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Westergaard Hinrichsen posted an update 6 months ago
Liposomal formulations produced by hydration of lipid films exhibited constant rate of terpineol-4 release. In addition, their incorporation into biomaterials, such as sponges, nanofibers and films, showed great potential for treating infections. Mainly due to the advantages of their incorporation into new drug delivery systems over conventional formulations, there is interest in the development of systems containing TTO as a pharmaceutical ingredient of plant origin. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. OBJECTIVES Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. METHODS According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. RESULTS Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. CONCLUSION The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Data mining algorithms are extensively used to classify the data, in which prediction of disease using minimal computation time plays a vital role. OBJECTIVES The aim of this paper is to develop the classification model from reduced features and instances. METHODS In this paper we proposed four search algorithms for feature selection the first algorithm is Random Global Optimal (RGO) search algorithm for searching the continuous, global optimal subset of features from the random population. The second is Global and Local Optimal (GLO) search algorithm for searching the global and local optimal subset of features from population. The third one is Random Local Optimal (RLO) search algorithm for generating random, local optimal subset of features from the random population. Finally the Random Global and Optimal (RGLO) search algorithm for searching the continuous, global and local optimal subset of features from the random population. RGLO search algorithm combines the properties of first three stated algorithm. The subsets of features generated from the proposed four search algorithms are evaluated using the consistency based subset evaluation measure. Instance based learning algorithm is applied to the resulting feature dataset to reduce the instances that are redundant or irrelevant for classification. The model developed using naïve Bayesian classifier from the reduced features and instances is validated with the tenfold cross validation. RESULTS Classification accuracy based on RGLO search algorithm using naïve Bayesian classifier is 94.82% for Breast, 97.4% for DLBCL, 98.83% for SRBCT and 98.89% for Leukemia datasets. CONCLUSION The RGLO search based reduced features results in the high prediction rate with less computational time when compared with the complete dataset and other proposed subset generation algorithm. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Basal-like carcinoma is one of the breast subtypes that lacks expression of the estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). It has a poor prognosis and aggressive behavior. It is in a heterogeneous group with various other types of cancer, including metaplastic carcinoma, carcinomas with medullary features, medullary carcinoma, adenoid cystic carcinoma, secretory carcinoma, and invasive carcinoma arising in the setting of BRCA1 mutations. Imaging features of basal-like cancers have not been uniform, and there are no studies with imaging comparisons between basal-like carcinomas. OBJECTIVES To compare imaging features of basal-like carcinomas and to understand their characteristics. METHODS By using our radiologic database, we retrospectively searched 37 cases of metaplastic carcinoma and 44 cases of invasive carcinoma with medullary features (ICMF). Two radiologists reviewed images according to ACR BI-RADS lexicon. RESULTS The higher Ki-67 and absence of calcifications were statistically significant in ICMF than in metaplastic carcinoma. Metaplastic carcinoma demonstrated oval shape and parallel orientation more frequently. ICMF showed more irregular shape and angular margin on ultrasound, irregular or spiculated margin on breast MRI. ICMF showed more delayed washout pattern of enhancement than metaplastic carcinoma. Intratumoral T2, a very high signal was noted more in metaplastic carcinoma. CONCLUSION Our study presents variable imaging features observed between basal-like carcinomas. Although it is not sufficient to predict clinical progress, aggressiveness or prognosis of basal-like carcinomas, the results of this study will be helpful in understanding and diagnosing various basallike carcinomas. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Automatic approach to vertebrae segmentation from computed tomography (CT) images is very important in clinical applications. As the intricate appearance and variable architecture of vertebrae across the population, cognate constructions in close vicinity, pathology, and the interconnection between vertebrae and ribs, it is a challenge to propose a 3D automatic vertebrae CT image segmentation method. OBJECTIVE The purpose of this study was to propose an automatic multi-vertebrae segmentation method for spinal CT images. METHODS Firstly, CLAHE-Threshold-Expansion was preprocessed to improve image quality and reduce input voxel points. learn more Then, 3D coarse segmentation fully convolutional network and cascaded finely segmentation convolutional neural network were used to complete multi-vertebrae segmentation and classification. RESULTS The results of this paper were compared with the other methods on the same datasets. Experimental results demonstrated that the Dice similarity coefficient (DSC) in this paper is 94.