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A population-based survey on interarch malocclusion and background determinants
No full textBACKGROUND: Genetics, environment, and ethnic factors are major contributors to the prevalence and variations of malocclusion. AIM: The aim of study was to determine the prevalence of interarch problems in school-aged children, 9 - 11 years, living in Tehran; and to describe the role of ethnicity, education, and economic status on them. METHODS: The present data were part of the Comprehensive Evaluation of Skeleto-Dental Anomalies (CESDA) study conducted in 2015 among children living in Tehran, Iran. Cluster random sampling was applied among 19 districts of Tehran. A total of 38 schools were selected, and out of 1585 participants, the data of 1429 children were collected (response rate = 90). The Chi-square test and binary logistic regression analysis were used for statistical analyses. RESULTS: Of all participants, 758 were boys and 671 were girls. The mean age was 121 +/- 8 months. The most significant background determinants associated with molar relationship were ethnicity and place of residence. Class I right molar relationship was seen in 57.9 of the children and was generally more frequent in boys. Normal overjet was observed in 47.1; 41.5 had an increased overjet, 16.2 had an anteroposterior cross-bite, and 11.8 had a lateral cross-bite. Midline discrepancy was seen among 61.1 of the children. Ideal anteroposterior, vertical, and horizontal relationship were observed in 31, 53, and 34 of the children, respectively. Only 10 of the children aged 9 - 11 years old had an ideal interarch relationship. Gender and place of residence had persistently significant association with having an ideal anteroposterior, vertical, horizontal, and interarch relationships (P < 0.002) in all four binary logistic regression models. CONCLUSIONS: The majority of the children aged 9 - 11 years old have at least one interarch problem, although it is commonly preventable. RELEVANCE FOR PATIENTS: Early detection of children's orthodontic problems may help with effective prevention of further advanced anomalies
Assessment of the clinical outcomes of diabetes exposure during pregnancy on mothers and their neonates. A cohort study
No full textBackground. Gestational diabetes is a metabolic disorder that occurs in more than 8 of all pregnancies. Hence, this study aims to investigate the relationship between gestational diabetes and the clinical outcomes of mothers and their infants in Ilam city. Methods. This study was conducted as a retrospective cohort. All diabetic pregnant mothers referring to obstetrics, gynecology clinics, and comprehensive health service centers were randomly selected. The clinical outcomes were analyzed using SPSS20. Results. In total, 332 pregnant women, including 166 diabetics and 166 non-diabetics with a mean age of 32.3 years, participated. Of the diabetic mothers, 31 people had overt diabetes before pregnancy. The results of this study revealed that the relative risk of neonatal jaundice in diabetic mothers was about seven times higher than that of nondiabetic mothers (P=0.001, RR=7). The relative risk of postpartum hemorrhage was about six times (P=0.00I, RR=5.9), blood hypertension was two times (P=0.0I3, RR=2.4) and the need for cesarean delivery was two times (P=0.009, RR=2.6), preeclampsia was 1.4 times (P= 0.011, RR= 1.4) neonatal infection was twice (P=0.002, RR=2.1), respiratory distress was one-third (P=0.012, RR=1.3), compared to nondiabetic mothers. Furthermore, diabetes and other clinical outcomes studied in mothers and infants were not significant. Conclusion. The results showed that pregnant mothers with diabetes are more likely to suffer from postpartum hemorrhage and blood pressure as well as toxemia during pregnancy. In addition, their neonates have a higher risk of neonatal icterus. Health care providers and pregnant mothers should consider these risks in prenatal care. Practical Implications. Since diabetes is a dangerous complication during pregnancy and can have detrimental effects on the mother and fetus, therefore, it is crucial to study the factors affecting its occurrence so that by recognizing the risk factors and consequences, an effective step can be taken to control this disorder. © 2023, Tabriz University of Medical Sciences. All rights reserved
Efficient sonocatalytic degradation of orange II dye and real textile wastewater using peroxymonosulfate activated with a novel heterogeneous TiO2–FeZn bimetallic nanocatalyst
No full textTiO2–FeZn nanocatalyst combined with sonolysis were used to activate peroxymonosulfate (PMS) as a highly efficient advanced oxidation process (US/TiO2–FeZn/PMS) for the decoloration of orange II dye (OII) and real textile wastewater. The characterization of the as-synthesized NPs was performed by SEM, FTIR, EDX and XRD analyses. Optimal experimental conditions of operational parameters were obtained: pH = 3, 15 mg/L initial OII concentration, 0.2 g/L PMS, 0.7 g/L nanocatalyst dosing, and 300 W ultrasonic power. The decolorization was observed to increase with increasing the dose of nanocatalyst and the ultrasonic power, and with decreasing pH (under acidic conditions). Under optimal experimental conditions, decolorization and COD removal of textile wastewater were 99.9 and 74.6, respectively, at 40 min. The TiO2–FeZn/PMS/US as a novel process exhibited a higher removal of OII (95) than TiO2 NPs/PMS/US process (54). The OII removal efficiency by the different processes decreased in the following order: TiO2–FeZn/US/PMS > TiO2–FeZn/PMS > TiO2–FeZn/US > TiO2 /US/PMS > US/PMS > TiO2–FeZn > PMS > US. The recyclability study revealed that the process could be reused up to three consecutive cycles. The current US/nanocatalyst/PMS system was concluded to be an efficient, reusable and stable nanocatalyst for the oxidation of textile dyes. © 2023, Iranian Chemical Society
High prevalence of mental disorders: a population-based cross-sectional study in the city of Ilam, Iran
No full textAimTo determine the age- and sex-standardized prevalence and risk factors of depression, anxiety, and stress symptoms in the city of Ilam. MethodIn this population-based cross-sectional study, 1,350 people were invited using a multi-stage stratified cluster random sampling method. Depression, anxiety, and stress symptoms were measured using the DASS-21 standard questionnaire. For data analysis, multiple ordinal logistic regression was used in Stata version 12 software. A significance level of 5 was considered. ResultsThe data of 1,431 people were analyzed. The age- and sex-standardized prevalence (95 CI) of severe depression, anxiety, and stress symptoms was 19.90 (17.64 to 22.16), 25.95 (23.48 to 28.43), and 15.75 (13.69 to 17.81), respectively. There was a positive association among depression symptoms with female sex (OR: 1.52; p < 0.003), Kurdish ethnicity (OR: 2.15; p < 0.004), low educational level (OR: 1.37; p < 0.031), job losing history (OR: 1.64; p < 0.001), mental disorders history (OR: 2.17; p < 0.001), hopelessness for the future (OR: 5.38; p < 0.001), and history of other diseases (OR: 1.67; p < 0.001). There was a positive association among anxiety symptoms with female sex (OR: 1.72; p < 0.001), job losing history (OR: 1.53; p < 0.003), mental disorders history (OR: 2.11; p < 0.001), hopelessness to future (OR: 3.33; p < 0.001) and history of other diseases (OR: 1.97; p < 0.001). Hopelessness for the future and a history of other diseases were the most effective variables for anxiety symptoms and stress symptoms. ConclusionA significant proportion of Ilam's urban population suffers from mental disorders. Increasing people's awareness, establishing counseling centers, and improving infrastructure should be considered by mental health policymakers who work in the province
Knowledge and Attitude of General Practitioners Regarding Pediatric Asthma and Allergy
No full textBackground: Asthma and allergy symptoms are usually intermittent in nature and may not manifest in the clinical examination to the extent that affect the patient; this presents a challenge to the general practitioners (GPs) or the patient in the diagnosis and follow-up treatment phases. Methods: The present cross-sectional study was conducted on 175 GPS. For collecting the data, the researchers-made online version of the Knowledge and Attitude questionnaire was uploaded in virtual networks, and all GPS were individually asked to fill the questionnaires. Data analysis was carried out using mean, standard deviation, ANOVA, independent t and regression statistical tests in SPSS ver. 16. Results: It was found that 134 (76.6) of the GPs had a partially true attitude and 41 (23.4) of them had an excellent attitude. Also, GPs had moderate and excellent knowledge in 157 (89.7) and 18 (10.3) cases, respectively. The mean +/- SD of the overall score of knowledge and attitude towards asthma was equal to 55.04 +/- 3.98. The overall score of the questionnaire and the score of all of its domains significantly correlated with age and years of work experience (p<0.05). Conclusion: Considering that most of the GPs in the present study had moderate knowledge and attitude towards asthma management, it is necessary to conduct educational interventions for this group of medical staff
Comparing machine learning algorithms to predict COVID-19 mortality using a dataset including chest computed tomography severity score data
No full textSince the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for mortality prediction of COVID-19 patients. The prognostic performances of the machine learning (ML)-based models for predicting clinical outcomes of COVID-19 patients had been mainly evaluated using demographics, risk factors, clinical manifestations, and laboratory results. There is a lack of information about the prognostic role of imaging manifestations in combination with demographics, clinical manifestations, and laboratory predictors. The purpose of the present study is to develop an efficient ML prognostic model based on a more comprehensive dataset including chest CT severity score (CT-SS). Fifty-five primary features in six main classes were retrospectively reviewed for 6854 suspected cases. The independence test of Chi-square was used to determine the most important features in the mortality prediction of COVID-19 patients. The most relevant predictors were used to train and test ML algorithms. The predictive models were developed using eight ML algorithms including the J48 decision tree (J48), support vector machine (SVM), multi-layer perceptron (MLP), k-nearest neighbourhood (k-NN), Naive Bayes (NB), logistic regression (LR), random forest (RF), and eXtreme gradient boosting (XGBoost). The performances of the predictive models were evaluated using accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) metrics. After applying the exclusion criteria, a total of 815 positive RT-PCR patients were the final sample size, where 54.85 of the patients were male and the mean age of the study population was 57.22 & PLUSMN; 16.76 years. The RF algorithm with an accuracy of 97.2, the sensitivity of 100, a precision of 94.8, specificity of 94.5, F1-score of 97.3, and AUC of 99.9 had the best performance. Other ML algorithms with AUC ranging from 81.2 to 93.9 had also good prediction performances in predicting COVID-19 mortality. Results showed that timely and accurate risk stratification of COVID-19 patients could be performed using ML-based predictive models fed by routine data. The proposed algorithm with the more comprehensive dataset including CT-SS could efficiently predict the mortality of COVID-19 patients. This could lead to promptly targeting high-risk patients on admission, the optimal use of hospital resources, and an increased probability of survival of patients
Comparison of machine learning algorithms to predict intentional and unintentional poisoning risk factors
No full textIntroduction: A major share of poisoning cases are perpetrated intentionally, but this varies depending on different geographical regions, age spectrums, and gender distribution. The present study was conducted to determine the most important factors affecting intentional and unintentional poisonings using machine learning algorithms.Materials and methods: The current cross-sectional study was conducted on 658 people hospitalized due to poisoning. The enrollment and follow-up of patients were carried out during 2020-2021. The data obtained from patients' files and during follow-up were recorded by a physician and entered into SPSS software by the registration expert. Different machine learning algorithms were used to analyze the data. Fit models of the training data were assessed by determining accuracy, sensitivity, specificity, F-measure, and the area under the rock curve (AUC). Finally, after analyzing the models, the data of the Gradient boosted trees (GBT) model were finalized.Results: The GBT model rendered the highest accuracy (91.5 & PLUSMN; 3.4) among other models tested. Also, the GBT model had significantly higher sensitivity (94.7 & PLUSMN; 1.7) and specificity (93.2 & PLUSMN; 4.1) compared to other models (P < 0.001). The most prominent predictors based on the GBT model were the route of poison entry (weight = 0.583), place of residence (weight = 0.137), history of psychiatric diseases (weight = 0.087), and age (weight = 0.085).Conclusion: The present study suggests the GBT model as a reliable predictor model for identifying the factors affecting intentional and unintentional poisoning. According to our results, the determinants of intentional poisoning included the route of poison entry into the body, place of residence, and the heart rate. The most important predictors of unintentional poisoning were age, exposure to benzodiazepine, creatinine levels, and occupation
The effect of metformin administration on cancer-specific survival, overall survival, progression-free survival, and disease progression in renal cell carcinoma patients; a systematic review and meta-analysis
No full textIntroduction: The increase in the incidence of renal cell carcinoma (RCC) has been reported worldwide. The anti-cancer impacts of metformin on the various types of cancer have been observed in clinical studies. Therefore, this study aims to survey the effect of metformin use on RCC patients using systematic review and meta-analysis methods. Materials and Methods: In this research, Cochrane, Web of Science, PubMed, Scopus databases, and Google Scholar web browser were searched using standard keywords. Data were analyzed with STATA 14 software. The significance level of tests P < 0.05 was considered. Results: The improvement in the progression-free survival (PFS) (HR: 0.72 (95 CI: 0.54, 0.94), P= 0.169, I2= 37.8) and cancer-specific survival (CSS) (HR: 0.36 (95 CI: 0.18, 0.75), P= 0.339, I2 = 7.5) was observed in eight studies with 10404 patients affected by RCC. However, no significant statistical effect was observed on the improvement in the disease progression (OR: 1.10 (95 CI: 0.85, 1.42), P= 0.326, I2 = 0) and cancer overall survival (OS) (HR: 0.72 (95 CI: 0.51, 1.01), P= 0.153, I2= 43.1). Conclusion: This study showed metformin administration improved CSS and PFS in RCC patients. More studies are warranted on the effect of metformin on the improvement in disease progression and OS of cancer. Registration: This study has been compiled based on the PRISMA checklist, and its protocol was registered on the PROSPERO website (ID= CRD42022369108; https://www.crd.york. ac.uk/prospero/displayrecord.php?ID=CRD42022369108)
Effect of Peganum harmala Extract on Biofilm and Involved Gene Expression in Biofilm Production of Candida albicans
No full textBackground: Since common drug therapies cannot eradicate Candida biofilm, extensive studies are required to develop more ef-fective antifungal compounds and identify their mechanism of action against Candida biofilm. Peganum harmala L. is a traditional medicinal plant, the seeds of which have been used to treat various diseases. Objectives: This study aimed to investigate the anti-biofilm mechanisms of P. harmala extract (PHE) and the expression of CAT1, EFG1, and BCR1 genes involved in oxidative stress response and biofilm formation in Candida albicans. Methods: Anti-biofilm activity of PHE was evaluated by crystal violet assay to determine biofilm formation on 33 C. albicans isolates. Finally, a real-time polymerase chain reaction was performed to analyze the effect of PHE on the expression of CAT1, EFG1, and BCR1 genes in C. albicans. Results: This study determined the minimum biofilm eradication concentration (MBEC) of 15 isolates in concentrations between 0.49-3.9 mu g/mL of P. harmala extract. Statistical analysis showed that the exposure of C. albicans biofilm to PHE significantly reduced the expression of CAT1 mRNA in C. albicans isolates (P = 0.0068). However, no significant difference was observed in the expression of EFG1 and BCR1 genes. Conclusions: The results demonstrated that PHE significantly decreased CAT1 expression in C. albicans cells treated with the herbal extract. PHE is likely to accumulate hydrogen peroxide (H2O2) by reducing CAT1 expression and disrupting the pro-oxidant/antioxidant balance that leads to the overproduction of reactive oxygen species (ROS) and can cause damage to cellular components and eventually destroy C. albicans biofilm
Bisphenol A adsorption using modified aloe vera leaf-wastes derived bio-sorbents from aqueous solution: kinetic, isotherm, and thermodynamic studies
No full textReactive-oxygen-species are produced more often in the body when bisphenol A (BPA), an endocrine-disrupting-substance, is present. In this investigation, bio-sorbents from an aqueous solution adapted from Aloe-vera were used to survey BPA removal. Aloe-vera leaf wastes were used to create activated carbon, which was then analyzed using Fourier transform infrared (FTIR), Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Thermogravimetric analysis (TGA), Zeta potential, and Brunauer-Emmett-Teller (BET) techniques. It was revealed that the adsorption process adheres to the Freundlich isotherm model with R-2>0.96 and the pseudo-second-order kinetic model with R-2>0.99 under ideal conditions (pH = 3, contact time = 45 min, concentration of BPA = 20 mg.L-1, and concentration of the adsorbent = 2 g.L-1). After five-cycle, the efficacy of removal was greater than 70. The removal of phenolic-chemicals from industrial-effluent can be accomplished with the assistance of this adsorbent in a cost-effective and effective-approach