Showing 7 results for Moudi
Hamidollah Iri, Dr Ghahreman Mahmoudi, Dr Mohammad Ali Jahani Tiji,
Volume 5, Issue 2 (10-2017)
Abstract
Background & Objective: The fair distribution of medical specialists among the population of a country is one of the requirements for the public health. We aimed to investigating the distribution of medical specialists using Gini coefficient in all governmental hospitals in two medical universities of Golestan (15 hospitals) and Mazandaran (23 hospitals) provinces.
Methods: The present practical study was conducted using descriptive and analytical methods. The research data including the number of physicians, population and number of active beds in each city and province were obtained from the deputy of treatment of medical universities in each province. The Lorenz curve and the Gini coefficient were used to analyze the distribution of specialists using Excel software. T-test was used to compare the Gini coefficients between the two provinces. Multiple regression tests were performed using SPSS software version 16 to investigate the relationship between variables.
Results: The results showed that Gini coefficients on the basis of population in Mazandaran province were within optimum limit (less than 0.2). However, Gini coefficients on the basis of population in Golestan province were undesirable (more than 0.2) and there was also an inequality in the Gini coefficients based on the number of population between the two provinces of Golestan and Mazandaran (P=0.000, t=17.89).
Conclusion: According to the findings, the distribution of specialist physicians is desirable in Mazandaran province based on population. However, there was inequality in the distribution of specialist physicians in Golestan province. The accurate and fair estimation of the required human resources and the distribution on the basis of population and required indicators could lead to a reduction in the cost of treatment for families and better efficiency of health resources.
Shima Kazemi Malek Mahmoudi *, Kiomars Niaz Azari, Negin Jabari,
Volume 6, Issue 2 (6-2018)
Abstract
Background & Objectives: Knowledge-based economy is one of the major parts of the resistance economy and is recognized as the essentials of the information technology age. As one of the most important educational units and the custodian of community health, universities of medical sciences play a pivotal role in the success of resistance economy based on the knowledge-based economy. Among the employees of a university, the managers and faculty members play an important role in this regard. This study aimed to explain the dimensions of the resistance economy based on the knowledge-based economy in universities of medical sciences.
Materials and Methods: This qualitative research had a purposeful sampling method. Data were collected via library and field research. In the library method, the relevant specialized scientific texts and resources were exploited, whereas in the fieldwork, the researcher interviewed 24 faculty members. The interviews continued until saturation, and after being recorded, they were transcribed in Word software, coded, classified, and encoded by Microsoft OneNote. After that, the interviews were analyzed. It is notable that ethical considerations were observed during the research process. In the field method, the texts and books were evaluated and information searched in scientific and specialized websites. In addition, relevant articles recently published in valid international journals were translated, and primary data with the topic of translation were collected. In the field research and after coordination with faculty members and main elites with a history of article or innovation production or a management background, the opinions of the interviewees were asked about some questions in a semi-structured manner. In addition, three-stage coding, open coding, axial coding, and selective coding were applied in the research. To perform open coding, the main sentences were recorded as codes, followed by classifying the similar codes. In axial coding, the classes were related to their subclasses by comparing the primary classes of open coding and revolving the ones that resembled around the common axis. In selective coding, the integration and refining process of the categories was performed to form and present the model, and the dimensions were extracted and explained.
Results: From the perspective of the faculty members and key experts, resistance economy based on knowledge-based economy in universities of medical sciences could be explained in dimensions of information and communications technology, innovation and entrepreneurship in medical sciences, human resources and knowledge-based team, administrative and structural system, resources of equipment and abilities, policy and socio-environmental impacts. Each of these dimensions also included sub-groups and special themes.
Conclusion: According to the results of the study, it could be stated that moving towards the knowledge-based economy is one of the requirements of the country on the path to economic development, which involves dimensions such as information and communications technology, innovation and entrepreneurship, specialized knowledge-generating team, strategic planning and policy-making, formulating and ensuring the legal framework and legal system, supplying and developing electronic infrastructures, and paying attention to capabilities and resources of facilities.
Samira Mahmoudi, Rozita Nasiri, Abolfazl Jafari Sales,
Volume 7, Issue 4 (12-2019)
Abstract
Background and objectives: Microbial resistance to antibiotics is one of the most common problems in the health care system. Therefore, many efforts have been performed to find new compounds as antimicrobial compounds. This study carried out to investigate the in-vitro antibacterial effect of methanolic extract of peppermint on standard Staphylococcus aureus, Bacillus cereus, Escherichia coli and Pseudomonas aeruginosa strain.
Material and Methods: In this experimental laboratory study, after collecting and performing pharmacognosy evaluations, methanolic extract of the peppermint plant was prepared and its antimicrobial effects on several bacteria were determined at concentrations of 20 to 400 mg/ml using the agar well diffusion method, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) methods. The test was repeated five times for each bacterium and the collected data were analyzed using SPSS software.
Results: It was revealed that the methanolic extract of peppermint had antimicrobial effects against standard Staphylococcus aureus, Bacillus cereus, Escherichia coli and Pseudomonas aeruginosa. The highest effect was observed in S. aureus and the least in P. aeruginosa. MIC and MBC of methanol extract on S. aureus, B. cereus, E. coli, and P. aeruginosa were 6.5-15.5, 15.5-25, 50-100, and 100-200 mg/ml, respectively.
Conclusion: The results of this study reveal that peppermint can be considered as an antibacterial medicinal herb and that if the concentration of the extract is higher, its antibacterial properties will also increase. Thus, it can be used as an alternative to the usual chemical drugs in the treatment of infections after evaluating their effects in vitro.
Fatemeh Monirian, Reyhane Abedi, Negar Balmeh, Samira Mahmoudi, Fereshteh Mirzaei Poor,
Volume 8, Issue 3 (10-2020)
Abstract
Background and objective: Some common problems in the health care system are Microbial resistance to antibiotics, the side effects of food additives, and preservatives. Considering the antibiotic resistance of microorganisms and the need to identify new compounds, the present study was conducted to determine the antimicrobial effects of Artemisia extracts.
Material And Method The study was performed in two stages including extraction and determination of antibacterial properties of aqueous, ethanolic, methanolic, acetone/ethanolic, and hydroethanolic extracts of Artemisia on standard Pseudomonas aeruginosa, Streptococcus pyogenes, and oral bacteria sample. The well diffusion method was performed to evaluate the concentration that had an inhibitory and bactericidal effect.
Result: Aqueous extract had an effect on the oral bacteria sample (8 mm), the methanolic extract had an effect on S. pyogenes and oral bacteria sample (15 mm) and finally acetone/ethanolic extracts had antimicrobial properties against S. pyogenes (16 mm). The concentration used for all five extracts was 50 mg/ml and showed an inhibition effect on the growth of S. pyogenes standard strain and oral bacteria sample. The less serial dilutions of extracts were tested but no antibacterial effects were seen. So, 50 mg/ml was the minimum concentration that had an inhibitory and bactericidal effect.
Conclusion: It can be inferred that aqueous, methanolic, and acetone/ethanolic extracts of Artemisia had the highest inhibitory effect on S. pyogenes and the oral bacteria sample. Consequently, by applying different extraction methods and by utilizing different solvents, it may be possible to more efficiently obtain biomaterials with antimicrobial properties from this plant.
Emadoddin Moudi, Hamidreza Kamalinia, Sepehr Ramezani, Abazar Akbarzade Pasha, Omid Majidi,
Volume 11, Issue 1 (7-2023)
Abstract
This article reports a pelvic hydatid cyst in a 10-year-old boy who initially presented with right flank pain. Computed tomography (CT) revealed a cystic lesion containing internal densities, which was found to be a hydatid cyst between the bladder and rectum during surgery. The lesion was excised and sent to a pathology laboratory for further examination.
Khadijeh Haghighat , Fariba Mahmoudi , Homayoun Khazali,
Volume 12, Issue 3 (12-2024)
Abstract
Background: Chrysin is a natural bioactive compound belonging to the flavonoid group. The pain-relieving effects of chrysin have been reported in several studies; however, the molecular mechanism underlying its analgesic properties remains unknown. In the present study, the analgesic effect of chrysin was investigated on the hypothalamic Calcitonin Gene-Related Peptide (CGRP) and Tachykinin 1 (Tac1) gene expression in a formalin-induced pain model.
Methods: Twenty male rats weighing 200 ± 10 g were divided into four groups. Pain was induced by injecting 50 μl of formalin into the hind paw. The control and formalin groups received saline. In addition, 20 or 40 µg of chrysin was injected into pain-induced rats via the third cerebral ventricle. After 30 minutes, a behavioral test was conducted. Hypothalamus samples were then dissected, and real-time polymerase chain reaction (PCR) was performed to measure gene expression.
Results: The mRNA levels of CGRP and Tac1 significantly increased in the formalin-treated rats compared to the control group. In contrast, the mRNA levels of CGRP and Tac1 were significantly reduced in the chrysin-treated groups compared to the formalin group. Furthermore, the pain score was significantly lower in the chrysin-treated groups compared to the formalin group.
Conclusion: The pain-relieving effects of chrysin were mediated through the downregulation of hypothalamic CGRP and Tac1 in the pain model rats.
Nahid Nematy , Emadoddin Moudi , Masoud Arabfard ,
Volume 13, Issue 2 (6-2025)
Abstract
Background: Bladder cancer (BC) is a life-threatening malignancy that can be successfully treated if diagnosed in its early stages. Machine learning techniques, by using large biological databases, are suggested as important approaches for identifying accurate diagnostic biomarkers. The present study aimed to introduce a simple and accurate model for the diagnosis of BC.
Methods: RNA-sequencing information of 412 primary bladder tumors versus 19 normal bladder tissues from The Cancer Genome Atlas were analyzed using the TCGAbiolinks R package to identify differentially expressed genes (DEGs). Gene ontology properties and the corresponding pathways of DEGs were investigated using the online ShinyGO tools. To develop a diagnostic model for BC, two binary classifier machine learning algorithms, C5.0 and CHAID, were employed in three subgroups of train, test, and validation sets using SPSS Modeler version 18.1. Their efficacy was evaluated using performance measures for binary classification.
Results: Most of the identified DEGs were associated with microtubule organization, coagulation, and myelination. Based on the constructed models, four important RNAs (Tubulin Polymerization-Promoting Protein: ENSG00000171368, Proteolipid Protein-1: ENSG00000123560, RP11-473E2: ENSG00000228877, and Coagulation Factor X: ENSG00000126218) were identified as important classifiers for diagnosis in both C5.0 and CHAID models. The CHAID model demonstrated superior performance in the testing dataset, achieving an accuracy of 98.75%, an F1-score of 99.36%, and an AUC of 99.4%.
Conclusion: According to the results, machine learning algorithms are beneficial for the diagnosis of BC and potentially useful for improving personalized medicine in BC patients. The developed model may serve as a non-invasive, data-driven tool to support early diagnosis and personalized treatment planning in clinical settings. Further evaluation using laboratory tests is suggested to validate the obtained results.
