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Background Checkpoint-blockade immunotherapy targeting programmed cell death protein 1 (PD-1) has recently shown promising efficacy in hepatocellular carcinoma (HCC). However, the factors affecting and predicting the response to anti-PD-1 immunotherapy in HCC are still unclear. Herein, we report the dynamic variation characteristics and specificities of the gut microbiome during anti-PD-1 immunotherapy in HCC using metagenomic sequencing. Results Fecal samples from patients responding to immunotherapy showed higher taxa richness and more gene counts than those of non-responders. For dynamic analysis during anti-PD-1 immunotherapy, the dissimilarity of beta diversity became prominent across patients as early as Week 6. In non-responders, Proteobacteria increased from Week 3, and became predominant at Week 12. Twenty responder-enriched species, including Akkermansia muciniphila and Ruminococcaceae spp., were further identified. The related functional genes and metabolic pathway analysis, such as carbohydrate metabolism and methanogenesis, verified the potential bioactivities of responder-enriched species. Conclusions Gut microbiome may have a critical impact on the responses of HCC patients treated with anti-PD-1 immunotherapy. The dynamic variation characteristics of the gut microbiome may provide early predictions of the outcomes of immunotherapy in HCC, which is critical for disease-monitoring and treatment decision-making. Electronic supplementary material The online version of this article (10.1186/s40425-019-0650-9) contains supplementary material, which is available to authorized users.

Along with the deepening development in communication technologies and the surge of mobile devices, a brandnew computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the upgrade of hardware architectures. Billions of bytes of data, generated at the network edge, put great demands on data processing and structural optimization. Therefore, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this article, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing a more optimal solution to the key concerns in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on edge. This article focuses on giving insights into this new inter-disciplinary field from a broader vision and perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research programs in Edge Intelligence.

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Few studies on risk factors for and transmission of Clostridium difficile infection (CDI) in China have been reported. A cross-sectional study was conducted for 3 years in eastern China. Consecutive stool specimens from hospitalized patients with diarrhea were cultured for C. difficile. C. difficile isolates from these patients then were analyzed for toxin genes, genotypes, and antimicrobial resistance. A severity score for the CDI in each patient was determined by a blinded review of the medical record, and these scores ranged from 1 to 6. A total of 397 out of 3,953 patients (10.0%) with diarrhea were found to have CDI. Severity of CDI was mild to moderate, and the average (Ϯ standard deviation) severity score was 2.61 Ϯ 1.01. C. difficile was isolated from stool specimens in 432 (10.9%) of all the patients who had diarrhea. C. difficile genotypes were determined by multilocus sequence analysis and PCR ribotyping; sequence type 37 (ST37)/ribotype 017 (RT017) (n ϭ 68, 16.5%) was the dominant genotype. Eleven patients (16.2%) with this genotype had a CDI severity score of 5. Overall, three RTs and four STs were predominant; these genotypes were associated with significantly different antimicrobial resistance patterns in comparison to all genotypes ( 2 ϭ 79.56 to 97.76; P Ͻ 0.001). Independent risk factors associated with CDI included age greater than 55 years (odds ratio . CDI is clearly a problem in eastern China and has a prevalence of 10.0% in hospitalized patients. Among risk factors for CDI, the advanced age threshold was younger for Chinese patients than that reported for patients in developed countries.[