High rate capabilities and remarkably cycle-stable flexible pseudocapacitors based on nano-coralloid arrays with sulfide vacancies enhanced Ni-Co-S nanoparticle covering.

Both poor electron conductivity and low ion diffusion of electrode materials are two main issues limiting the rate performance of pseudocapacitors. The present work reports the design and fabrication of hierarchically nano-architectured electrodes consisting of sulfide vacancies enhanced Ni-Co-S nanoparticle covering bent nickel nano-forest (BNNF). We propose new insight into vastly increased ion-accessible active sites and fast charge storage/delivery enhanced the reaction kinetics. The Ni-Co-S@BNNF electrode exhibits extremely high rate performance with 90.1% capacity retention from 1 to 20 A g-1, and even still remains 83.6% capacity at 40 A g-1, much superior to reported NiCo2S4-based electrodes. The high rate performance is attributed to the unique nano-architecture providing increased ion availability of electrochemically active sites and high conductivity for fast electron transport. Especially the electrode achieves remarkable long-term cycle stability with more than 100% initial capacity value after 5000 cycles at 5 A g-1and exhibits excellent cycle reversibility even at 20 A g-1. Goog cycle stability should be attributed to the sulfide vacancies in Ni-Co-S nano-branches and the electrode architecture sustaining structural strain during fast redox reactions. An asymmetric pseudocapacitor applying such electrode achieves a high energy density of 99.9 W h kg-1and exhibits superior cycling stability at a high current density of 20 A g-1. This study underscores the potential importance of developing nanoarrays covered with highly redox-active materials with increasing ions/charge kinetics for energy storage.

Mutational landscape of gastric cancer and clinical application of genomic profiling based on target next-generation sequencing.

BACKGROUND: Gastric cancer (GC) is a leading cause of cancer deaths, and an increased number of GC patients adopt to next-generation sequencing (NGS) to identify tumor genomic alterations for precision medicine. METHODS: In this study, we established a hybridization capture-based NGS panel including 612 cancer-associated genes, and collected sequencing data of tumors and matched bloods from 153 gastric cancer patients. We performed comprehensive analysis of these sequencing and clinical data. RESULTS: 35 significantly mutated genes were identified such as TP53, AKAP9, DRD2, PTEN, CDH1, LRP2 et al. Among them, 29 genes were novel significantly mutated genes compared with TCGA study. TP53 is the top frequently mutated gene, and tends to mutate in male (p = 0.025) patients and patients whose tumor located in cardia (p = 0.011). High tumor mutation burden (TMB) gathered in TP53 wild-type tumors (p = 0.045). TMB was also significantly associated with DNA damage repair (DDR) genes genotype (p = 0.047), Lauren classification (p = 1.5e-5), differentiation (1.9e-7), and HER2 status (p = 0.023). 38.31% of gastric cancer patients harbored at least one actionable alteration according to OncoKB database. CONCLUSIONS: We drew a comprehensive mutational landscape of 153 gastric tumors and demonstrated utility of target next-generation sequencing to guide clinical management.

Comprehensive analysis of gene expression profiles provides insight into the pathogenesis of Crohn's disease.

Crohn's disease (CD) is a type of inflammatory bowel disease that cannot be fully cured by medication or surgery. In the present study, the aim was to understand the underlying mechanisms of CD. Two CD microarray datasets were downloaded from The Gene Expression Omnibus database: GSE36807 (13 CD and 7 normal samples) and GSE59071 (8 CD and 11 normal samples). A series of bioinformatics analyses were conducted, including weighted gene co­expression network analysis to identify stable modules, and analysis of differentially expressed genes (DEGs) between CD and normal samples. The common DEGs in the GSE36807 and GSE59071 datasets were screened. Subsequently, overlapping genes in the stable modules and the DEGs were selected to construct a protein­protein interaction (PPI) network using Cytoscape software. Enrichment analysis of genes in the network was performed to explore their biological functions. A total of 10 stable modules and 927 DEGs were identified, of which 234 genes were shared in the stable modules and the DEGs. After removal of 32 uncharacterized genes, 202 genes were selected to build the PPI network. Low density lipoprotein receptor (LDLR), toll­like receptor 2 (TLR2), lipoprotein lipase (LPL), forkhead box protein M1 (FOXM1) and neuropeptide Y (NPY) were revealed as key nodes with high degree. Pathway enrichment analysis demonstrated that LPL was enriched in the peroxisome proliferator­activated receptor (PPAR) signaling pathway. In conclusion, LDLR, TLR2, FOXM1 and NPY, as well as LPL in the PPAR signaling pathway may serve critical roles in the pathogenesis of CD.

Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA­sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas database served as a validation set. Based on the t­test and Wilcoxon test, the significantly differentially expressed genes (DEGs) were obtained by screening the intersecting DEGs. The prognosis-associated genes and clinical factors were identified using Cox regression analysis in the R survival package. The optimal prognosis­associated pathways were examined using the Cox­proportional hazards (Cox­PH) model in the R penalized package. Finally, risk prediction models were constructed and validated using the Cox­PH model and the Kaplan­Meier method, respectively. There were a total of 382 significant DEGs, including 268 upregulated genes and 114 downregulated genes. A total of 50 prognosis­associated genes were identified, 16 optimal prognosis­associated pathways (including mitochondrial pathway and the tyrosine­protein kinase JAK­signal transducer and activator of transcription signaling pathway, which involve caspase 7, phosphoinositide­3­kinase regulatory subunit 3, peroxisome proliferator­activated receptor γ and collagen triple helix repeat containing 1) and four prognosis­associated clinical factors [including Pathologic_N, Pathologic_stage, mutL homolog 1 (MLH1) mutation and recurrence]. The pathway­ and clinical­factor­based risk prediction model exhibited marked prognostic accuracy. The clinical­factor­based risk prediction model with improved P­values for prognosis prediction may be superior to the pathway­based risk prediction model in predicting the prognosis of GC patients.

Novel Polymer Aerogel toward High Dimensional Stability, Mechanical Property, and Fire Safety.

Inorganc silica-based aerogels, the earliest and widely used aerogels, have poorer mechanical properties than their organic substitutes, which are flammable. In this study, a novel polymeric aerogel with high strength, inherent flame retardancy, and cost-effectiveness, which is based on poly(vinyl alcohol) (PVA) cross-linked with melamine-formaldehyde (MF), was prepared under aqueous condition with an ecofriendly freeze-drying and postcuring process. Combined with the additional rigid MF network and benifited from the resulting unique infrastructure of inter-cross-linked flexible PVA segments and rigid MF segments, PVA-based aerogels exibited a significantly decreased degradation rate and sharply decreased peak heat release rate (PHRR) in cone calorimeter tests (by as much as 83%) compared with neat PVA. The polymer aerogels have a limiting oxygen index (LOI) as high as 36.5% and V-0 rating in UL-94 test. Furthermore, the aerogel samples exposured to harsh temperatures maintain their dimensions (<10% change), original mechanical strength and fire safety. Therefore, this work provides a novel stragegy for preparing pure organic polymeric aerogel materials with high mechanical strength, dimensional stability, and fire safety.

Flame retardation of cellulose-rich fabrics via a simplified layer-by-layer assembly.

Due to the high cellulose content of cotton (88.0-96.5%), the flame retardation of cotton fabrics can be achieved via an approach for the flame retardation of cellulose. In this work, a facile water-based flame retardant coating was deposited on cotton fabrics by a 'simplified' layer-by-layer (LbL) assembly. The novel coating solution was based on a mild reaction between ammonium polyphosphate (APP) and branched polyethyleneimine (BPEI), and the reaction mechanism was studied. TGA results showed that the char residues of coated fabrics were remarkably increased. The fabric with only 5wt% coating showed self-extinguishing in the horizontal flame test, and the peak heat release rate (pHRR) in cone calorimeter test decreased by 51%. Furthermore, this coating overcame a general drawback of flame-retardant LbL assembly which was easily washed away. Therefore, the simplified LbL method provides a fast, low-cost, eco-friendly and wash-durable flame-retardant finishing for the cellulose-rich cotton fabrics.