Publications by authors named "Seyyed Mohammad Razavi"

4 Publications

  • Page 1 of 1

The effect of two phosphodiesterase inhibitors on bone healing in mandibular fractures (animal study in rats).

J Korean Assoc Oral Maxillofac Surg 2020 Aug;46(4):258-265

Torabinejad Dental Research Center and Department of Oral and Maxillofacial Pathology, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.

Objectives: Despite advances in maxillofacial surgery, impaired bone healing remains a concern for surgical teams. Many studies have evaluated the effects of sildenafil and pentoxifylline on bone healing. However, their effects on healing of bone fractures have not been well investigated. This study aimed to assess the effects of the phosphodiesterase inhibitors sildenafil and pentoxifylline on healing of mandibular fractures in rats.

Materials And Methods: A total of 60 rats were randomly divided into six groups of 10. Mandibular fracture was induced in all rats. After the surgical procedure, group C1 received saline, group S1 received 10 mg/kg sildenafil and group P1 received 50 mg/kg pentoxifylline. The rats were sacrificed after 1 week. Groups C4, S4, and P4 received pharmaceutical therapy as in groups C1, S1, and P1 but were sacrificed after 4 weeks. The samples then underwent histological analysis.

Results: The mean rate of bone healing of mandibular fractures in groups S1 and P1 was significantly higher than in group C1 at 1 week (<0.001). The mean rate of bone healing of mandibular fractures in group P1 was higher than in group S1 at 1 week (=0.04). The mean rate of bone healing of mandibular fractures in groups S4 (=0.001) and P4 (=0.004) was significantly higher than in group C4 at 4 weeks, but no significant difference was noted in the rate of healing between groups P4 and S4 (=0.53).

Conclusion: Sildenafil and pentoxifylline can be used as adjuncts to enhance bone healing in rats.
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http://dx.doi.org/10.5125/jkaoms.2020.46.4.258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7469969PMC
August 2020

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model.

Int J Environ Res Public Health 2020 01 23;17(3). Epub 2020 Jan 23.

Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
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http://dx.doi.org/10.3390/ijerph17030731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037941PMC
January 2020

Genetic study of the BRAF gene reveals new variants and high frequency of the V600E mutation among Iranian ameloblastoma patients.

J Oral Pathol Med 2018 Jan 7;47(1):86-90. Epub 2017 Nov 7.

Department of Oral and Maxillofacial Pathology, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.

Background: Ameloblastoma is a benign, slow-growing and locally invasive tumor. It is one of the most prevalent odontogenic tumors, with an incidence rate of 1% of all oral tumors and approximately 18% of odontogenic tumors. A group of genes have been investigated in patients with ameloblastoma. The BRAF V600E mutation has been implicated as the most common mutation in ameloblastoma. The presence or absence of this mutation has been associated with several clinicopathological properties, including location, age at diagnosis, histology, and prognosis. Although some populations have been investigated so far, little data are available on the Iranian population. The current research was launched to study the BRAF V600E mutation among a cohort of Iranian patients with ameloblastoma.

Methods: In this clinicopathological and molecular biology study, a total of 19 formalin-fixed, paraffin-embedded tissues were studied. DNA extraction was performed, followed by PCR-sequencing of exons 10 and 15 of the BRAF gene to identify mutations. In silico analysis was performed for the identified variants. Results were analyzed by T test, Chi-square, and Fisher's exact test.

Results: Totally, 12 of 19 samples (63%) harbored the p. V600E hotspot mutation. In addition, we identified several variants, two of which were novel. The c.1769T>G (p. V590G) and c.1751C>T (p.L584F) as the novel variants showed a possible damaging effect by in silico analysis. No variant was found within exon 10.

Conclusions: Our study confirms the role of BRAF mutations in ameloblastoma in the Iranian patients studied.
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http://dx.doi.org/10.1111/jop.12610DOI Listing
January 2018

Electrocardiogram Based Identification using a New Effective Intelligent Selection of Fused Features.

J Med Signals Sens 2015 Jan-Mar;5(1):30-9

Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335143PMC
February 2015