Water pollution has become one of the leading causes of human health problems. Low molecular weight pollutants, even at trace concentrations in water sources, have aroused global attention due to their toxicity after long-time exposure. There is an increased demand for appropriate methods to detect these pollutants in aquatic systems. Aptamers, single-stranded DNA or RNA, have high affinity and specificity to each of their target molecule, similar to antigen-antibody interaction. Aptamers can be selected using a method called Systematic Evolution of Ligands by EXponential enrichment (SELEX). Recent years we have witnessed great progress in developing aptamer selection and aptamer-based sensors for low molecular weight pollutants in water sources, such as tap water, seawater, lake water, river water, as well as wastewater and its effluents. This review provides an overview of aptamer-based methods as a novel approach for detecting low molecular weight pollutants in water sources.
Keeping the integrity and transparency of the cornea is the most important issue to ensure normal vision. There are more than 10 million patients going blind due to the cornea diseases worldwide. One of the effective ways to cure corneal diseases is corneal transplantation. Currently, donations are the main source of corneas for transplantation, but immune rejection and a shortage of donor corneas are still serious problems. Graft rejection could cause transplanted cornea opacity to fail. Therefore, bioengineer-based corneas become a new source for corneal transplantation. Limbal stem cells (LSCs) are located at the basal layer in the epithelial palisades of Vogt, which serve a homeostatic function for the cornea epithelium and repair the damaged cornea. LSC-based transplantation is one of the hot topics currently. Clinical data showed that the ratio of LSCs to total candidate cells for a transplantation has a significant impact on the effectiveness of the transplantation. It indicates that it is very important to accurately identify the LSCs. To date, several putative biomarkers of LSCs have been widely reported, whereas their specificity is controversial. As reported, the identification of LSCs is based on the characteristics of stem cells, such as a nuclear-to-cytoplasm ratio (N/C) ≥ 0.7, label-retaining, and side population (SP) phenotype. Here, we review recently published data to provide an insight into the circumstances in the study of LSC biomarkers. The particularities of limbus anatomy and histochemistry, the limits of the current technology level for LSC isolation, the heterogeneity of LSCs and the influence of enzyme digestion are discussed. Practical approaches are proposed in order to overcome the difficulties in basic and applied research for LSC-specific biomarkers.
AIMIn our previous study, we have built a nine-gene (GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1B, CXCR4, PFN1, and CALR) expression detection system based on the GeXP system. Based on peripheral blood and GeXP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma (HCC) patients and healthy people.METHODSLogistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fifty-two patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.RESULTSArtificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.CONCLUSIONMulti-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.
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