CSS) in the high-risk group were worse than those in the intermediate- and low-risk groups. Surgery, radiotherapy, and chemotherapy were found to significantly improve the CSS in the high- and intermediate-risk groups. Conclusion: In this study, the RF model constructed could accurately predict the risk of LM in PC patients, which has the potential to provide clinicians with more personalized clinical decision-making recommendations.

The LncRNA DUXAP10 Could Function as a Promising Oncogene in Human Cancer.

Cancer is one of the most prevalent and deadliest diseases globally, with an increasing morbidity of approximately 14 million new cancer cases per year. Identifying novel diagnostic and prognostic biomarkers for cancers is important for developing cancer therapeutic strategies and lowering mortality rates. Long noncoding RNAs (lncRNAs) represent a group of noncoding RNAs of more than 200 nucleotides that have been shown to participate in the development of human cancers. The novel lncRNA DUXAP10 was newly reported to be abnormally overexpressed in several cancers and positively correlated with poor clinical characteristics of cancer patients. Multiple studies have found that DUXAP10 widely regulates vital biological functions related to the development and progression of cancers, including cell proliferation, apoptosis, invasion, migration, and stemness, through different molecular mechanisms. The aim of this review was to recapitulate current findings regarding the roles of DUXAP10 in cancers and evaluate the potential of DUXAP10 as a novel biomarker for cancer diagnosis, treatment, and prognostic assessment.

Analysis of Multi-Layer RNA Modification Patterns for the Characterization of Tumor Immune Microenvironment in Hepatocellular Carcinoma.

Background: RNA modifications have emerged as important posttranscriptional changes in multiple tumor cellular processes and tumorigenesis, including hepatocellular carcinoma (HCC). However, the potential roles and the interaction between regulators of RNA modifications and the tumor microenvironment (TME) are unclear in HCC. Methods: The gene expression profiles of 26 RNA modification "writers" were investigated in the TCGA cohort. The unsupervised clustering approach was used to class these RNA modification regulators. The characteristics of immune cell infiltration from TME for each cluster was tested by the CIBERSORT method. Additionally, we established a scoring model to evaluate the RNA modification characteristics of individual tumors. The associations between the scoring model and genetic as well as clinical characteristics, drug sensitivity, and response to immunotherapy were also analyzed. Results: We mapped the somatic mutations and somatic copy number variation of the RNA modification regulators. The expression of all selected regulators was detected, and two modification patterns were identified that featured distinct immune cell infiltration characteristics. Subsequently, we developed a score model (termed as WM-Score model). Furthermore, the survival analysis showed that the WM-Score value was associated with HCC patient prognosis. The results of the ROC curves analysis and multivariate analysis all confirmed that the WM-Score value was strongly associated with anti-cancer drug resistance and therapeutic efficacy of immunotherapy, thus could be used as an independent risk factor in HCC. Conclusion: Our research identified two RNA modification patterns characterized by distinct TME, and the WM-Score model was developed that might serve as reliable prognostic and immunotherapeutic effect predictor of HCC.

Long Non-coding RNA Small Nucleolar RNA Host Gene 14, a Promising Biomarker and Therapeutic Target in Malignancy.

Small nucleolar RNA host gene 14 (SNHG14) is a long non-coding RNA found to be overexpressed in various types of cancers. Moreover, the expression level of SNHG14 was closely associated with multiple clinicopathological characteristics such as prognosis, tumor differentiation, TNM stage, and lymph node metastasis. Functionally, gain- and loss-of-function of SNHG14 revealed that overexpressed SNHG14 promoted cancer cell viability, invasion, and migration, whereas its down-regulation produced the opposite effect. Mechanistically, regulating its target gene expression by sponging distinct miRNAs might be the major mechanism underlying the oncogenic functions of SNHG14. Thus, SNHG14 might be a promising prognostic biomarker and therapeutic target for cancers. In this review, we discuss the expression profile, biological function, and molecular mechanisms of SNHG14 in cancers to provide a molecular basis for the clinical utility of SNHG14 in the future.

Understanding of supramolecular emulsion interfacial polymerization in silico.

The composition and structure of a membrane determine its functionality and practical application. We study the supramolecular polymeric membrane prepared by supramolecular emulsion interfacial polymerization (SEIP) on the oil-in-water droplet via the computer simulation method. The factors that may influence its structure and properties are investigated, such as the degree of polymerization and molecular weight distribution (MWD) of products in the polymeric membranes. We find that the SEIP can lead to a higher total degree of polymerization as compared to the supramolecular interfacial polymerization (SIP). However, the average chain length of products in the SEIP is lower than that of the SIP due to its obvious interface curvature. The stoichiometric ratio of reactants in two phases will affect the MWD of the products, which further affects the performance of the membranes in practical applications, such as drug release rate and permeability. Besides, the MWD of the product by SEIP obviously deviates from the Flory distribution as a consequence of the curvature of reaction interface. In addition, we obtain the MWD for the emulsions whose size distribution conforms to the Gaussian distribution so that the MWD may be predicted according to the corresponding emulsion size distribution. This study helps us to better understand the controlling factors that may affect the structure and properties of supramolecular polymeric membranes by SEIP.

N6-methyladenosine (m6A)-mediated messenger RNA signatures and the tumor immune microenvironment can predict the prognosis of hepatocellular carcinoma.

BACKGROUND: N6-methyladenosine (m6A)-mediated ribonucleic acid (RNA) methylation is considered to be the most significant and abundant epigenetic modification in eukaryotic cells, and plays an essential role in the carcinogenesis and molecular pathogenesis of hepatocellular carcinoma (HCC). However, the relationship between m6A regulation and immune cell infiltration of the tumor immune microenvironment (TIME) has not yet been clarified. We aimed to investigate the roles of m6A RNA gene regulators in HCC immune regulation and prognosis. METHODS: The Cancer Genome Atlas (TCGA) database was used, and unsupervised clustering of 21 m6A regulators was performed based on differential gene expression. Gene Set Variation Analysis (GSVA), single-sample Gene Set Enrichment Analysis (ssGSEA), the empirical Bayes method, and m6A scores were used in our analyses. RESULTS: Of 433 samples, 101 (23.22%) had m6A regulatory factor mutations. From these, we identified three m6A subtypes, which correlated with different TIME phenotypes: immune rejection, immune infiltration, and immune deficiency. Tumors with low methyltransferase-like 3 (METTL3) expression had increased infiltration of dendritic cells (DCs) in the TIME. Reduced METTL3 expression also led to an overall increase in expression of major histocompatibility complex (MHC) molecules, costimulatory molecules, and adhesion molecules. The m6A subtypes were scored and analyzed for correlations. Patients with epithelial-mesenchymal transition (EMT) subtypes had lower m6A scores than the other three molecular subtypes. Survival analysis found that patients with low m6A scores had better overall survival [hazard ratio (HR) 1.6 (1.1-2.3)] and a 1.16 times better 5-year survival rate than patients with high m6A scores (56% vs. 48%). CONCLUSIONS: Our results demonstrated that three different m6A modification subtypes contribute to immune regulation in HCC and have potential as novel prognostic indicators and immune therapeutic targets.

Glycogen Phosphorylase B Is Regulated by miR101-3p and Promotes Hepatocellular Carcinoma Tumorigenesis.

Glycogen metabolism plays a key role in tumorigenesis. High expression levels of glycogen phosphorylase B (PYGB) were reported in several cancers and might be served as a prognostic biomarker for cancer from precancerous lesions. Previous studies indicated the high expression of PYGB in hepatocellular carcinoma (HCC) tissues. However, the detailed roles of PYGB in HCC, as well as the regulatory mechanisms, are still unclear. In this study, we confirmed that PYGB was overexpressed in HCC tissues. PYGB overexpression was significantly associated with an aggressive tumor phenotype and poor prognosis of HCC patients. Functionally, PYGB knockdown suppressed HCC cell proliferation, migration and invasion in vitro, as well as tumorigenesis and metastasis in vivo. Bioinformatics analysis indicated that PYGB overexpression might enhance epithelial to mesenchymal transition (EMT) in HCC. Moreover, miR-101-3p was identified to post-transcriptionally inhibit the expression of PYGB via binding to 3'-UTR of PYGB. Overexpression of PYGB antagonized the regulatory effect of miR-101-3p on cell proliferation, migration and invasion in HCC cells. In summary, our results suggest that miR-101-3p/PYGB axis has an important role in HCC and PYGB could be served as a novel prognostic biomarker and therapeutic target for improving the prognosis of HCC patients.

Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model.

Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in Henan Province, China, compared clinical data, screened for high-risk fatal factors, built a death prediction model and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan. Out of the 523 cases, 429 were mild, 78 severe survivors, 16 non-survivors. The non-survivors with median age 71 were older and had more comorbidities than the mild and severe survivors. Non-survivors had a relatively delay in hospitalization, with higher white blood cell count, neutrophil percentage, D-dimer, LDH, BNP, and PCT levels and lower proportion of eosinophils, lymphocytes and albumin. Discriminative models were constructed by using random forest with 16 non-survivors and 78 severe survivors. Age was the leading risk factors for poor prognosis, with AUC of 0.907 (95% CI 0.831-0.983). Mixed model constructed with combination of age, demographics, symptoms, and laboratory findings at admission had better performance (p = 0.021) with a generalized AUC of 0.9852 (95% CI 0.961-1). We chose 0.441 as death prediction threshold (with 0.85 sensitivity and 0.987 specificity) and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan successfully. Mixed model can accurately predict clinical outcomes of COVID-19 patients.

Identifying drug candidates for hepatocellular carcinoma based on differentially expressed genes.

The prognosis for patients with advanced hepatocellular carcinoma (HCC) is extremely poor, mainly due to rapid progression and a paucity of effective drugs. Genome-wide analysis allows for potential drugs to be explored based on differentially expressed genes (DEGs). However, drug candidates and DEGs in HCC are largely unknown. In this study, we investigated DEGs and prognostication using The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), the Gene Expression Omnibus (GEO), and immunohistochemical staining. Protein-protein interaction networks between DEGs were also analyzed to clarify 12 hub genes and query online databases for potential HCC therapeutic drugs. We found that 885 of 3219 DEGs from a TCGA dataset were associated with prognosis. We clarified 12 hub genes that were overexpressed in tumor samples and significantly associated with poor overall survival (OS) in HCC patients. These findings were validated using GEO and ICGC cohorts. Moreover, promising drug candidates targeted against HCC were predicted using online databases. Collectively, the upregulation of 12 hub genes was associated with poor prognosis for patients with HCC, and focusing on their expression may advance efforts towards targeted HCC therapies.

Vaccinia virus expressing IL-37 promotes antitumor immune responses in hepatocellular carcinoma.

The aim of this study was to investigate the effect of vaccinia virus expressing IL-37 (VV-IL-37) on cell proliferation, migration and invasion of hepatocellular carcinoma (HCC) and its possible underlying molecular mechanisms. In this study, we constructed a cancer-targeted vaccinia virus carrying the IL-37 gene knocked in the region of the viral thymidine kinase (TK) gene. Human HCC cell lines were assayed in vitro for cell proliferation, migration and invasion. Serum level, relative mRNA level and protein level of IL-37 in HCC cell lines SMMC7721 and Bel7402 were tested by ELISA assay, qRT-PCR and western blot, respectively. The levels of IL-2, IFN-γ and TNF-α in HCC tumor tissues were also analyzed by ELISA. STAT3 and p-STAT3 expression in tumor tissues were determined by western blot. Our results showed that VV-IL-37 efficiently infected and inhibited HCC cells proliferation, migration and invasion via decreasing STAT3 phosphorylation. In vivo, VV-IL-37 expressed IL-37 at a high level in the transplanted tumor, reduced STAT3 activity, and eventually inhibited tumor growth. In conclusion, we demonstrate that VV-IL-37 promotes antitumor immune responses in HCC.

Mesenchymal Stem Cell/Red Blood Cell-Inspired Nanoparticle Therapy in Mice with Carbon Tetrachloride-Induced Acute Liver Failure.

Acute liver failure is a critical condition characterized by global hepatocyte death and often time needs a liver transplantation. Such treatment is largely limited by donor organ shortage. Stem cell therapy offers a promising option to patients with acute liver failure. Yet, therapeutic efficacy and feasibility are hindered by delivery route and storage instability of live cell products. We fabricated a nanoparticle that carries the beneficial regenerative factors from mesenchymal stem cells and further coated it with the membranes of red blood cells to increase blood stability. Unlike uncoated nanoparticles, these particles promote liver cell proliferation in vitro and have lower internalization by macrophage cells. After intravenous delivery, these artificial stem cell analogs are able to remain in the liver and mitigate carbon tetrachloride-induced liver failure in a mouse model, as gauged by histology and liver function test. Our technology provides an innovative and off-the-shelf strategy to treat liver failure.

Polymer-grafted nanoparticles prepared via a grafting-from strategy: a computer simulation study.

Nanoparticles (NPs) grafted with polymer chains prepared via a grafting-from strategy are studied through coarse-grained molecular dynamics simulations combined with our stochastic reaction model. A system involving multiple individual NPs, with grafting-from processes for all the NPs induced simultaneously, is simulated, so that chain growth competition on the same NP, as well as between neighbouring NPs, are both naturally considered. Our results imply that there should be an optimized range of NP sizes, as compared to monomer size, in which initiator sites are most easily induced. Besides, when the initiator density is high, a shielding effect from the sparse long chains on the most short chains or initiators evidently yields an extremely unbiased distribution of chains. We also adopt a representative polymer-tethered NP prepared via a grafting-from strategy to study the potential of mean force between NPs, so that the dispersion and stabilization abilities of such polymer-grafted NPs in a polymer matrix can be generally predicted during the preparation of polymer nanocomposite materials. Our study helps to elucidate the cause of chain dispersity during the grafting-from process and could act as a guide for better design and to improve the performance of polymer nanocomposites.

The selectivity of nanoparticles for polydispersed ligand chains during the grafting-to process: a computer simulation study.

By constructing a grafting-to reaction model of polydispersed polymer chains to bind onto nanoparticles (NPs), we elucidate the changes of grafting density, polydispersity index and chain length distribution of grafted ligand chains as a dependent of the feeding polymer chains. Our study shows a linear dependence of the grafting density on the average chain length of the feeding polymers. We also clearly demonstrate the NP's selectivity of short chains in the later stage of the reaction. Our results also show that the polydispersity of the ligand chains on each individual NP is commonly higher than that of the feeding polymer chains. Furthermore, the simulations imply that polydispersed polymer chains are more easily bound during the grafting-to process compared to monodispersed chains. Thus, relatively higher polydispersity of feeding polymer chains are beneficial to promoting the grafting density of NPs. Our study helps to elucidate the cause of chain polydispersity and could act as a guide to finely regulate the polydispersity of ligand chains on nanoparticles.

AMPK activation ameliorates D-GalN/LPS-induced acute liver failure by upregulating Foxo3A to induce autophagy.

BACKGROUND AND AIM: Acute liver failure (ALF) is an uncommon but serious disease still carrying a high mortality. This study aimed to investigate the mechanism of AMPK on D-GalN/LPS-induced ALF. METHODS: In this study, we utilized intraperitoneal injection of D-GalN/LPS to induce ALF model, and analyzed the expression of AMPK, inflammatory cytokines (TNF-α, IL-1ß and IL-6), Foxo3A and autophagy-related genes (Atg-5, Beclin-1, Atg-7) by real-time quantitative polymerase chain reaction (RT-PCR) in liver tissue. We also examined the level of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum of ALF mice. AMPK activation and inhibition of autophagy were induced by AICAR and 3-MA, respectively. Silence and overexpression of Foxo3A were performed by si-Foxo3A and pcDNA-Foxo3A, respectively. Lastly, the BMDM-conditioned medium (BMDM-CM) derived from BMDMs treated with AICAR and LPS were used to explore the effect of AMPK and Foxo3A on hepatocytes. RESULT: The expression of AMPK was decreased in liver tissue and the level of ALT and AST were increased in serum of D-GalN/LPS-induced ALF mice. AMPK activation ameliorated ALF by inhibiting inflammation (downregulated TNF-α, IL-1ß and IL-6 expression), activating autophagy (increased Atg-5, Beclin-1 and Atg-7 expression) and upregulating Foxo3A expression. Silence of Foxo3A decreased AMPK-activated autophagy, but overexpressing Foxo3A attenuated liver failure by activating autophagy. In addition, AMPK activation alleviated liver failure in vitro. CONCLUSION: Thus, AMPK/Foxo3A/autophagy pathway may be an effective treatment approach to ameliorate ALF.