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Showing 5 results for Sattari
Cytotoxicity of nano-hydroxyapatite on human-derived oral epithelium cell line: an in vitro study
Farid Abassi , Mandana Sattari , Noushin Jalayer Naderi, Marzie Sorooshzadeh ,
Volume 74, Issue 5 (August 2016)
Abstract

Background: Hydroxyapatite nanoparticles have a more surface contact and solubility than conventional hydroxyapatite. Hydroxynanoparticles enhances the biological and mechanical properties of new regenerated tissues. The hydroxyapatite nanoparticles have received attention as a new and effective osseous graft for using as scaffolds in bone regeneration. The reports on hydroxyapatite nanoparticles biocompatibility are controversial. It has been shown that hydroxyapatite nanoparticles induces inflammatory reaction and apoptosis. The aim of the present study was to evaluate the cytotoxicity of nano-hydroxyapatite on the human epithelial cells.

Methods: The study was experimental and completed in vitro. The study was carried out in department of Immonulogy, Faculty of Medicine, Shahid Beheshti University of Medical Sciences in November 2014. The human-derived oral epithelium cell line (KB) obtained from Pasteur Institute, Tehran, Iran were exposed to hydroxyapatite nanoparticles at 0.01, 0.05, 0.1, 0.5, 0.75, 1, 2.5 and 5 mg/ml concentrations in 24, 48 and 72 hours. Rod-shaped hydroxyapatite nanoparticles with 99% purity and maximum 100 nm sized particles were used. Methylthiazol tetrazolium bromide (MTT) method was employed for cell vitality evaluation. Enzyme-linked immunosorbent assay (ELISA) was used for assessing the viability of cells. Distilled water and fetal bovine serum (FBS) were positive and negative controls. ANOVA and Duncan tests were used for statistical analysis.

Results: The cytotoxicity of different concentrations of hydroxyapatite nanoparticles on human-derived oral epithelium cell line in 24 (P< 0.001), 48 (P< 0.001) and 72 hours (P< 0.001) was significantly different. The nano-hydroxyapatite particles at 0.5 to 1 mg/ml had the highest cytotoxicity effect on human-derived oral epithelium cells in 24, 48 and 72 hours. Lower concentrations than 0.05 mg/ml had the best biocompatibility properties in 24, 48 and 72 hours.

Conclusion: Hydroxyapatite nanoparticles had a good biocompatibility. The biocompatibility of hydroxyapatite nanoparticles were dose and time dependent. The lower concentrations than 0.05 mg/ml of nano-hydroxyapatite had the best biocompatibility over time.


Using data mining techniques for predicting the survival rate of breast cancer patients: a review article
Hossein Bagherian, Shaghayegh Haghjooy Javanmard, Mehran Sharifi, Mohammad Sattari,
Volume 79, Issue 3 (june 2021)
Abstract
 
  This review was conducted between December 2018 and March 2019 at Isfahan University of Medical Sciences. A review of various studies revealed what data mining techniques to predict the probability of survival, what risk factors for these predictions, what criteria for evaluating data mining techniques, and finally what data sources for it have been used to predict the survival of breast cancer patients. This review is based on the Prism statement consisting of published studies in the field of predicting the survival of breast cancer patients using data mining techniques from 2005 to 2018 in databases such as Medline, Science Direct, Web of Science, Embase data and Scopus. After searching in these databases, 527 articles were retrieved. After removing duplicates and evaluating the articles, 21 articles were used. The three techniques of logistic regression, decision tree, and support vector machine have been most used in articles. Age, tumor grade, tumor stage, and tumor size are used more than other risk factors. Among the criteria, the accuracy criterion was used in more studies. Most of the studies used the Surveillance, Epidemiology, and End Results Program (SEER) dataset. Typically, in the field of survival probability prediction, data mining techniques in the field of classification are given more attention due to their adaptation to this field. Accordingly, data mining techniques such as decision tree techniques, logistic regression, and support vector machine were used in more studies than other techniques. The use of these techniques can provide a good basis for clinicians to evaluate the effectiveness of different treatments and the impact of each of these methods on patients' longevity and survival. If the output of these techniques is used to provide the data input required by a decision support system, clinicians can provide risk factors related to the patient, the patient's age, and the patient's physical condition when providing services to breast cancer patients. Through the outputs provided by the decision support system, they provided the most optimal decision to choose the best treatment method and consequently increase patient survival.

Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques
Firouze‬h Moeinzadeh, Mohammad Hossein Rouhani , Mojgan Mortazavi , Mohammad Sattari,
Volume 79, Issue 6 (September 2021)
Abstract
Background: Millions of deaths occur around the world each year due to lack of access to appropriate treatment for chronic kidney disease patients. Given the importance and mortality rate of this disease, early and low-cost prediction is very important. The researchers intend to identify chronic kidney disease through the optimal combination of techniques used in different stages of data mining.
Methods: This cross-sectional research was conducted from February 1999 to May 2014. The used data set included 4145 samples and 32 attributes, where Each sample corresponded to a patient and each attribute corresponded to the demographic and clinical traits. There were several eligibility criteria for the patients for clinical testing. These criteria for the clinical testing included having 18 years of age and older, living in Isfahan city, willing to participate in the study, lack of fever and cold during laboratory tests, no strenuous exercise 48 hours before laboratory tests, and fasting. Individuals who had an incomplete questionnaire or were unwilling to perform accurate tests were excluded from the study. The target variable is kidney disease, the values of which include sick and healthy. Four data mining techniques have been used in the dataset. These techniques are support vector machine (SVM), random forest (RF), artificial neural network (ANN) and Chi-square automatic interaction detection (CHAID).
Results: Accuracy is the evaluation criteria for comparing available data mining methods. Based on the accuracy criterion, the support vector machine performed better than other techniques (random forest, neural network and CHAID). The best rule is that if the patients consume salt in their diet, their age is between 50 and 69, and they have diabetes. they are 82% more likely to develop chronic kidney disease.
Conclusion: The derived rules also showed that if we use salt and we have diabetes, we are at the risk of developing chronic kidney disease. Moreover, having diabetes can increase the risk of mortality in chronic kidney patients. Aged people should also be more careful about getting chronic kidney disease. Because, they are more prone to develop chronic kidney disease.
 

Predicting the cause of kidney stones in patients using random forest, support vector machine and neural network
Mojgan Mortazavi, Abdolamir Atapour, Maryam Mohammadi, Mohammad Sattari,
Volume 79, Issue 9 (December 2021)
Abstract
Background: Today, with the advancement of technology in various fields, the importance of recording data in the field of health is increasing so much that for many diseases around the world, including kidney disease, registration systems have been set up. This is happening in our country and in the future, the number of these systems will increase. The medical data set contains valuable information that will be time-consuming and costly to obtain using laboratory methods, so there is a need for low-cost methods for extracting information. This study focuses on developing a predictive model for classifying the cause of kidney stones in Isfahan using three data mining techniques.
Methods: This cross-sectional research has been done from February 2021 to May 2021. The used medical data set includes information of 353 kidney stone patients in Isfahan. In this study, six target attributes of sodium, phosphate, oxalate, citrate, cysteine and uric acid were identified. The techniques for each of the 6 attributes are used separately. The techniques used in this study were three data mining techniques including random forest (RF), artificial neural network (ANN) and support vector machine (SVM).
Results: The best performance in terms of accuracy is related to support vector machine techniques in uric acid class, support vector machine in oxalate class and neural network in cysteine class. The worst performance is related to the random forest technique in the citrate class. The safest rules with a 66% confidence level are for the citrate and sodium classes, and the least reliable rule with a 50% confidence level is for the oxalate class.
Conclusion: Kidney stones can occur due to various reasons such as low citrate and high calcium oxalate. For example, for citrate, factors such as blood pH (potential of hydrogen), blood sugar and blood pressure are effective. To prevent any of the causes of kidney stones, factors should be controlled.

Predicting the Performance of Faculty Members of Medical Universities in Providing Virtual Learning in the Covid-19 Pandemic in terms of Problem-Solving Methods and Their Individual Social Characteristics: A Data Mining Study
Mohammad Sattari, Rahele Samouei,
Volume 80, Issue 12 (March 2023)
Abstract
Background: In the Covid-19 Pandemic, virtual education in universities became essential and came with some challenges, especially for professors who had the role of presenters. In this regard, the study was conducted to predict the performance of professors in providing virtual training in Covid-19 in terms of problem-solving methods and their demographics.
Methods: A descriptive-analytical study was performed on 252 professors of Iranian universities of medical sciences from 2021 April to 2021. Also, demographic characteristics such as gender, field of study, position, job rank and work experience were asked. The faculty members' performance questionnaire in providing virtual training (α=0.89) and the problem-solving methods questionnaire (α=0.75) was administered virtually and the data were analyzed by Random forest, CHAID and ID3 techniques.
Results: Based on used data mining methods findings, factors related to teachers' satisfaction with their performance in providing virtual education were "the possibility of monitoring the performance of homework", "establishing order and regulations", "preparing standard educational content", "using multimedia content", "Mastery of software, educational systems, and multimedia content", and "possibility of examining the quality and quantity of students' learning". Also, interpersonal problem-solving methods, such as "believing in the role of personality traits of people in their behavior", "solving problems with effort and follow-up", "notifying people's mistakes in interpersonal interactions", "giving people the opportunity to check their behavior", "proposing solutions to solve problems for the benefit of both parties", and "dividing big problems into smaller parts" have played a big role in professors' satisfaction about their teaching methods. These characteristics are related to more basic areas such as self-regulation, pursuit and challenge, agreeableness, and realism.
Conclusion: The results of the study showed that the performance of teachers in providing virtual education is influenced by some behavioral factors and individual situational abilities. However, despite the virtual training implementation difficulties, it is a productive opportunity that can be used in the days of returning for conditions (after-covid 19 condition) without physical distance along with face-to-face training.

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