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Moreover, in both groups, the effect of gestational age on reducing SPAP was more convincing than that of the neonatal age. Further, in both groups, a significant reverse correlation was observed between the SPAP and the neonatal weight; however, it appeared to be markedly higher in group 1.

Our study renders IVF as being culpable in the incidence of pulmonary hypertension among neonates. Hence, to detect the likelihood of pulmonary arterial hypertension in IVF neonates, it is recommended to monitor their PAP during the neonatal period, and thereby facilitate them with the required treatment.

Our study renders IVF as being culpable in the incidence of pulmonary hypertension among neonates. Hence, to detect the likelihood of pulmonary arterial hypertension in IVF neonates, it is recommended to monitor their PAP during the neonatal period, and thereby facilitate them with the required treatment.Proteins are complex macromolecules accountable for the biological processes in the cell. In biomedical research, the images of protein are extensively used in medicine. The rate at which these images are produced makes it difficult to evaluate them manually and hence there exists a need to automate the system. The quality of images is still a major issue. In this paper, we present the use of different image enhancement techniques that improves the contrast of these images. Besides the quality of images, the challenge of gathering such datasets in the field of medicine persists. VE-822 solubility dmso We use generative adversarial networks for generating synthetic samples to ameliorate the results of CNN. The performance of the synthetic data augmentation was compared with the classic data augmentation on the classification task, an increase of 2.7% in Macro F1 and 2.64% in Micro F1 score was observed. Our best results were obtained by the pretrained Inception V4 model that gave a fivefold cross-validated macro F1 of 0.603. The achieved results are contrasted with the existing work and comparisons show that the proposed method outperformed.Anabolic effects of low magnitude high frequency (LMHF) vibrations on bone tissue were consistently shown in the literature in vivo, however in vitro efforts to elucidate underlying mechanisms are generally limited to 2D cell culture studies. Three dimensional cell culture platforms better mimic the natural microenvironment and biological processes usually differ in 3D compared to 2D culture. In this study, we used laboratory grade filter paper as a scaffold material for studying the effects of LHMF vibrations on osteogenesis of bone marrow mesenchymal stem cells in a 3D system. LMHF vibrations were applied 15 min/day at 0.1 g acceleration and 90 Hz frequency for 21 days to residing cells under quiescent and osteogenic conditions. mRNA expression analysis was performed for alkaline phosphatase (ALP) and osteocalcin (OCN) genes, Alizarin red S staining was performed for mineral nodule formation and infrared spectroscopy was performed for determination of extracellular matrix composition. The highest osteocalcin expression, mineral nodule formation and the phosphate bands arising from the inorganic phase was observed for the cells incubated in osteogenic induction medium with vibration. Our results showed that filter paper can be used as a model scaffold system for studying the effects of mechanical loads on cells, and LMHF vibrations induced the osteogenic differentiation of stem cells.The detection, counting, and precise segmentation of white blood cells in cytological images are vital steps in the effective diagnosis of several cancers. This paper introduces an efficient method for automatic recognition of white blood cells in peripheral blood and bone marrow images based on deep learning to alleviate tedious tasks for hematologists in clinical practice. First, input image pre-processing was proposed before applying a deep neural network model adapted to cells localization and segmentation. Then, model outputs were improved by using combined predictions and corrections. Finally, a new algorithm that uses the cooperation between model results and spatial information was implemented to improve the segmentation quality. To implement our model, python language, Tensorflow, and Keras libraries were used. The calculations were executed using NVIDIA GPU 1080, while the datasets used in our experiments came from patients in the Hemobiology service of Tlemcen Hospital (Algeria). The results were promising and showed the efficiency, power, and speed of the proposed method compared to the state-of-the-art methods. In addition to its accuracy of 95.73%, the proposed approach provided fast predictions (less than 1 s).In this letter, a new feature descriptor called three dimensional local oriented zigzag ternary co-occurrence fused pattern ( 3 D - L O Z T C o F P ) is proposed for computed tomography (CT) image retrieval. Unlike the conventional local pattern based approaches, where the relationship between the reference and its neighbors in a circular shaped neighborhood are captured in a 2-D plane, the proposed descriptor encodes the relationship between the reference and it's neighbors within a local 3D block drawn from multiscale Gaussian filtered images employing a new 3D zigzag sampling structure. The proposed 3D zigzag scan around a reference not only provides an effective texture representation by capturing non-uniform and uniform local texture patterns but the fine to coarse details are also captured via multiscale Gaussian filtered images. In this letter, we have introduced three unique 3D zigzag patterns in four diverse directions. In 3 D - L O Z T C o F P , we first calculate the 3D local ternary pattern within a local 3D block around a reference using proposed 3D zigzag sampling structure at both radius 1 and 2. Then the co-occurrence of similar ternary edges within the local 3D cube is computed to further enhance the discriminative power of the descriptor. A quantization and fusion based scheme is introduced to reduce the feature dimension of the proposed descriptor. Experiments are conducted on popular NEMA and TCIA-CT image databases and the results demonstrate superior retrieval efficiency of the proposed 3 D - L O Z T C o F P descriptor over many local pattern based approaches in terms of average retrieval precision and average retrieval recall in CT image retrieval.