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Age and Sex Standardized Prevalence of Corneal Opacity and Its Determinants: Tehran Geriatric Eye Study (TGES)
No full textBackground: We aimed to determine the age and sex standardized prevalence of corneal opacity and its determinants Methods: The Tehran Geriatric Eye Study (TGES) is a population-based cross-sectional study conducted on 3791 subjects aged above 60 yr in Tehran, Iran (2019) selected using stratified random cluster sampling. After sampling, all subjects underwent complete ophthalmic, optometric, and eye examinations. Results: The 3310 participated in the study, of whom the data of 3284 were analyzed. The age and sex standardized prevalence with 95 confidence interval (CI) of corneal opacity in at least one eye, both eyes, and one eye was 9.58 (95 CI: 8.50 to 10.79), 5.52 (95 CI: 4.71 to 6.45), and 4.07 (95 CI: 3.35 to 4.94), respectively. The mean uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA) according to LogMar were worse in subjects with corneal opacity (both P80 yr (OR: 2.05; P: 0.004), and lack of insurance coverage (OR: 1.87; P: 0.004) increased the odds and high school education (OR: 0.68; P: 0.003) reduced the odds of corneal opacity. Among the study variables, sex was the most important determinant of corneal opacity (standardized beta: 0.126). Conclusion: This study found a high prevalence of corneal opacity in the geriatric population. Considering the increasing trend of population aging in Iran, attention should be paid to prioritizing public health policies to estimate resources required for providing comprehensive corneal services and improving geriatric eye health
The Prevalence of Frailty and its Associated Factors among Iranian Hospitalized Older Adults
No full textBackground: As the population ages, the impact of age-related diseases on health is becoming more apparent. Frailty is one of the most important issues faced by older adults. Objectives: This study aimed to determine the prevalence of frailty and the factors affecting it among older adults admitted to teaching hospitals in Ilam in 2020. Methods: This cross-sectional study was performed on 270 older adults admitted to teaching hospitals in Ilam. Participants were selected through consecutive sampling. Data were collected using the Tilburg Frailty Indicator and analyzed by the Chi-square test and logistic regression analysis. Results: The mean age of the older adults participating in the study was 71.97 +/- 8.42 years. Overall, 18.1 of older adults were frail, and frailty was significantly associated with having a chronic disease, being accompanied by a close relative, hospitalization, age, sex, marital status, and education level (P < 0.05). The most important predictors of frailty in older adults were age, sex, history of stroke, and being accompanied by a close relative (P < 0.05). Conclusion: About one-fifth of the older adults participating in this study were frail. The prevalence of frailty was higher among women, those with chronic diseases or a history of stroke, single people, and those with low education levels. Therefore, these people need special attention
Microbiological and drug resistance patterns of bronchoalveolar lavage samples taken from hospitalized patients in Iran
No full textIntroduction Pulmonary diseases are amongst the most common causes of premature death and distressing disorders worldwide. This study aimed to detect the fastidious and routine infectious agents, and their drug resistance patterns in bronchoalveolar lavage (BAL) samples.Methods A total of 44 BAL samples were collected by bronchoscopy from patients with respiratory disorders hospitalized at 2 teaching hospitals in Ilam, Iran. The samples were cultured on routine bacterial culture media to identify the bacterial agents and calculate the colony count. Antibiotic susceptibility was determined by disk diffusion method according to the CLSI protocol. PCR was used to detect the fastidious bacteria Mycoplasma pneumoniae and Chlamydia pneumoniae using the 16srRNA specific primers and Legionella pneumophila using the mip specific primers.Results Overall, 100 bacterial isolates were isolated by culture from the 44 BAL samples including: Staphylococcus aureus (24, 31.2 ), Streptococcus pyogenes (18, 23.4 ), Enterococcus spp. (11, 14.3 ), Acinetobacter baumannii (11, 14.3 ), Pseudomonas aeruginosa (11, 14.3 ), Enterococcus spp. (10, 13), Micrococcus spp. (5, 6.5 ), Staphylococcus epidermidis (5, 6.5) and Klebsiella pneumoniae (5, 6.5). PCR detected 4 positive samples (9.1 ) for Chlamydia pneumoniae but no positive cases for Mycoplasma pneumoniae and Legionella pneumophila. Acinetobacter baumannii showed the highest resistance rate (81.8 ) to aztreonam and ceftazidime. Seventy-five percent of the Staphylococcus aureus isolates were resistant to cefoxitin (MRSA) and 83.3 had the mecA gene. Vancomycin resistance was observed in 27.3 of the Enterococcus species (VRE). Resistance to piperacillin, cefotaxime, ciprofloxacin and imipenem was observed in 54.5, 45.5, and 36.4 of the Pseudomonas aeruginosa isolates, respectively. The frequency of organisms isolated from the ICU was higher (46) than from other wards.Conclusions The presence of MRSA, cephalosporins-resistant Enterobacteriaceae as well as Pseudomonas aeruginosa and Acinetobacter baumannii resistant against piperacillin, imipenem, cefotaxime, aztreonam and ciprofloxacin amongst different wards, especially the ICU ward of the surveyed hospitals, is a major healthcare concern and it is necessary to wisely scrutinize the preventive strategies for antibiotic resistant infections
Computational Clues of Immunogenic Hotspots in Plasmodium falciparum Erythrocytic Stage Vaccine Candidate Antigens: In Silico Approach
No full textMalaria is the most pernicious parasitic infection, and Plasmodium falciparum is the most virulent species with substantial morbidity and mortality worldwide. The present in silico investigation was performed to reveal the biophysical characteristics and immunogenic epitopes of the 14 blood-stage proteins of the P. falciparum using comprehensive immunoinformatics approaches. For this aim, various web servers were employed to predict subcellular localization, antigenicity, allergenicity, solubility, physicochemical properties, posttranslational modification sites (PTMs), the presence of signal peptide, and transmembrane domains. Moreover, structural analysis for secondary and 3D model predictions were performed for all and stable proteins, respectively. Finally, human helper T lymphocyte (HTL) epitopes were predicted using HLA reference set of IEDB server and screened in terms of antigenicity, allergenicity, and IFN-gamma induction as well as population coverage. Also, a multiserver B-cell epitope prediction was done with subsequent screening for antigenicity, allergenicity, and solubility. Altogether, these proteins showed appropriate antigenicity, abundant PTMs, and many B-cell and HTL epitopes, which could be directed for future vaccination studies in the context of multiepitope vaccine design
Comparison of the effect of ferrous sulfate and ferrous gluconate on prophylaxis of iron deficiency in toddlers 6-24 months old: A randomized clinical trial
No full textBACKGROUND: Iron deficiency anemia (IDA) is one of the most common anemias, especially in children 4-23 months. Therefore, prophylaxis is necessary to improve iron status as well as reduce IDA in Toddlers. The aim of this study was to compare the efficacy of daily supplementation with ferrous gluconate (FG) and ferrous sulfate (FS) on iron status in toddlers. MATERIALS AND METHODS: A total of 120 healthy toddlers were divided randomly into 2 groups at the Amir-Kabir Hospital, Arak, Iran and received FS and FG from March 2020 to December 2020. Iron status was evaluated at baseline and after 6 months of supplementation. The statistical significance of the differences in iron status between FS and FG groups was calculated using Student's t-test and the Pearson' s Chi-square test for qualitative variables. SPSS software (version 16, Chicago, IL, USA) was used for statistical analysis. RESULTS: Comparison of iron status of FS and FG groups toddlers at baseline and after 6 months of supplementation showed that there was a significant difference in hemoglobin (Hb) (10.46 vs. 12.45, P = 0.001) and ferritin level (28.08 vs. 59.63, P = 0.001). CONCLUSIONS: Although prophylaxis with FG led to a higher Hb and ferritin levels, our study recommended that both FG and FS supplements were effective for prophylactic use in the prevention of IDA. However, FG was more effective than FS because FG group that received FG supplementation indicated a higher Hb and ferritin levels in comparison to the FS group that received FS supplementation
Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms
No full textBACKGROUND: Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA). OBJECTIVE: This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. MATERIAL AND METHODS: In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models. RESULTS: A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. CONCLUSION: The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models
Design of Clinical Decision Support System to Diagnose Breast Cancer: An Approach Using Data Mining
No full textBackground and Aim: Breast cancer is one of the most common and aggressive malignancies in women. Timely diagnosis of breast cancer plays an important role in preventing the progression of this disease, timely treatment measures, and aftermath reducing the mortality rate of these patients. Machine learning has the potential ability to diagnose diseases quickly and cost-effectively. This study aims to design a CDSS based on the rules extracted from the decision tree algorithm with the best performance to diagnose breast cancer in a timely and effective manner. Materials and Methods: The data of 597 suspected people with breast cancer (255 patients and 342 healthy people) were retrospectively extracted from the electronic database of Ayatollah Taleghani Hospital in Abadan city with 24 characteristics, mainly pertained to lifestyle and medical histories. After selecting the most important variables by using the Chi-square Pearson and one-way analysis of variance (P<0.05), the performance of selected data mining algorithms including RF, J-48, DS, RT and XG -Boost was evaluated for breast cancer diagnosis in Weka 3.4 software. Finally, the breast cancer diagnostic system was designed based on the best model and through C# programming language and Dot Net Framework V3.5.4. Results: Fourteen variables including personal history of breast cancer, breast sampling, and chest X-ray, high blood pressure, increased LDL blood cholesterol, presence of mass in upper inner quadrant of the breast, hormone therapy with estrogen, hormone therapy with Estrogen-progesterone, family history of breast cancer, age, history of other cancers, waist-to-hip ratio and fruit and vegetable consumption showed a significant relationship with the output class at the P<0.05. Based on the results of the performance evaluation of selected algorithms, the RF model with sensitivity, specificity, accuracy, and F- measure equal to 0.97, 0.99, 0.98, 0.974, respectively, AUC=0.936 had higher performance than other selected algorithms and was suggested as the best model for breast cancer diagnosis. Conclusion: It seems that using modifiable variables such as lifestyle and reproductive-hormonal characteristics as input to the RF algorithm to design the CDSS, can detect breast cancer cases with optimal accuracy. In addition, the proposed system can be effectively adapted in real clinical environments for quick and effective disease diagnosis. © 2022, Tehran University of Medical Sciences. All rights reserved
ABO rs657152 and Blood Groups Are as Predictor Factors of COVID-19 Mortality in the Iranian Population
No full textSeveral studies have discovered a relationship between specific blood types, genetic variations of the ABO gene, and coronavirus disease 2019 (COVID-19). Therefore, the aim of this study was to evaluate the association between ABO rs657152 polymorphisms and ABO blood groups with COVID-19 mortality. The tetraprimer amplification refractory mutation system, polymerase chain reaction method, was used for ABO rs657152 polymorphism genotyping in 1,211 dead and 1,442 improved patients. In the current study, the frequency of ABO rs657152 AA than CC genotypes was significantly higher in dead patients than in improved patients. Our findings indicated that blood type A was associated with the highest risk of COVID-19 mortality compared to other blood groups, and patients with blood type O have a lower risk of infection, suggesting that blood type O may be a protective factor against COVID-19 mortality. Multivariate logistic regression test indicated that higher COVID-19 mortality rates were linked with alkaline phosphatase, alanine aminotransferase, high density lipoprotein, low-density lipoprotein, fasting blood glucose, uric acid, creatinine, erythrocyte sedimentation rate, C-reactive protein, 25-hydroxyvitamin D, real-time PCR Ct values, ABO blood groups, and ABO rs657152 AA genotype. In conclusion, the AA genotype of ABO rs657152 and blood type A were associated with a considerably increased frequency of COVID-19 mortality. Further research is necessary to validate the obtained results. © 2022 Fahimeh Mirzaei Gheinari et al
Melatonin in cryopreservation media improves transplantation efficiency of frozen–thawed spermatogonial stem cells into testes of azoospermic mice
No full textBackground: Cryostorage of spermatogonial stem cells (SSCs) is an appropriate procedure for long-term storage of SSCs for fertility preservation. However, it causes damage to cellular structures through overproduction of ROS and oxidative stress. In this study, we examined the protective effect of melatonin as a potent antioxidant in the basic freezing medium to establish an optimal cryopreservation method for SSCs. Methods: SSCs were obtained from the testes of neonatal male mice aged 3–6 days. Then, 100 μM melatonin was added to the basic freezing medium containing DMSO for cryopreservation of SSCs. Viability, apoptosis-related markers (BAX and BCL2), and intracellular ROS generation level were measured in frozen–thawed SSCs before transplantation using the MTT assay, immunocytochemistry, and flow cytometry, respectively. In addition, Western blotting and immunofluorescence were used to evaluate the expression of proliferation (PLZF and GFRα1) and differentiation (Stra8 and SCP3) proteins in frozen–thawed SSCs after transplantation into recipient testes. Results: The data showed that adding melatonin to the cryopreservation medium markedly increased the viability and reduced intracellular ROS generation and apoptosis (by decreasing BAX and increasing BCL2) in the frozen–thawed SSCs (p < 0.05). The expression levels of proliferation (PLZF and GFRα1) and differentiation (Stra8 and SCP3) proteins and resumption of spermatogenesis from frozen–thawed SSCs followed the same pattern after transplantation. Conclusions: The results of this study revealed that adding melatonin as an antioxidant to the cryopreservation medium containing DMSO could be a promising strategy for cryopreservation of SSCs to maintain fertility in prepubertal male children who suffer from cancer. © 2022, The Author(s)
Developing the breast cancer risk prediction system using hybrid machine learning algorithms
No full textBACKGROUND: Breast cancer (BC) is the most common cause of cancer-related deaths in women globally. Currently, many machine learning (ML)-based predictive models have been established to assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation even with having healthy lifestyle behaviors or preventing disease at early stages can significantly lead to optimal population-wide BC health. Thus, we aimed to develop a prediction model by using a genetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC. MATERIAL AND METHODS: The data of 3168 healthy individuals and 1742 patient case records in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a modified hybrid GA was used to perform feature selection and optimization of selected features. Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward, the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support system based on the best model was developed. RESULTS: After performing feature selection, age, consumption of dairy products, BC family history, breast biopsy, chest X-ray, hormone therapy, alcohol consumption, being overweight, having children, and education statuses were selected as the most important features for prediction of BC. The experimental results showed that the decision tree yielded a superior performance than other ML models, with values of 99.3, 99.5, 98.26 for accuracy, specificity, and sensitivity, respectively. CONCLUSION: The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies. © 2022 Journal of Research in Medical Sciences | Published by Wolters Kluwer - Medknow