Displaying publications 1 - 20 of 51 in total

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  1. Vepa A, Saleem A, Rakhshan K, Daneshkhah A, Sedighi T, Shohaimi S, et al.
    PMID: 34207560 DOI: 10.3390/ijerph18126228
    BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making.

    METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.

    RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

  2. Salari N, Heydari M, Hassanabadi M, Kazeminia M, Farshchian N, Niaparast M, et al.
    J Orthop Surg Res, 2020 Oct 28;15(1):495.
    PMID: 33115483 DOI: 10.1186/s13018-020-01999-7
    BACKGROUND: The Dupuytren disease is a benign fibroproliferative disorder that leads to the formation of the collagen knots and fibres in the palmar fascia. The previous studies reveal different levels of Dupuytren's prevalence worldwide; hence, this study uses meta-analysis to approximate the prevalence of Dupuytren globally.

    METHODS: In this study, systematic review and meta-analysis have been conducted on the previous studies focused on the prevalence of the Dupuytren disease. The search keywords were Prevalence, Prevalent, Epidemiology, Dupuytren Contracture, Dupuytren and Incidence. Subsequently, SID, MagIran, ScienceDirect, Embase, Scopus, PubMed and Web of Science databases and Google Scholar search engine were searched without a lower time limit and until June 2020. In order to analyse reliable studies, the stochastic effects model was used and the I2 index was applied to test the heterogeneity of the selected studies. Data analysis was performed within the Comprehensive Meta-Analysis Software version 2.0.

    RESULTS: By evaluating 85 studies (10 in Asia, 56 in Europe, 2 in Africa and 17 studies in America) with a total sample size of 6628506 individuals, the prevalence of Dupuytren disease in the world is found as 8.2% (95% CI 5.7-11.7%). The highest prevalence rate is reported in Africa with 17.2% (95% CI 13-22.3%). According to the subgroup analysis, in terms of underlying diseases, the highest prevalence was obtained in patients with type 1 diabetes with 34.1% (95% CI 25-44.6%). The results of meta-regression revealed a decreasing trend in the prevalence of Dupuytren disease by increasing the sample size and the research year (P < 0.05).

    CONCLUSION: The results of this study show that the prevalence of Dupuytren disease is particularly higher in alcoholic patients with diabetes. Therefore, the officials of the World Health Organization should design measures for the prevention and treatment of this disease.

  3. Salari N, Khazaie H, Hosseinian-Far A, Khaledi-Paveh B, Kazeminia M, Mohammadi M, et al.
    Hum Resour Health, 2020 12 17;18(1):100.
    PMID: 33334335 DOI: 10.1186/s12960-020-00544-1
    BACKGROUND: Stress, anxiety, and depression are some of the most important research and practice challenges for psychologists, psychiatrists, and behavioral scientists. Due to the importance of issue and the lack of general statistics on these disorders among the Hospital staff treating the COVID-19 patients, this study aims to systematically review and determine the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients.

    METHODS: In this research work, the systematic review, meta-analysis and meta-regression approaches are used to approximate the prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients. The keywords of prevalence, anxiety, stress, depression, psychopathy, mental illness, mental disorder, doctor, physician, nurse, hospital staff, 2019-nCoV, COVID-19, SARS-CoV-2 and Coronaviruses were used for searching the SID, MagIran, IranMedex, IranDoc, ScienceDirect, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases. The search process was conducted in December 2019 to June 2020. In order to amalgamate and analyze the reported results within the collected studies, the random effects model is used. The heterogeneity of the studies is assessed using the I2 index. Lastly, the data analysis is performed within the Comprehensive Meta-Analysis software.

    RESULTS: Of the 29 studies with a total sample size of 22,380, 21 papers have reported the prevalence of depression, 23 have reported the prevalence of anxiety, and 9 studies have reported the prevalence of stress. The prevalence of depression is 24.3% (18% CI 18.2-31.6%), the prevalence of anxiety is 25.8% (95% CI 20.5-31.9%), and the prevalence of stress is 45% (95% CI 24.3-67.5%) among the hospitals' Hospital staff caring for the COVID-19 patients. According to the results of meta-regression analysis, with increasing the sample size, the prevalence of depression and anxiety decreased, and this was statistically significant (P 

  4. Nankali A, Kazeminia M, Jamshidi PK, Shohaimi S, Salari N, Mohammadi M, et al.
    Health Qual Life Outcomes, 2020 Sep 24;18(1):314.
    PMID: 32972380 DOI: 10.1186/s12955-020-01561-3
    BACKGROUND: Endometriosis is one of the most common causes of infertility. The causes of the disease and its definitive treatments are still unclear. Moreover, Anti-Mullerian Hormone (AMH) is a glycoprotein dimer that is a member of the transient growth factors family. This research work aimed to identify the effect of unilateral and bilateral laparoscopic surgery for endometriosis on AMH levels after 3 months, and 6 months, using meta-analysis.

    METHODS: In this study, the articles published in national and international databases of SID, MagIran, IranMedex, IranDoc, Cochrane, Embase, Science Direct, Scopus, PubMed, and Web of Science (ISI) were searched to find electronically published studies between 2010 and 2019. The heterogeneous index between studies was determined using the I2 index.

    RESULTS: In this meta-analysis and systematic review, 19 articles were eligible for inclusion in the study. The standardized mean difference was obtained in examining of unilateral laparoscopic surgery for endometriosis (before intervention 2.8 ± 0.11, and after 3 months 2.05 ± 0.13; and before intervention 3.1 ± 0.46 and after 6 months 2.08 ± 0.31), and in examining bilateral laparoscopic surgery for endometriosis examination (before intervention 2.0 ± 08.08, and after 3 months 1.1 ± 0.1; and before intervention 2.9 ± 0.23 and after 6 months 1.4 ± 0.19).

    CONCLUSION: The results of this study demonstrate that unilateral and bilateral laparoscopic surgery for endometriosis is effective on AMH levels, and the level decreases in both comparisons.

  5. Mohammadi M, Kazeminia M, Abdoli N, Khaledipaveh B, Shohaimi S, Salari N, et al.
    Health Qual Life Outcomes, 2020 Nov 23;18(1):373.
    PMID: 33225933 DOI: 10.1186/s12955-020-01599-3
    BACKGROUND: Opioids addiction and misuse are among the major problems in the world today. There have been several preliminary studies examining the effect of methadone on depression among addicts, however, these studies have reported inconsistent and even contradictory results. Therefore, the aim of the present study was to determine the effect of methadone on depression in addicts in Iran and around the world, using a meta-analysis approach.

    METHODS: This study was a systematic review and meta-analysis including articles published in the SID, MagIran, IranMedex, IranDoc, Cochrane, Embase, ScienceDirect, Scopus, PubMed and Web of Science databases were searched systematically to find articles published from 2006 to March 2019. Heterogeneity index was determined using the Cochran's test (Qc) and I2. Considering heterogeneity of studies, the random effects model was used to estimate the standardized difference of mean score for depression. Subsequently, the level of depression reduction in Iran and worldwide in the intervention group before and after the testwas measured.

    RESULTS: A total of 19 articles met the inclusion criteria, and were therefore selected for this systematic review and meta-analysis. The sample size of the intervention group in the selected studies was 1948. According to the meta-analysis results, the mean depression score in the intervention group was 26.4 ± 5.6 and 18.4 ± 2.6 before and after intervention respectively, indicating the reducing effect of methadone on depression, and this difference was statistically significant (P 

  6. Salari N, Khazaie H, Hosseinian-Far A, Ghasemi H, Mohammadi M, Shohaimi S, et al.
    Global Health, 2020 09 29;16(1):92.
    PMID: 32993696 DOI: 10.1186/s12992-020-00620-0
    BACKGROUND: In all epidemics, healthcare staff are at the centre of risks and damages caused by pathogens. Today, nurses and physicians are faced with unprecedented work pressures in the face of the COVID-19 pandemic, resulting in several psychological disorders such as stress, anxiety and sleep disturbances. The aim of this study is to investigate the prevalence of sleep disturbances in hospital nurses and physicians facing the COVID-19 patients.

    METHOD: A systematic review and metanalysis was conducted in accordance with the PRISMA criteria. The PubMed, Scopus, Science direct, Web of science, CINHAL, Medline, and Google Scholar databases were searched with no lower time-limt and until 24 June 2020. The heterogeneity of the studies was measured using I2 test and the publication bias was assessed by the Egger's test at the significance level of 0.05.

    RESULTS: The I2 test was used to evaluate the heterogeneity of the selected studies, based on the results of I2 test, the prevalence of sleep disturbances in nurses and physicians is I2: 97.4% and I2: 97.3% respectively. After following the systematic review processes, 7 cross-sectional studies were selected for meta-analysis. Six studies with the sample size of 3745 nurses were examined in and the prevalence of sleep disturbances was approximated to be 34.8% (95% CI: 24.8-46.4%). The prevalence of sleep disturbances in physicians was also measured in 5 studies with the sample size of 2123 physicians. According to the results, the prevalence of sleep disturbances in physicians caring for the COVID-19 patients was reported to be 41.6% (95% CI: 27.7-57%).

    CONCLUSION: Healthcare workers, as the front line of the fight against COVID-19, are more vulnerable to the harmful effects of this disease than other groups in society. Increasing workplace stress increases sleep disturbances in the medical staff, especially nurses and physicians. In other words, increased stress due to the exposure to COVID-19 increases the prevalence of sleep disturbances in nurses and physicians. Therefore, it is important for health policymakers to provide solutions and interventions to reduce the workplace stress and pressures on medical staff.

  7. Salari N, Mohammadi M, Vaisi-Raygani A, Abdi A, Shohaimi S, Khaledipaveh B, et al.
    BMC Geriatr, 2020 02 03;20(1):39.
    PMID: 32013895 DOI: 10.1186/s12877-020-1444-0
    BACKGROUND: Depression is one of the most common psychiatric disorders in the older adult and one of the most common risk factors for suicide in the older adult. Studies show different and inconsistent prevalence rates in Iran. This study aims to determine the prevalence of severe depression in Iranian older adult through a meta-analysis approach.

    METHODS: The present meta-analysis was conducted between January 2000-August 2019. Articles related to the subject matter were obtained by searching Scopus, Sciencedirect, SID, magiran, Barakat Knowledge Network System, Medline (PubMed), and Google Scholar databases. The heterogeneity of the studies was evaluated using I2 index and the data were analyzed in Comprehensive Meta-Analysis software.

    RESULTS: In a study of 3948 individuals aged 50-90 years, the overall prevalence of severe depression in Iranian older adult was 8.2% (95% CI, 4.14-6.3%) based on meta-analysis. Also, in order to investigate the effects of potential factors (sample size and year of study) on the heterogeneity of severe depression in Iranian older adult, meta-regression was used. It was reported that the prevalence of severe depression in Iranian older adult decreased with increasing sample size and increasing years of the study, which is significantly different (P 

  8. Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I
    PLoS One, 2014;9(11):e112987.
    PMID: 25419659 DOI: 10.1371/journal.pone.0112987
    Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.
  9. Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I
    Theor Biol Med Model, 2013 Sep 18;10:57.
    PMID: 24044669 DOI: 10.1186/1742-4682-10-57
    OBJECTIVE: The classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.

    METHODS: A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.

    RESULTS: The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.

    CONCLUSION: The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.

  10. Kazeminia M, Abdi A, Shohaimi S, Jalali R, Vaisi-Raygani A, Salari N, et al.
    Head Face Med, 2020 Oct 06;16(1):22.
    PMID: 33023617 DOI: 10.1186/s13005-020-00237-z
    BACKGROUND: Early childhood caries (ECC) is a type of dental caries in the teeth of infants and children that is represented as one of the most prevalent dental problems in this period. Various studies have reported different types of prevalence of dental caries in primary and permanent teeth in children worldwide. However, there has been no comprehensive study to summarize the results of these studies in general, so this study aimed to determine the prevalence of dental caries in primary and permanent teeth in children in different continents of the world during a systematic review and meta-analysis.

    METHODS: In this review study, articles were extracted by searching in the national and international databases of SID, MagIran, IranMedex, IranDoc, Cochrane, Embase, ScienceDirect, Scopus, PubMed, and Web of Science (ISI) between 1995 and December 2019. Random effects model was used for analysis and heterogeneity of studies was evaluated by using the I2 index. Data were analyzed by using the Comprehensive Meta-Analysis (Version 2) software.

    FINDINGS: In this study, a total of 164 articles (81 articles on the prevalence of dental caries in primary teeth and 83 articles on the prevalence of dental caries in permanent teeth) were entered the meta-analysis. The prevalence of dental caries in primary teeth in children in the world with a sample size of 80,405 was 46.2% (95% CI: 41.6-50.8%), and the prevalence of dental caries in permanent teeth in children in the world with a sample size of 1,454,871 was 53.8% (95% CI: 50-57.5%). Regarding the heterogeneity on the basis of meta-regression analysis, there was a significant difference in the prevalence of dental caries in primary and permanent teeth in children in different continents of the world. With increasing the sample size and the year of study, dental caries in primary teeth increased and in permanent teeth decreased.

    CONCLUSION: The results of this study showed that the prevalence of primary and permanent dental caries in children in the world was found to be high. Therefore, appropriate strategies should be implemented to improve the aforementioned situation and to troubleshoot and monitor at all levels by providing feedback to hospitals.

  11. Salari N, Kazeminia M, Shohaimi S, Nankali AA, Mohammadi M
    Reprod Biol Endocrinol, 2020 Nov 09;18(1):108.
    PMID: 33168010 DOI: 10.1186/s12958-020-00666-0
    BACKGROUND: Previous caesarean scar pregnancy is one type of ectopic pregnancy in myometrium and fibrous tissue of previous caesarean scar. One of the therapeutic methods of this type of ectopic pregnancy is treatment with methotrexate. Given various findings on the treatment of caesarean scar pregnancy with methotrexate and lack of global report in this regard, we aimed to achieve a global report on the treatment of CSP with methotrexate through related literature review and analysis of the results of the studies, to enable more precise planning to reduce complications of CSP.

    METHOD: This review study extracted information through searching national and international databases of SID،, Embase, ScienceDirect, ، Scopus, ، PubMed, Web of Science (ISI) between 2003 and January 2020. To perform the meta-analysis, random-effects model and heterogeneity of the studies with I2 index were investigated. Data were sanalysed using Comprehensive Meta-Analysis version 2.

    RESULTS: In total, 26 articles with a sample size of 600 individuals were enrolled in the meta-analysis. According to the results of the study, the mean level of β-hCG was 28,744.98 ± 4425.1 mIU/ml before the intervention and was 23,836.78 ± 4533.1 mIU/ml after the intervention. The mean intraoperative blood loss (ml) was 4.8 ± 3.76 ml, mean hospital stay (days) was 11.7 ± 1.2 days, mean time for serum-hCG normalization (days) was 41.6 ± 3.2 days, success was 90.7% (95% CI: 86.7-93.5%), and complication was 9% (95% CI: 6.3-12.8%).

    CONCLUSION: The results of the current study show methotrexate significantly reduces β-hCG levels and can be effective in treating caesarean scar pregnancy and its complications.

  12. Salari N, Kazeminia M, Shohaimi S, Mohammadi M
    Clin Rheumatol, 2021 Jun 22.
    PMID: 34159490 DOI: 10.1007/s10067-021-05829-x
    BACKGROUND: Rheumatoid arthritis is a chronic inflammatory and systemic autoimmune disease associated with synovial fluid inflammatory lesions and articular changes. The aim of the present study was to determine socioeconomic inequality in RA patients using a meta-analysis approach.

    METHODS: A systematic search of national and international databases of SID, MagIran, Google Scholar, Cochrane, Embase, ScienceDirect, Scopus, PubMed, and Web of Science (WoS) was conducted to find articles published from 1988 to March 2020. Random effects model was used for analysis and heterogeneity of studies was investigated using I2 index. Data analysis was then carried out using Comprehensive Meta-Analysis (Ver. 2).

    RESULTS: A total of 51 articles with a total sample size of 48,195 individuals were included in the meta-analysis in all the components. The results showed that 18.9% (95% CI: 4.9-13.25%) of patients were single patients, 70.6% (95% CI: 63.5-76.8%) were married, 31.6% (95% CI: 24.5-39.7%) had low economic status, 52.1% (95% CI: 5.8-44.53%) had moderate economic status, level of education was below diploma in 33% (95% CI: 27.1-39.5%) of cases, 36.2% (95% CI: 27.3-46.1%) were smokers, and 8.8% (95% CI: 2.8-24.1%) of patients were unemployed.

    CONCLUSION: The results of the present study indicate high socioeconomic inequality in RA patients in the main components of the study. Hence, to improve the aforementioned status and find causes and do the monitoring at all levels, appropriate solutions must be adopted by providing feedback to policy-makers.

    KEY POINTS: • The results showed that 18.9% (95% CI: 4.9-13.25%) of patients were single patients. • 70.6% (95% CI: 63.5-76.8%) were married and 31.6% (95% CI: 24.5-39.7%) had low economic status. • 52.1% (95% CI: 5.8-44.53%) had moderate economic status; 36.2% (95% CI: 27.3-46.1%) were smokers.

  13. Rasoulpoor S, Shohaimi S, Salari N, Vaisi-Raygani A, Rasoulpoor S, Shabani S, et al.
    J Diabetes Metab Disord, 2021 Jun;20(1):665-672.
    PMID: 34222084 DOI: 10.1007/s40200-021-00797-0
    Background: Fungal infections including Candida albicans is one of the most important health concerns among type 2 diabetic patients. Therefore, this study aimed to determine the prevalence of C. albicans skin infection in patients with type 2 diabetes in a systematic review and meta-analysis.

    Methods: In this review study, data were extracted from national and international databases of SID, MagIran, IranMedex, IranDoc, Google Scholar, Cochrane, Embase, ScienceDirect, Scopus, PubMed, and Web of Science (WoS) with no time limit until January 2021. The random effects model was used for doing analysis and the I2 index was used for assessing the heterogeneity of studies. Data were analyzed using Comprehensive Meta-Analysis (Version 2).

    Results: The prevalence of C. albicans skin infection in patients with type 2 diabetes was 11.4% (95% CI: 8.9%-14.4%) in 13 reviewed articles with a sample size of 1348. Regarding the heterogeneity based on meta-regression, there was a significant difference between the effect of sample size (P 

  14. Jafari H, Shohaimi S, Salari N, Kiaei AA, Najafi F, Khazaei S, et al.
    PLoS One, 2022;17(1):e0262701.
    PMID: 35051240 DOI: 10.1371/journal.pone.0262701
    Anthropometry is a Greek word that consists of the two words "Anthropo" meaning human species and "metery" meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.
  15. Salari N, Karami MM, Bokaee S, Chaleshgar M, Shohaimi S, Akbari H, et al.
    Eur J Med Res, 2022 Feb 05;27(1):20.
    PMID: 35123565 DOI: 10.1186/s40001-022-00644-9
    BACKGROUND: Urinary tract infection is the most common infection in type 2 diabetic patients. Various studies have reported different outbreaks of urinary tract infections in type 2 diabetic patients. Therefore, the present study aimed to determine the prevalence of urinary tract infections in type 2 diabetic patients during a systematic review and meta-analysis in order to develop interventions to reduce the incidence of urinary tract infections in type 2 diabetic patients.

    METHODS: In this study, systematic review and meta-analysis of study data related to the prevalence of urinary tract infection in type 2 diabetic patients were conducted using keywords including type 2 diabetes, urinary tract infection, diabetes, prevalence, meta-analysis and their English equivalents in SID, MagIran, IranMedex, IranDoc, Google Scholar, Cochrane, Embase, Science Direct, Scopus, PubMed and Web of Science (WoS) databases from 1993 to 2020. In order to perform the analysis of qualified studies, the model of random-effects was used, and the inconsistency of studies with the I2 index was investigated. Data analysis was performed with Comprehensive Meta-Analysis (Version 2).

    RESULTS: Based on a total of 15 studies with a sample size of 827,948 in meta-analysis, the overall prevalence of urinary tract infection in patients with type 2 diabetes was 11.5% (95% confidence interval: 7.8-16.7%). The prevalence of urinary tract infections in diabetic Iranian patients increased with increasing number of years of research, (p 

  16. Salari N, Darvishi N, Hemmati M, Shohaimi S, Ghyasi Y, Hossaini F, et al.
    Arch Virol, 2022 Feb 14.
    PMID: 35165781 DOI: 10.1007/s00705-022-05382-1
    Hepatitis C virus (HCV), one of the most significant causes of liver inflammation, has a high annual mortality rate. The unfavorable hygiene conditions and inadequate health monitoring in many prisons increase the risk of blood-borne infections such as hepatitis C. The growing incidence of this disease among prisoners results in overspill transmission to the general population from undiagnosed prisoners that have been released. Therefore, the aim of this study was to investigate the prevalence of hepatitis C among the world's prison population. A systematic review and meta-analysis of studies on the prevalence of hepatitis C was carried out using the keywords "Prevalence", "Hepatitis C", and "Prisoner" in the Iranian and international databases SID, MagIran, Iran Doc, Science Direct, Scopus, PubMed, and Web of Science (WoS) from January 1990 to September 2020. After transferring the articles to the information management software EndNote and eliminating duplicate studies, the remaining studies were reviewed based on inclusion and exclusion criteria, three stages of primary and secondary evaluation, and qualitative evaluation. Comprehensive meta-analysis software and Begg and Mazumdar and I2 tests were used for data analysis and assessment of dissemination bias, and heterogeneity, respectively. Out of 93 studies (22 from Asia, 26 from Europe, seven from Africa, 29 from America, and nine from Australia) with a total sample size of 145,823 subjects, the prevalence of hepatitis C in prisoners worldwide was estimated to be 17.7% (95% confidence interval, 15-20.7%). The highest prevalence of hepatitis C on the continents included in this study was reported in prisoners incarcerated in Australia and Oceania, with 28.4% (95% CI: 21.6-36.4) in nine studies, and Europe, with 25.1% (95% CI: 19.4-31.8) in 26 studies. All studies used an ELISA test for the detection of HCV antibodies. The results showed a prevalence of HCV of 17.7% in prisoners worldwide, ranging between 10 and 30% over five continents (Asia, Europe, America, Africa, and Australia and Oceania). The highest prevalence was reported in Australia and Oceania (28.4%), indicating the need to pay more attention to this issue on the continent. It is necessary to reduce the incidence of the disease in prisons by appropriate policy-making and the development of accurate and practical programs, including the distribution of free syringes and examination, testing, and screening of prisoners.
  17. Salari N, Fatahi B, Valipour E, Kazeminia M, Fatahian R, Kiaei A, et al.
    J Orthop Surg Res, 2022 Feb 15;17(1):96.
    PMID: 35168641 DOI: 10.1186/s13018-022-02996-8
    BACKGROUND: A variety of mutations in the largest human gene, dystrophin, cause a spectrum from mild to severe dystrophin-associated muscular dystrophies. Duchenne (DMD) and Becker (BMD) muscular dystrophies are located at the severe end of the spectrum that primarily affects skeletal muscle. Progressive muscle weakness in these purely genetic disorders encourages families with a positive history for genetic counseling to prevent a recurrence, which requires an accurate prevalence of the disorder. Here, we provide a systematic review and meta-analysis to determine the prevalence of DMD and BMD worldwide.

    METHOD: The current systematic review and meta-analysis was carried out using Cochrane seven-step procedure. After determining the research question and inclusion and exclusion criteria, the MagIran, SID, ScienceDirect, WoS, ProQuest, Medline (PubMed), Embase, Cochrane, Scopus, and Google Scholar databases were searched to find relevant studies using defined keywords and all possible keyword combinations using the AND and OR, with no time limit until 2021. The heterogeneity of studies was calculated using the I2 test, and the publication bias was investigated using the Begg and Mazumdar rank correlation test. Statistical analysis of data was performed using Comprehensive Meta-Analysis software (version 2).

    RESULTS: A total of 25 articles involving 901,598,055 people were included. The global prevalence of muscular dystrophy was estimated at 3.6 per 100,000 people (95 CI 2.8-4.5 per 100,000 people), the largest prevalence in the Americans at 5.1 per 100,000 people (95 CI 3.4-7.8 per 100,000 people). According to the subgroup analysis, the prevalence of DMD and BMD was estimated at 4.8 per 100,000 people (95 CI 3.6-6.3 per 100,000 people) and 1.6 per 100,000 people (95 CI 1.1-2.4 per 100,000 people), respectively.

    CONCLUSION: Knowing the precise prevalence of a genetic disorder helps to more accurately predict the likelihood of preventing its occurrence in families. The global prevalence of DMD and BMD was very high, indicating the urgent need for more attention to prenatal screening and genetic counseling for families with a positive history.

  18. Salari N, Darvishi N, Bartina Y, Larti M, Kiaei A, Hemmati M, et al.
    J Orthop Surg Res, 2021 Nov 13;16(1):669.
    PMID: 34774085 DOI: 10.1186/s13018-021-02821-8
    BACKGROUND: Osteoporosis is one of the most common bone system diseases that is associated with an increased risk of bone fractures and causes many complications for patients. With age, the prevalence of this disease increases so that it has become a serious problem among the elders. In this study, the prevalence of osteoporosis among elders around the world is examined to gain an understanding of its prevalence pattern.

    METHODS: In this systematic review and meta-analysis, articles that have focused on prevalence of osteoporosis in the world's elders were searched with these key words, such as Prevalence, Osteoporosis, Elders, Older adult in the Science Direct, Embase, Scopus, PubMed, Web of Science (WoS) databases and Google Scholar search engine, and extracted without time limit until March 2020 and transferred to information management software (EndNote). Then, duplicate studies were eliminated and the remaining studies were evaluated in terms of screening, competence and qualitative evaluation based on inclusion and exclusion criteria. Data analysis was performed with Comprehensive Meta-Analysis software (Version 2) and Begg and Mazumdar test was used to check the publication bias and I2 test was used to check the heterogeneity.

    RESULTS: In a review of 40 studies (31 studies related to Asia, 5 studies related to Europe and 4 studies related to America) with a total sample size of 79,127 people, the prevalence of osteoporosis in the elders of the world; 21.7% (95% confidence interval: 18.8-25%) and the overall prevalence of osteoporosis in older men and women in the world, 35.3% (95% confidence interval: 27.9-43.4%), 12.5% (95% confidence interval: 9.3-16.7%) was reported. Also, the highest prevalence of osteoporosis in the elders was reported in Asia with; 24.3% (95% confidence interval: 20.9-28.1%).

    CONCLUSION: The results of the present study showed that the prevalence of osteoporosis in the elders and especially elders' women is very high. Osteoporosis was once thought to be an inseparable part of elders' lives. Nowadays, Osteoporosis can be prevented due to significant scientific advances in its causes, diagnosis, and treatment. Regarding the growing number of elderly people in the world, it is necessary for health policy-makers to think of measures to prevent and treat osteoporosis among the elders.

  19. Salari N, Hasheminezhad R, Abdolmaleki A, Kiaei A, Razazian N, Shohaimi S, et al.
    Neurol Sci, 2023 Jan;44(1):59-66.
    PMID: 36114398 DOI: 10.1007/s10072-022-06406-z
    BACKGROUND: Sexual function is often impaired following neurological disorders such as multiple sclerosis (MS). Young women with MS encourage disruptions in sexual function, sexual behaviors, and family formation as common global problems. Thus, the aim of the present systematic review and meta-analysis study was to investigate the global prevalence of female sexual dysfunction (FSD) worldwide.

    METHODS: Various databases (PubMed, Scopus, Web of Science, Embase, and ScienceDirect) along with Google Scholar search engine were hired for systematic searching in the field of the prevalence of FSD (by July 2022). The heterogeneity of the studies was assessed using I2 index, and random effects model was used to perform the analysis (CMA software, v.2).

    RESULTS: Following assessment of 14 included studies with the sample size of 2115 women, a total prevalence of sexual dysfunction (SD) in women with MS was reported 62.5% (95% CI 53.9-70.5). Meta-regression assessment also showed that FSD accelerates following increasing the sample size and the year of the studies.

    CONCLUSION: The total prevalence of SD in women with MS was found considerably high (62.5%) in the world, which needs more serious attention by health policymakers. Correct implementation of health policies can potentially increase the society's awareness and successful treatment of SD in MS patients.

  20. Salari N, Hasheminezhad R, Abdolmaleki A, Kiaei A, Shohaimi S, Akbari H, et al.
    Arch Womens Ment Health, 2022 Dec;25(6):1021-1027.
    PMID: 36445469 DOI: 10.1007/s00737-022-01281-1
    The increased number of female smokers is considered a global health challenge in recent years. One of the detrimental effects of smoking is sexual hormone fluctuation causing female sexual dysfunction (FSD). This systematic review and meta-analysis aimed to investigate the effects of smoking leading to FSD. Electronic databases (PubMed, Scopus, Web of Science, Embase, Science Direct, and Google Scholar) were hired for systematic searching. Until June 2022, whole qualified studies reporting the consequences of smoking on FSD were gathered for data analysis based on the random effects model (CMA software, v.2). Study heterogeneity and publication bias were also assessed using I2 index and Egger test, respectively. Ten eligible studies with a sample size of 15,334 female smokers (18-79 years) were selected. Following data analysis, the odds ratio representing the effects of smoking on FSD was found 1.48 (95%CI: 1.2-1.83), indicating that female smokers were 48% more susceptible to FSD than non-smokers. Also, the publication bias was reported as non-significant (p = 0.178). Since smoking is an increasingly common phenomenon in females and women smokers are 48% more susceptible to the FSD, preparation of necessary health measures by the health policymakers to reduce the number of female smokers and subsequent health services seems necessary.
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