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Ghaemmaghami Z, Firoozbakhsh P, Gholami D, Khodabandelu S, Baay M, Alemzadeh-Ansari MJ, Mohebbi B, Hosseini Z, Boudagh S, Pouraliakbar H, Pasebani Y, Rafati A, Khalilpour E, Khalili Y, Arabian M, Maleki M, Bakhshandeh H, Sadeghipour P. Increased prevalence of thyroid dysfunction in Tehran - HAMRAH study. BMC Endocr Disord 2023; 23:270. [PMID: 38053115 DOI: 10.1186/s12902-023-01524-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND The aim of the current study is to assess the prevalence of different categories of thyroid dysfunction and their associated risk factors among the modern urban population of Tehran, the capital of Iran. METHODS The present investigation is a sub-study of the HAMRAH study, a population-based prospective study designed to assess the prevalence of traditional cardiovascular risk factors and their changes through a 10-year follow-up. 2228 (61% female) adults aged between 30 and 75 years old and with no overt cardiovascular diseases were selected through a multistage cluster randomized sampling. Blood levels of thyroid-stimulating hormone (TSH), thyroxin (T4), and triiodothyronine (T3) were measured with the aim of assessing the prevalence of abnormal thyroid function status among the modern urban Iranian population, and in order to report the total prevalence of participants with clinical hypo- or hyperthyroidism, the number of individuals taking thyroid-related drugs were added to the ones with overt thyroid dysfunction. A subgroup analysis was also performed to determine the associated risk factors of thyroid dysfunction. RESULTS The prevalence of thyroid dysfunction among the total population was 7% (95%CI: 5.9 - 8%) and 0.4% (95% CI: 0.1 - 0.6%) for subclinical and overt hypothyroidism, and 1.6% (95% CI: 1 - 2%) and 0.2% (95% CI: 0 - 0.3%) for subclinical and overt hyperthyroidism, respectively. Clinical thyroid dysfunction was detected in 10.3% of the study population (9.4% had clinical hypo- and 0.9% had clinical hyperthyroidism). In the subgroup analysis, thyroid dysfunction was significantly more prevalent among the female participants (P-value = 0.029). CONCLUSIONS In the current study, the prevalence of different categories of abnormal thyroid status, and also the rate of clinical hypo- and hyperthyroidism was assessed using the data collected from the first phase of the HAMRAH Study. In this study, we detected a higher prevalence of clinical and subclinical hypothyroidism among the Iranian population compared to the previous studies.
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Affiliation(s)
- Zahra Ghaemmaghami
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Parisa Firoozbakhsh
- Cardio-Oncology Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Delara Gholami
- Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Sajad Khodabandelu
- Department of Biostatistics and Epidemiology, Student Research Committee, School of Health, Mazandaran University of Medical Sciences, Sari, Iran
| | - Mohammadreza Baay
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Vali-Asr Ave, 1995614331, Tehran, Iran
| | - Mohammad Javad Alemzadeh-Ansari
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Vali-Asr Ave, 1995614331, Tehran, Iran
| | - Bahram Mohebbi
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Vali-Asr Ave, 1995614331, Tehran, Iran
| | - Zahra Hosseini
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Vali-Asr Ave, 1995614331, Tehran, Iran
| | - Shabnam Boudagh
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Tehran, Iran
| | - Hamidreza Pouraliakbar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Yeganeh Pasebani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Ali Rafati
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Ehsan Khalilpour
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Vali-Asr Ave, 1995614331, Tehran, Iran
| | - Yasaman Khalili
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Maedeh Arabian
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Majid Maleki
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran
| | - Hooman Bakhshandeh
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Vali-Asr Ave, Tehran, 1995614331, Iran.
| | - Parham Sadeghipour
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, University of Medical Sciences, Vali-Asr Ave, 1995614331, Tehran, Iran.
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Bakhshandeh N, Mohammadi M, Mohammadi P, Nazari E, Damchi M, Khodabandelu S, Mokhtari H. Increased expression of androgen receptor and PSA genes in LNCaP (prostate cancer) cell line due to high concentrations of EGCG, an active ingredient in green tea. Horm Mol Biol Clin Investig 2022:hmbci-2022-0054. [PMID: 36578191 DOI: 10.1515/hmbci-2022-0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 12/11/2022] [Indexed: 12/30/2022]
Abstract
OBJECTIVES Androgen receptor (AR) play a key role in the onset and progression of prostate cancer. Epigallocatechin-3-gallate (EGCG) is a polyphenolic compound and the active ingredient in green tea, which is involved in modulating gene expression through epigenetic alterations. Previous studies have shown that EGCG at low concentrations reduces the expression of AR and prostate-specific antigen (PSA) in the LNCaP cell line of prostate cancer. In this study, the effect of higher EGCG concentrations on AR and PSA expression in LNCaP prostate cancer cell line was investigated. METHODS In this study, LNCaP prostate cancer cell line was used and after MTT test, concentrations of 40, 60 and 80 μg/mL EGCG were used for treatment. Then, the expression of AR and PSA genes was evaluated by RT-PCR. AR protein expression was also assessed by Western blotting. RESULTS The present study showed that treatment of LNCaPs cells by EGCG reduces cell proliferation. The IC50 value was 42.7 μg/mL under experimental conditions. It was also observed that EGCG at concentrations of 40 and 80 μg/mL increased the expression of AR and PSA (p<0.05). CONCLUSIONS The present study showed that the effect of EGCG on AR expression was different at different concentrations, so that unlike previous studies, higher concentrations of EGCG (80 and 40 μg/mL) increased AR and PSA expression. It seems that due to the toxic effects of EGCG in high concentrations on cancer cells and the possibility of its effect on normal cells, more caution should be exercised in its use.
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Affiliation(s)
- Nadereh Bakhshandeh
- Department of Medical Biochemistry and Genetics, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Maryam Mohammadi
- Health System Research, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Parisa Mohammadi
- Department of Clinical Biochemistry, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Elahe Nazari
- Department of Biology, Islamic Azad University, Gorgan Branch, Gorgan, Iran
| | - Mehdi Damchi
- Department of Clinical Biochemistry, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Sajad Khodabandelu
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Hossein Mokhtari
- Amol Faculty of Paramedicine, Mazandaran University of Medical Sciences, Sari, Iran
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Khodabandelu S, Ghaemian N, Khafri S, Ezoji M, Khaleghi S. Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data. J Res Health Sci 2022; 22:e00555. [PMID: 36511373 PMCID: PMC10422153 DOI: 10.34172/jrhs.2022.90] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/23/2022] [Accepted: 08/02/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND This study aims to show the impact of imbalanced data and the typical evaluation methods in developing and misleading assessments of machine learning-based models for preoperative thyroid nodules screening. STUDY DESIGN A retrospective study. METHODS The ultrasonography features for 431 thyroid nodules cases were extracted from medical records of 313 patients in Babol, Iran. Since thyroid nodules are commonly benign, the relevant data are usually unbalanced in classes. It can lead to the bias of learning models toward the majority class. To solve it, a hybrid resampling method called the Smote-was used to creating balance data. Following that, the support vector classification (SVC) algorithm was trained by balance and unbalanced datasets as Models 2 and 3, respectively, in Python language programming. Their performance was then compared with the logistic regression model as Model 1 that fitted traditionally. RESULTS The prevalence of malignant nodules was obtained at 14% (n = 61). In addition, 87% of the patients in this study were women. However, there was no difference in the prevalence of malignancy for gender. Furthermore, the accuracy, area under the curve, and geometric mean values were estimated at 92.1%, 93.2%, and 76.8% for Model 1, 91.3%, 93%, and 77.6% for Model 2, and finally, 91%, 92.6% and 84.2% for Model 3, respectively. Similarly, the results identified Micro calcification, Taller than wide shape, as well as lack of ISO and hyperechogenicity features as the most effective malignant variables. CONCLUSION Paying attention to data challenges, such as data imbalances, and using proper criteria measures can improve the performance of machine learning models for preoperative thyroid nodules screening.
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Affiliation(s)
- Sajad Khodabandelu
- Student Research Committee, School of Medicine, Faculty of Health, Babol University of Medical Science, Babol, Iran
| | - Naser Ghaemian
- Department of Radiology, Babol University of Medical Sciences, Babol, Iran
| | - Soraya Khafri
- Research Center for Social Determinants of Health, Health Research Institute, Department of Biostatistics and Epidemiology, Faculty of Health, Babol University of Medical Sciences, Babol, Iran
| | - Mehdi Ezoji
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Sara Khaleghi
- Student Research Committee, School of Medicine, Faculty of Health, Babol University of Medical Science, Babol, Iran
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Khodabandelu S, Basirat Z, Khaleghi S, Khafri S, Montazery Kordy H, Golsorkhtabaramiri M. Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them. BMC Med Inform Decis Mak 2022; 22:228. [PMID: 36050710 PMCID: PMC9434923 DOI: 10.1186/s12911-022-01974-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/24/2022] [Indexed: 12/03/2022] Open
Abstract
Background This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques.
Methods The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development. Results In this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success. Conclusion The results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01974-8.
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Affiliation(s)
- Sajad Khodabandelu
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Zahra Basirat
- Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Sara Khaleghi
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Soraya Khafri
- Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
| | - Hussain Montazery Kordy
- Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Masoumeh Golsorkhtabaramiri
- Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
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Khaleghi S, Nikbakht HA, Khodabandelu S, Khafri S. Incidence and investigation of Covid-19 trend in Babol, northern Iran: A Joinpoint regression analysis. Caspian J Intern Med 2022; 13:236-243. [PMID: 35872691 PMCID: PMC9272972 DOI: 10.22088/cjim.13.0.236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND In December 2019, China released the first report of the coronavirus (COVID-19). On March 11, 2020 the World Health Organization (WHO) characterized the COVID-19 as "pandemic". The rapid occurrence of positive cases motivated this study to examine the trend of incidence cases. METHODS We used the data from the database of the Deputy of Health of Babol City and in Iran, the country report of definite cases of the disease that was reported to the World Health Organization had been used. This study was a cross-sectional study and the data from period of 56 weeks (from February 24, 2020 to March 20, 2021) were gathered. Descriptive analysis with SPSS20 and data classification with EXCEL2016 and Joinpoint regression with Joinpoint trend analysis software 4.9.0.0 identify the significant changes in the temporal trends of the outbreak. RESULTS In this study, 11341 patients with a mean age of 53.56 years, of whom 5865(51.5%) were males, were studied. Three waves of Covid19 were created. AWPC (average weekly percentage change) incidence rate with a slope of 2.7 was estimated for Babol and 6.2 for Iran. The incidence was higher in men in the first wave of 1887(55.6%) and so is the third 2373(50.1%), the average age in the third wave (50.92) was lower than the other waves as well. CONCLUSION The incidence of coronavirus in men was higher in three waves and also the incidence was increasing in younger age groups. Also, due to the observance of health protocols and quarantine during the peak in Iran and Babol, we witnessed a decrease in incidence.
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Affiliation(s)
- Sara Khaleghi
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Hossein-Ali Nikbakht
- Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Sajad Khodabandelu
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Soraya Khafri
- Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran,Correspondence: Soraya Khafri, Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, 4719173716, Iran. E-mail: , Tel: 0098 1132274880, Fax: 0098 1132274880
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