Abstract: Background: Microvesicles (MVs) are produced through the outward vesicles budding and fission from the cell surface. Recently, it was discovered that extracellular MVs circulate in bodily fluids of cancer patients and could serve as potential diagnostic biomarkers. However, the diagnostic and prognostic roles of peripheral circulating MVs for hepatocellular carcinoma (HCC) remain unclear. Objective: The aim of this study was to investigate whether the peripheral blood MVs could serve as potential biomarkers for detection of HCC. Methods: Peripheral blood samples were obtained prior to treatment from 55 patients with HCC, 40 patients with liver cirrhosis and 21 healthy controls. MVs…were isolated from peripheral blood by centrifugation and measured by using bicinchoninic acid assay. Results: Peripheral blood MVs levels were significantly elevated in HCC patients compared to those in liver cirrhosis (p< 0.001). Furthermore, MVs levels was correlated with the HCC tumor size, pathological classification and TNM stage (p< 0.01). Of note, MVs levels were significantly reduced in the 1 month post-operative blood samples when compared to those in the pre-operative samples in the 17 HCC cases tested. MVs levels did not relate to liver enzymes, AFP levels, alcohol drinking or smoking habits (p> 0.05). In contrast, serum MVs levels correlated with the age of patients, leukocytes, platelets and prothrombin time. The results of receiver operating characteristic (ROC) analysis indicated better performance of MVs than AFP for early detection of HCC. The areas under the ROC curve of MVs for discriminating patients with early (TNM stage I) and relatively early (TNM stage II) HCC from liver cirrhosis was 0.83 (95% CI: 0.74–0.93) and 0.94 (95% CI: 0.88–1.00), respectively. Conclusions: Peripheral blood MVs levels were increased in patients with HCC and associated with the progression of disease. Serum MVs might serve as novel biomarkers for the diagnosis of HCC at early stage.
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Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for…policy-making.
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Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network