Triplex hairpin oligosensor for ultrasensitive determination of miRNA-155 as a cancer marker using Si quantum dots and Au nanoparticles.

In this study, a new triplex hairpin oligosensor was developed for the determination of a breast cancer biomarker using silicon quantum dots (Si QD) (λex = 370 nm, λem = 482 nm) as donor and gold nanoparticles (GNP) as an acceptor in a FRET (fluorescence resonance energy transfer) mechanism. In the triplex hairpin oligosensor, a triplex-forming oligonucleotide (TFO) labeled with Si QD and a single-strand DNA labeled with GNP form a hairpin shape with a triplex structure at the hairpin stem. In a turn-on mechanism, the triplex hairpin stem is opened in the presence of sequence-specific miRNA-155 which leads to the release of the Si QD-labeled TFO probe and recovery of the fluorescence signal. About 80 % of the fluorescence intensity of the Si QD-TFO is quenched in the triplex hairpin structure of the oligosensor and in the presence of 800 pM miRNA-155, the fluorescence signal recovered to 57.7 % of its initial value. The LOD of about 10 pM was obtained. The designed triplex-based biosensor can discriminate concentrations of breast cancer biomarkers with high selectivity.

Associations of Oxalate Consumption and Some Individual Habits with the Risk of Kidney Stones / 中国医学科学杂志(英文版)

Kidney stone is a highly recurrent disease in the urinary tract system. Most kidney stones are calcium stones, usually consisting of either calcium oxalate or calcium phosphate. Supersaturation of soluble calcium, oxalate, phosphate, and citrate in the urine is the basis for calcium stone formation. Genetics, diet, low physical activity, and individual habits contribute to the formation of kidney stones. In this review, the associations of the risk of kidney stones with oxalate consumption and some individual habits, such as smoking, alcohol drinking, and opium consumption, are summarized.

Mineralogy, geochemistry, 13C and 16O isotopic characteristics of urinary stones in Iran, a case study of Lorestan Province.

Owing to the importance of urinary stones as one of the biominerals in the human body, it is necessary to investigate their chemical composition and mineralogy. In this matter, a mineralogical study using X-ray diffraction and scanning electron microscopy indicated that urinary stones in Lorestan Province were divided into 5 groups of calcium oxalate, urate, cysteine, phosphate and mixed stones (Whewellite, uric acid, phosphate). In this regard, the microscopic studies revealed that Whewellite was the most important mineral phase among various phases. In the following, the major and rare elements of each group were determined by inductively coupled plasma mass spectrometry (ICP-MS) and X-ray fluorescence analysis. The obtained results demonstrated that Ca was found the most abundant element in urinary stones. In the analysis results of the major oxides, compared to other major oxides, CaO had the highest frequency in urinary stones. The reason was due to the role of calcium in most of the basic functions in cell metabolism. The average values of isotope 13C and 16O in the studied urinary stones were obtained - 33.71 and - 20.57, respectively. Overall, the values of 13C isotope in urinary stones were lower than those in the similar stones and human hard tissues in other countries.

Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran.

It is essential to study spatial distribution of Potentially Toxic Elements (PTEs) in surface soil, and separate PTEs geochemical background from their human sources, and also determine their correlation with other environmental factors in order to assess their impacts on humans, provide realistic environmental geochemical maps, and carry out soil management. For this purpose, this study was designed to investigate the surface soil in Ahvaz, Southwest of Iran. The applied methods were exploratory data analysis (EDA), including boxplot, Q-Q plot, probability plot (PP), concentration-area (C-A), number-size (N-S) fractal model, and singularity index (SI) model. The obtained results revealed that the fractal models led to a more realistic distinction among the geochemical population compared to the EDA methods such as probability plot. Furthermore, the C-A model was found to be more effective on the separation of subpopulation compared to the N-S and PP models. The studied elements exhibited a similar pattern implying that pollution is a function of geochemical dispersion regarding the surface soil in Ahvaz (Zn â‰« Pb > Cu > As). The studied metals-major elements plot also indicated that there was no meaningful relationship between Pb, Zn, Cu, and major elements in the study area. Plots of association of Pb, Zn, Cu, and As distinctly showed two general geogenic and anthropogenic populations. Moreover, the results of SI revealed that the highly contaminated area was consistent with the main defined hotspots and anthropogenic sources of elements as well as places affected by the contaminated area that have not been reported in previous studies. Furthermore, a combination of geochemical and geographical model comprising different statistical models was developed to more effectively separate geogenic from anthropogenic sources. Also, the geochemical background for the studied elements (Pb 180 mg/kg; Cu 200 mg/kg; Zn 90 mg/kg; As 65 mg/kg) was shown to be higher than the Iranian soil quality guideline with Pb, Zn, Cu, and As of 100, 80, 200, and 18 mg/kg, respectively.