Systematic Review and Meta-Analysis on the Association Between Outpatient Statins Use and Infectious Disease-Related Mortality

Background: To update and refine systematic literature review on the association between outpatient statins use and mortality in patients with infectious disease. Materials and Methods: We searched articles published before September 31, 2012, on the association between statins and infectious disease-related mortality through electronic databases. Eligible articles were analyzed in Review Manager 5.1. We conducted stratification analysis by study design, infection types, clinical outcomes and study locations. Results: The pooled odds ratio (OR) for death (statins use vs. no use) across the 41 included studies was 0.71 (95% confidence interval: 0.64, 0.78). The corresponding pooled ORs were 0.58 (0.38, 0.90), 0.66 (0.57, 0.75), 0.71 (0.57, 0.89) and 0.83 (0.67, 1.04) for the case-control study, retrospective cohort studies, prospective cohort studies and RCTs; 0.40 (0.20, 0.78), 0.61 (0.41, 0.90), 0.69 (0.62, 0.78) and 0.86 (0.68, 1.09) for bacteremia, sepsis, pneumonia and other infections; 0.62 (0.534, 0.72), 0.68 (0.53, 0.89), 0.71 (0.61, 0.83) and 0.86 (0.70, 1.07) for 30-day, 90-day, in-hospital and long-term (>1 year) mortality, respectively. Conclusions: Outpatient statins use is associated with a lower risk of death in patients with infectious disease in observational studies, but in a less extent in clinical trials. This association also varies considerably by infection types and clinical outcomes

A novel method for high accuracy sumoylation site prediction from protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein sumoylation is an essential dynamic, reversible post translational modification that plays a role in dozens of cellular activities, especially the regulation of gene expression and the maintenance of genomic stability. Currently, the complexities of sumoylation mechanism can not be perfectly solved by experimental approaches. In this regard, computational approaches might represent a promising method to direct experimental identification of sumoylation sites and shed light on the understanding of the reaction mechanism.</p> <p>Results</p> <p>Here we presented a statistical method for sumoylation site prediction. A 5-fold cross validation test over the experimentally identified sumoylation sites yielded excellent prediction performance with correlation coefficient, specificity, sensitivity and accuracy equal to 0.6364, 97.67%, 73.96% and 96.71% respectively. Additionally, the predictor performance is maintained when high level homologs are removed.</p> <p>Conclusion</p> <p>By using a statistical method, we have developed a new SUMO site prediction method – SUMOpre, which has shown its great accuracy with correlation coefficient, specificity, sensitivity and accuracy.</p

Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling

In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas from dialogues. In spoken dialogues, abundant dialogue noise and common semantics could obscure the underlying informative content, making the general topic modeling approaches difficult to apply. In addition, for customer service, role-specific information matters and is an indispensable part of a summary. To effectively perform topic modeling on dialogues and capture multi-role information, in this work we propose a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues. Comprehensive studies on a real-world Chinese customer service dataset demonstrated the superiority of our method against several strong baselines.Comment: Accepted by AAAI 2021, 9 page

GOLM1 Stimulation of Glutamine Metabolism Promotes Osteoporosis via Inhibiting Osteogenic Differentiation of BMSCs

Background/Aims: Bone marrow mesenchymal stem cells (BMSCs) play an essential role in osteoporosis. However, the molecular mechanisms and the involvement of glutamine metabolism in osteogenic BMSCs differentiation and osteoporosis remain largely unclear. In this study, we investigated the role of Golgi membrane protein 1 (GOLM1) and glutamine metabolism in BMSCs differentiation and osteoporosis. Methods: Osteogenic differentiation-inducing media (Odi) was used to induce the osteogenic differentiation of BMSCs. The mRNA expression of GOLM1, ALP, Runx2, Osx, BSP and OCN was determined by qRT-PCR assay. Western blot assay was used to analyze GOLM1, p-mTOR, mTOR, p-S6 and S6 abundance in GOLM1 silencing and over-expressed BMSCs. Glutamine uptake, intracellular glutamine, glutamate and α-KG level was detected using indicated Kits. GOLM1 antibody, glutamine metabolism inhibitors EGCG and BPTES were used to treat ovariectomy (OVX)-induced osteoporosis. Bone mineral density and bone volume relative to tissue volume (%) were analyzed by micro-CT. Serum was collected from osteoporosis patients and healthy participants and subjected to GOLM1 determination using ELISA Kit. Results: GOLM1 expression and glutamine metabolism were suppressed by Odi. GOLM1 blockage or inhibition of glutamine metabolism promoted the osteogenic differentiation of BMSCs induced by Odi. GOLM1 activated glutamine metabolism depending on the mTOR signaling pathway. In vivo, GOLM1 antibody or combination of glutamine inhibitor EGCG and BPTES rescued the osteoporosis in an OVX-operated mouse model. Serum GOLM1 level was increased in the patients of osteoporosis compared with healthy people. Conclusion: GOLM1 stimulates glutamine metabolism to suppress the osteogenic differentiation of BMSCs and to promote osteoporosis. Therefore, GOLM1 activation of glutamine metabolism is a potential target for osteoporosis