Efficient Z-scheme photocatalyst from simultaneous decoration of In2S3 nanosheets and WO3 nanorods on graphene sheets.

Inspired by natural photosynthesis, the Z-scheme photocatalyst is a promising approach to extend the absorption spectra of photocatalysts and reduce the recombination of photo-generated electrons and holes. However, the fabrication of well-structured efficient multi-component Z-scheme photocatalysts is still a big challenge. We report here a facile one-pot method to synthesize graphene-based Z-scheme photocatalysts. The one-pot method guarantees good distribution of well-structured individual components on thin-layered rGO sheets with excellent connections. With inactive WO3 nanorods and inactive ß-In2S3 nanosheets attached to the surface of the rGO sheets, the synthesized In2S3/WO3/rGO tertiary nanocomposite shows excellent visible-light catalytic activity for hydrogen production at 1524 µmol g(-1) h(-1), demonstrating unambiguously the Z-scheme catalytic mechanism. To prevent cross-reactions and interferences, our strategy was to choose no more than one ionic precipitation reaction for the one-pot process, as unwanted cross-reactions could become inevitable if many cations and anions were present. This fabrication strategy should be applicable generally to synthesize other multiple-component nanocomposites, as demonstrated also by the preliminary results of the successful synthesis of the BiVO4/WO3/rGO nanocomposite (one ionic precipitation reaction and one hydrolysis reaction) and WO3/TiO2/rGO nanocomposite (two hydrolysis reactions).

A comprehensive evaluation of single nucleotide polymorphisms associated with osteosarcoma risk: A protocol for systematic review and network meta-analysis.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with osteosarcoma (OS) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with OS. METHODS: Databases were searched to identify association studies of SNPs and OS published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, and Wan fang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability for noteworthy associations. The methodological quality of data was assessed based on the STrengthening the REporting of Genetic Association Studies statement Stata 14.0 will be used for systematic review and meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with OS susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with OS susceptibility. REGISTRATION: INPLASY202040023.

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with pancreatic cancer risk.

In this meta-analysis, we systematically investigated the correlation between single nucleotide polymorphisms (SNPs) and pancreatic cancer (PC) risk. We searched PubMed, Network Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang databases up to January 2020 for studies on PC risk-associated SNPs. We identified 45 case-control studies (36,360 PC patients and 54,752 non-cancer individuals) relating to investigations of 27 genes and 54 SNPs for this meta-analysis. Direct meta-analysis followed by network meta-analysis and Thakkinstian algorithm analysis showed that homozygous genetic models for CTLA-4 rs231775 (OR =0.326; 95% CI: 0.218-0.488) and VDR rs2228570 (OR = 1.976; 95% CI: 1.496-2.611) and additive gene model for TP53 rs9895829 (OR = 1.231; 95% CI: 1.143-1.326) were significantly associated with PC risk. TP53 rs9895829 was the most optimal SNP for diagnosing PC susceptibility with a false positive report probability < 0.2 at a stringent prior probability value of 0.00001. This systematic review and meta-analysis suggest that TP53 rs9895829, VDR rs2228570, and CTLA-4 rs231775 are significantly associated with PC risk. We also demonstrate that TP53 rs9895829 is a potential diagnostic biomarker for estimating PC risk.