No abstract

In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method – AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice.

Our reference intervals for TSH and FT4 are distinct from the ranges reported in the DxI 600 instruction manual and previously reported data, confirming the importance of method-specific reference intervals.

No abstract

Background: Diabetes mellitus was a chronic low-grade inflammatory disease and had increased circulating inflammatory cytokines and acute phase proteins. We aimed to identify the changes of inflammatory cytokines in newly diagnosed type 2 diabetic patients after short-term intensive insulin therapy using continuous subcutaneous insulin infusion (CSII).Methods: Thirty-three newly diagnosed type 2 diabetic patients were enrolled between September 2020 to December 2020. Expression of 40 inflammatory cytokines of the patients were tested with RayBiotech antibody array before and after 1 week of intensive insulin therapy of CSII. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out to explore the signaling pathway involved in the therapy. Results: Five inflammatory cytokines were downregulated significantly after 1 week of CSII therapy. They were interleukin-6 receptor (IL-6R), regulated upon activation normal T-cell expressed and secreted (RANTES), intercellular adhesion molecule-1 (ICAM-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), and platelet-derived growth factor type BB (PDGF-BB) (p < 0.05 and foldchange <0.83). Among patients with baseline glycated hemoglobin (HbA1c) < 10%, three proinflammatory cytokines were decreased significantly after therapy: IL-6R, RANTES, and ICAM-1. As for the patients with baseline HbA1c ≥ 10%, eight inflammatory cytokines were inhibited significantly after the treatment, including ICAM-1, IL-6R, RANTES, TIMP-1, TIMP-2, macrophage inflammatory protein-1 beta (MIP-1β), PDGF-BB, and tumor necrosis factor receptor type II (TNF RII).No matter which subgroup of baseline HbA1c level was considered, the decreased cytokines after CSII therapy were significantly involved in TNF signaling pathway. Nuclear factor-kappa B (NF-κB) signaling pathway was mainly enriched in patients with baseline HbA1c ≥ 10%.Junyu He and Peiji Dai contributed equally to this study.