Selection Of A Novel Aptamer Against Vitronectin Using Capillary Electrophoresis And Next Generation Sequencing

Breast cancer (BC) results in ≃40,000 deaths each year in the United States and even among survivors treatment of the disease may have devastating consequences, including increased risk for heart disease and cognitive impairment resulting from the toxic effects of chemotherapy. Aptamer-mediated drug delivery can contribute to improved treatment outcomes through the selective delivery of chemotherapy to BC cells, provided suitable cancer-specific antigens can be identified. We report here the use of capillary electrophoresis in conjunction with next generation sequencing to develop the first vitronectin (VN) binding aptamer (VBA-01; Kd 405 nmol/l, the first aptamer to vitronectin (VN; Kd = 405 nmol/l), a protein that plays an important role in wound healing and that is present at elevated levels in BC tissue and in the blood of BC patients relative to the corresponding nonmalignant tissues. We used VBA-01 to develop DVBA-01, a dimeric aptamer complex, and conjugated doxorubicin (Dox) to DVBA-01 (7:1 ratio) using pH-sensitive, covalent linkages. Dox conjugation enhanced the thermal stability of the complex (60.2 versus 46.5°C) and did not decrease affinity for the VN target. The resulting DVBA-01-Dox complex displayed increased cytotoxicity to MDA-MB-231 BC cells that were cultured on plasticware coated with VN (1.8 × 10⁻⁶mol/l) relative to uncoated plates (2.4 × 10⁻⁶ mol/l), or plates coated with the related protein fibronectin (2.1 × 10⁻⁶ mol/l). The VBA-01 aptamer was evaluated for binding to human BC tissue using immunohistochemistry and displayed tissue specific binding and apparent association with BC cells. In contrast, a monoclonal antibody that preferentially binds to multimeric VN primarily stained extracellular matrix and vessel walls of BC tissue. Our results indicate a strong potential for using VN-targeting aptamers to improve drug delivery to treat BC

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Boron intake, osteocalcin polymorphism and serum level in postmenopausal osteoporosis

The relationship between daily boron intake and osteocalcin-mediated osteoporosis was studied in boron-exposed postmenopausal women. It is known that boron and osteocalcin are important in bone metabolism, however the effect of boron in bone metabolism has not been fully discovered. The study was performed on 53 postmenopausal women aged 55–60 living in parts of Balikesir, Turkey, where the subjects are naturally exposed to high (≥1 mg/L) or low ( 0.05). The differences in the distribution of osteocalcin genotypes and alleles in postmenopausal women were not significant between the boron exposed and the control groups (P > 0.05). Serum osteocalcin level in the CC genotype was significantly higher compared to the TC genotype in boron-exposed group (P ≤ 0.05). Our study suggests that daily boron intake of 1 mg/L may affect bone metabolism in postmenopausal women positively. © 2018 Elsevier Gmb

Impacts of climate change on regulating nitrogen retention in the River Weisse Elster in Germany

In this study, climate scenarios (dry, medium and wet) have been used to characterize changing climatic and flow conditions for the period 2050-2054 in the 4th order River Weisse Elster in Germany. Present and future periods of nitrogen turnover were simulated with the WASPS river water quality model. Results revealed that, for a dry climate scenario, the mean denitrification rate was 71% higher during summer (low flow period between 2050 and 2054) and 51% higher during winter (high flow period) compared to the reference period. In the 42-km study reach, N-retention through denitrification amounted to 5.1% of the upper boundary N load during summer low flow conditions during the reference period. For the future dry climate scenario, this value increased by up to 10.2%. In our case study, the investigated climate scenarios showed that future discharge changes may have a larger impact on denitrification rates than future temperature changes