Marine-derived antioxidant polysaccharides have aroused extensive attention because of their potential nutritional and therapeutic benefits. However, the comprehensive comparison of identified marine-derived antioxidant polysaccharides is still inaccessible, which would facilitate the discovery of more efficient antioxidants from marine organisms. Thus, this review summarizes the sources, chemical composition, structural characteristics, and antioxidant capacity of marine antioxidant polysaccharides, as well as their protective in vivo effects mediated by antioxidative stress reported in the last few years (2013–2019), and especially highlights the dominant role of marine algae as antioxidant polysaccharide source. In addition, the relationships between the chemical composition and structural characteristics of marine antioxidant polysaccharides with their antioxidant capacity were also discussed. The antioxidant activity was found to be determined by multiple factors, including molecular weight, monosaccharide composition, sulfate position and its degree.
Long noncoding RNAs (lncRNA) are attractive biomarkers and therapeutic targets because of their disease- and stage-restricted expression. Small nucleolar RNA host gene 17 (SNHG17) belongs to a large family of noncoding genes hosting small RNAs, with its expression pattern and biological function not clarified in gastric cancer (GC). Thus, we conducted this study to investigate the functional significance and the underlying mechanisms of SNHG17 in GC progression. Our results showed that SNHG17 expression was upregulated in GC tissues and cells, and its high expression was significantly correlated with increased invasion depth, lymphatic metastasis, and advanced TNM stage. The expression of plasma SNHG17 was also found upregulated in patients with GC compared with healthy controls, with a moderate accuracy for diagnosis of GC (area under the receiver operating characteristic curve = 0.748; 95% CI, 0.666-0.830). Gain- and loss-of-function of SNHG17 revealed that SNHG17 promoted GC cell proliferation, cell cycle progression, invasion, and migration and inhibited apoptosis. Mechanistic investigations showed that SNHG17 was associated with polycomb repressive complex 2 and that this association was required for epigenetic repression of cyclin-dependent protein kinase inhibitors, including p15 and p57, thus contributing to the regulation of GC cell cycle and proliferation. Furthermore, rescue experiments indicated that SNHG17 functioned as an oncogene via activating enhancer of zeste homolog 2 in GC cells. Our study provides a new perspective for SNHG17 acting as a noncoding oncogene in GC tumorigenesis, and it may serve as a novel early diagnostic marker and potential target for the treatment of GC.
The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance.
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