Increased prevalence of thyroid dysfunction in Tehran - HAMRAH study.

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.

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them.

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.

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.

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.

Incidence and investigation of Covid-19 trend in Babol, northern Iran: A Joinpoint regression analysis.

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.

Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data.

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.