Aims: To investigate the impact of three single nucleotide polymorphisms (SNPs) in VDR gene in PCOS and serum level of vitamin D. \nMethods and Results: This a case-control study that included a total of 50 women with polycystic ovary syndrome (PCOS) and other age-matched 50 women with regular menstrual cycle. Blood samples were collected from all women, and vitamin D receptor (VDR) gene fragment corresponding the SNPs ApaI, BsmI and TaqI were amplified with specific primers. Genotyping was performed with restriction fragment length polymorphism (RFLP). Serum level of vitamin D was estimated by high-performance liquid chromatography (HPLC). The frequency of genotypes and alleles for ApaI and BsmI polymorphisms were comparable between patients and controls with no significant differences. However, the frequency of C allele for TaqI in patients and controls was 54% and 32%, respectively with a significant difference (OR=2.5, 95%CI= 1.4-4.34, p= 0.002). The mean serum level of 25(OH)D in patients was 20.66±5.46 ng/ml compared with 31.79±4.43 ng/ml in controls with a significant difference. \nConclusions: Allele C of the SNP TaqI may be considered as a risk factor for PCOS, and associated with low serum level of 25(OH)D.
Aim: This study is of a methodological type that evaluates the Turkish validity and reliability of the Humanist Nurse-Patient Scale. \nMethods: The research was conducted between April and May 2019 at Isparta State Hospital with 225 patients. The data were collected with a personal information form and the Humanist Nurse-Patient Scale. \nResults: Content Validity Index of the scale is 0,92. In the factor analysis, it was observed that the scale obtained in the Turkish adaptation consisted of 22 items and 5 sub-dimensions, and the items created in the sub-dimensions of the scale displayed a different structure from the original scale. The internal consistency Cronbach\'s Alpha coefficient of the scale was calculated as 0.912. As a result, the scale obtained as a result of the Turkish adaptation study of the Humanist Nurse-Patient Scale is a valid and reliable measurement tool.\nKey Words\nNursing care, nurse-patient relations, nursing theory, validity, reliability
The antifungal activity potentiality effects of the nanomaterial MgO and TiO2 in controlling Fusarium wilt on tomato plants under greenhouse conditions. The antifungal activity of MgO and TiO2 (NPs) were examined in vitro by using different concentration of the nanoparticles and the inhibited growth eight Fusarium isolates were recorded. Subsequently, the hyphae and spore formation were tested using the Scanning Electron Microscopy (SEM). Accordingly, greenhouse experiments were performed on three tomato cultivars inoculated by the fungus FLO4 isolate and sprayed with the desired concentrations of MgO and TiO2 compared with the control plants. The capability of the NPs in controlling the wilt disease and their prolonged effects on the plant defense response were examined. The results revealed that the two NPs succeeded to reduce the fungal radial growth and the highest reduction of the fungal growth was achieved with the high concentration of the tested NPs. A 99% reduction of fungal radial growth was obtained by MgO NPs (at 200 ppm) while 66% reduction was observed in case of TiO2NPs (at 100 ppm). The resulted in greenhouse experiments showed that MgO NPs was preferred because it decreased Fusarium wilt severity into 13.99 % compared to 51.8% infected control (F) but not treated with NPs and those treated with the chemical fungicide. Consequently, the plant immune response against the fungal infection was raised and this response was reflected through the incensement both of the activity of the defense-related enzymes (PPO and PAL) and the gene expression of PR1 and PR5 genes.
Vehicle to vehicle distance measurement is one of the main tasks of the Advanced Driver Assistance System (ADAS). To measure the distance between the subject vehicle and the target vehicle from images, this study proposes a machine learning approach aiming at developing algorithms that can learn and create statistical models for data analysis and distance prediction. The proposed model consists of the YOLO (you look only once) method for vehicle detection task and the supervised neural network learning algorithm for distance estimation. Therefore, this research is divided two stages. In the first stage, the purpose is to produce bounding boxes of multiple objects within the image and generate ground truth of distance between vehicles from KITTI dataset. The KITTI dataset is a popular dataset which can be used for vision algorithm testing of ADAS. It contains a large number of stereo image pairs captured from a car driving in an urban scenario and also provides sparse depth data matched with the stereo vision. On the basis of the first stage, information pertaining of the vehicle objects and their bounding boxes are used as input features for neural network model to learn the implicit distance relation with preceding vehicles in the second stage. The experimental images from real road scenarios are extracted the public KITTI dataset. The results of the quantitative and qualitative comparisons on the KITTI dataset show that the proposed neural network model can effectively predict vehicle distance.
Autoimmune bullous diseases (ABD), which are divided into pemphigus and pemphigoid, are a series of blistering disorders of the skin and mucous membranes induced by pathogenic autoantibodies. This article reviews and summarizes cases of ABD associated with autoimmune liver diseases (ALD). Sixteen cases of ABD associated with ALD were found, of which 87.5% (14/16) were pemphigoid. Furthermore, 2, 8, and 6 patients had ALD such as autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis (PSC), respectively, apart from ABD. Half (8/16) of the patients were male. Tendency of a preceded disease (ABD or ALD) was not observed. In six cases of concomitant ABD and PSC, three had ulcerative colitis complication. Distinguishing ABD associated ALD from a comorbidity of ABD and ALD may be difficult. Thus, the pathophysiology and clinical features of concomitant cases of ABD and ALD need to be further elucidated.
Groundwater plays a vital role in meeting the demands of the water supply for irrigation, human consumption and industrial needs. Groundwater level simulation gets its importance in predicting and planning of water resource management systems. In this study, groundwater levels of the three major southern districts of India were taken for analysis, the districts are Tirunelveli, Thoothukudi and Kanniyakumari. These districts were situated in the banks of the river Thamirabarani. The historic groundwater levels from the year 1991 to 2019 were taken for this analysis. Quarterly data were considered for this research. So data sets for each district would be 29x4=116. These were feed into the artificial neural network tool and after several training activities a final model for each district is arrived. The predicted values were found to be much closer to the observed values. Co-relation co-efficient value R value of 0.99307, MSE of 0.27 and RMSE of 0.519 denotes the model is well-fitted one to predict future groundwater levels of Tirunelveli district. Similarly, Thoothukudi model showed MSE of 0.24, RMSE of 0.49 and R value equal to 0.9013. Kanniyakumari district model showed MSE of 0.33, RMSE of 0.109 and R value equal to 0.924.
Background: Data mining is characterized as a process of transforming data information into a humancomprehensible code format such as rules, formula, algorithm, and so on. Bioinformatics is developed to solve a \nbiological problem by using data mining technique. Identifying biomedical domain entities is a difficult task and the\nenhanced model is used to classify the entities from biomedical literature full text articles in PubMed database.\nMethods: The enhanced model involves 3 stages namely pre-processing, identification of the entities using dictionarybased approach and verification and validation with benchmarking databases. In Dictionary based approach, Disgenet \nand Pubtator are considered as the dictionary which is a benchmarking database. This approach defines entities using \nthe en_ner_bionlp13cg_md model from spacy package. \nResults and Conclusion: For experimental purposes, 99 full text articles related to Alzheimer\'s disease are considered \nwhich are downloaded from NCBI. Finally, demonstrated that our enhanced model for dictionary based approach \noutperforms in aspects of accuracy, precision and retrieval value. The enhanced model achieved 82% of accuracy \noverall. Compared to state-of -art method, the model obtained the better accuracy. These results suggested that the \nenhanced model is obtained high performance for extracting biomedical entities from PubMed articles. The \nimprovement is mostly due to the dictionary because Disgenet and Pubtator are considered the dictionary in the \nenhanced work. Since, the method is more fitting to classify biomedical entities.
In this research, the chemical co-precipitation method was adapted to synthesis strontium ferrite (SrFe2O4) nanoparticles. The relationship of the calcination temperatures (650, 750 and 850̊ C) was investigated in detail. The structural, elemental, morphological and magnetic properties of strontium ferrite nanoparticles were examined by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and vibrating sample magnetometer (VSM) techniques. The XRD reveals the orthorhombic structure and FTIR transmittance spectra showed strontium ferrite related molecular functional groups. The highest saturation magnetization (Ms) of 22.17 obtained from the VSM study and SEM micrograph showed the spherical nanoparticles with less agglomerated structure.
