Search

Background: In children, high blood pressure can develop into hypertension and its consequences during puberty and adulthood. High blood pressure in children is often secondary to other causes, including renal diseases. Nephrolithiasis is one of the causes of secondary hypertension. The extensive use of cephalosporins in hospitals, particularly ceftriaxone, can result in nephrolithiasis. Therefore, the purpose of this study was to assess the relationship between ceftriaxone treatment and elevated blood pressure in children. Method: The research was conducted as a case-control study over an 18-month period from 2018 to 2019. In this study, blood pressure was measured in 111 children aged 3-13 years who were hospitalized at Amir Kabir Hospital in Arak and received ceftriaxone for at least 48 hours. As a control group, 111 children who did not receive ceftriaxone had their blood pressure measured. The blood pressure levels and percentiles of children in the two groups were then compared. Result: In the case and control groups, the mean age was 5.1±1.61 and 6.04±2.4 years, and the mean height was 109.17±10.71 and 114.86±12.95 cm, respectively. A slightly higher mean systolic blood pressure percentile was observed in the case group (65.59±18.17) than in the control group (65.28±14.51) (P=0.112), and the mean diastolic blood pressure percentile was also slightly higher in the case group (58.89±18.88) than in the control group (54.85±19.28) (P=0.317). The difference in diastolic blood pressure was greater than in systolic blood pressure. However, these detected differences were slight and not statistically significant. Conclusion: This study showed no association between blood pressure levels and ceftriaxone treatment in children older than three years who received the medicine for at least 48 hours. However, additional research is suggested, focusing on the effects of the medicine at higher doses and over a longer period of time following administration. [ABSTRACT FROM AUTHOR]

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today's successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study. [ABSTRACT FROM AUTHOR]

Purpose: Convolutional neural networks, as one of the most important models of deep learning, have gained high accuracy on these issues. In this study, discussion and text analysis at the sentence level and improving the performance of neural networks to detect fake news has been convolution. The network of words for bags of words in the data model so that each word according to the two-dimensional vector space to become matrices. One of the limitations of convolutional networks is that it works at the word level and cannot consider the relationship and distance between sentences. And sentence-level analysis is a major problem in this research. Sentence level analysis is a major problem in this research. In this research, a basic model based on convolutional networks is proposed in which documents are given to the network in the form of 3D tensors to solve the mentioned problem. Considering 3D tensors allows the model to learn the position of words in a sentence and achieve more accurate results in detecting fake news. Methodology: This study is applied research in which about 42,000 Persian news from different cities of Iran were collected from Twitter and using preprocessing, additional and useless data is deleted and after tagging the deleted texts, the news text is used for the proposed approach using Python software and related libraries. Findings: During testing, some machine learning algorithms had more power in classification problems, but with the changes in the structure of the convolutional network algorithm, better results were obtained than machine learning algorithms and other similar algorithms. Conclusion: Considering 3D tensors allows the model to learn the position of words in a sentence, and this proposed model has gained considerable accuracy compared to the proposed approaches in the literature. The proposed model without adding additional overhead in terms of the number of features and network depth, by changing the input has been able to achieve better and more acceptable results than other approaches in the literature and achieve an accuracy of more than 94%. [ABSTRACT FROM AUTHOR]

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers. [ABSTRACT FROM AUTHOR]