Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification brought by centralized FL paradigm. Moreover, it is easier to allocate incentives to nodes with the help of the blockchain. Various centralized federated learning frameworks like FedML, have emerged in the community to help boost the research on FL. However, decentralized blockchain-based federated learning framework is still missing, which cause inconvenience for researcher to reproduce or verify the algorithm performance based on blockchain. Inspired by the above issues, we have designed and developed a blockchain-based federated learning framework by embedding Ethereum network. This report will present the overall structure of this framework, which proposes a code practice paradigm for the combination of FL with blockchain and, at the same time, compatible with normal FL training task. In addition to implement some blockchain federated learning algorithms on smart contract to help execute a FL training, we also propose a model ownership authentication architecture based on blockchain and model watermarking to protect the intellectual property rights of models. These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this

Hepatocellular carcinoma (HCC) is a widespread, common type of cancer in Asian countries, and the need for biomarker-matched molecularly targeted therapy for HCC has been increasingly recognized. However, the effective treatment for HCC is unclear. Therefore, identifying additional hub genes and pathways as novel prognostic biomarkers for HCC is necessary. In this study, the expression profiles of GSE121248, GSE45267 and GSE84402 were obtained from the Gene Expression Omnibus (GEO), including 132 HCC and 90 noncancerous liver tissues. Differentially expressed genes (DEGs) between HCC and noncancerous samples were identified by GEO2 R and Venn diagrams. In total, 109 DEGs were identified in these datasets, including 24 upregulated genes and 85 downregulated genes. Subsequently, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) preliminary analyses of the DEGs were performed using DAVID. The protein–protein interaction (PPI) network of the DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape. Module analysis of the PPI network was performed using MCODE to get hub genes. Moreover, the influence of the hub genes on overall survival was determined with Kaplan–Meier plotter. All hub genes were analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and KEGG. Overall, the hub genes DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, and ANLN were upregulated in HCC, and the survival rate was lower for HCC with increased expression of these hub genes. CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway, and CCNB1, CDK1, and BUB1B were enriched in the cell cycle. In brief, we screened 15 hub genes and pathways to identify potential prognostic markers for HCC treatment. However, the specific occurrence and development of HCC with expression of the hub genes should be verified in vivo and in vitro.

Dietary habits are crucial in the progression of hepatic lipid accumulation and nonalcoholic fatty liver disease (NAFLD). However, there are limited studies using 1H-magnetic resonance spectroscopy (1H-MRS) and dual-echo in-phase and out-phase magnetic resonance spectroscopy imaging (dual-echo MRI) to assess the effects of dietary nutrient intakes on hepatic lipid contents. In the present study, we recruited 36 female adults (NAFLD:control = 19:17) to receive questionnaires and medical examinations, including dietary intakes, anthropometric and biochemical measurements, and 1H-MRS and dual-echo MRI examinations. NAFLD patients were found to consume diets higher in energy, protein, fat, saturated fatty acid (SFA), and polyunsaturated fatty acid (PUFA). Total energy intake was positively associated with hepatic fat fraction (HFF) and intrahepatic lipid (IHL) after adjustment for age and body-mass index (BMI) (HFF: β = 0.24, p = 0.02; IHL: β = 0.38, p = 0.02). Total fat intake was positively associated with HFF and IHL after adjustment for age, BMI and total energy intake (HFF: β = 0.36, p = 0.03; IHL: β = 0.42, p = 0.01). SFA intake was positively associated with HFF and IHL after adjustments (HFF: β = 0.45, p = 0.003; IHL: β = 1.16, p = 0.03). In conclusion, hepatic fat content was associated with high energy, high fat and high SFA intakes, quantified by 1H-MRS and dual-echo MRI in our population. Our findings are useful to provide dietary targets to prevent the hepatic lipid accumulation and NAFLD.

Background: Infantile eczema is an immunological disease that is characterized by itchy and dry skin. Recent studies have suggested that gut microbiota (GM) plays a role in the development and progression of eczema. To further evaluate this potential link, we collected feces from 19 infants with eczema and 14 infants without eczema and analyzed the molecular discrepancies between the two groups using 16S rDNA analysis.Results: Bacteroidaceae and Deinococcaceae were significantly enriched in eczema infants, and Bacteroidaceae was potentially involved in autoimmune diseases by promoting the Th17 (T helper cell 17) secretion of IL-17 (interleukin-17). In the infants without eczema, the co-abundance network featured three core nodes: Clostridiaceae, Veillonellaceae, and Lactobacillaceae, all of which were lacking in the infants with eczema. Furthermore, our data suggested that Enterobacteriaceae was the core of the co-abundance network for the diseased subjects.Conclusions: GM is closely connected to the human immune system, and the dysbiotic GM network plays a role in eczema. This study furthered our understanding of the dynamic GM network and its correlation to the occurrence of eczema.

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