Secondary analysis: Graph analysis of brain connectivity network in autism spectrum disorder.

Background: Autism spectrum disorder is a neurodevelopmental condition in which impaired connectivity of the brain network. The functional magnetic resonance imaging (fMRI) technique can provide information on the early diagnosis of autism by evaluating communication patterns in the brain. The present study aimed to assess functional connectivity (FC) variations in autism patients. Materials and Methods: Resting-state fMRI data were obtained from the "ABIDE" website. These data include 294 autism patients with a mean (standard deviation) age of 16.49 (7.63) and 312 healthy individuals with a mean (standard deviation) age of 15.98 (6.31). In this study, changes in communication patterns across different brain regions in autism patients were investigated using graph-based models. Results: The FC cluster of 17 regions in the brain, such as the hippocampus, cuneus, and inferior temporal, was different between the patient and healthy groups. Based on connectivity analysis of pair regions, 36 of the 136 correlations in the cluster were significantly different between the two groups. The middle temporal gyrus had more communication than the other regions. The largest difference between groups was - 0.112, which corresponding to the right middle temporal and right thalamus regions. Conclusion: The findings of this study revealed functional relationship alterations in patients with autism compared to healthy individuals, indicating the disease's effects on the brain connectivity network.

Potential metabolic biomarkers of critical limb ischemia in people with type 2 diabetes mellitus.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a significant risk factor for the development of critical limb ischemia (CLI), the most advanced stage of peripheral arterial disease. The concurrent existence of T2DM and CLI often leads to adverse outcomes, namely limb amputation. OBJECTIVE: To identify biomarkers for improving the screening of CLI in high-risk people with T2DM. METHODS: We investigated metabolome profiles in serum samples of 113 T2DM people with CLI (n = 23, G2) and without CLI (n = 45, G0: no lower limb stenosis (LLS) and n = 45, G1: LLS < 50%), using hydrogen nuclear magnetic resonance (1H NMR) approach. Principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to analyze 1H NMR data. RESULTS: Twenty potential metabolites that could discriminate people with T2DM and CLI (G2) from non-CLI patients without LLS (G0) were determined in serum samples. The correct percent of classification for the PLS-DA model for the test set samples was 85% (n = 20) and 100% (n = 5) for G0 and G2 groups, respectively. Non-CLI patients with LLS < 50% (G1) were projected on the PCA abstract space built using 20 discriminatory metabolites. Eleven people with T2DM and LLS < 50% were prospectively followed, and their ankle-brachial index (ABI) was measured after 4 years. A promising agreement existed between the PCA model's predictions and those obtained by ABI values. CONCLUSION: The findings suggest that confirmation of blood potential metabolic biomarkers as a complement to ABI for screening of CLI in a large group of high-risk people with T2DM is needed.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department.

Background: The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). Materials and methods: A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation. Results: The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. Conclusion: The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors. How to cite this article: Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

Group-level comparison of brain connectivity networks.

BACKGROUND: Functional connectivity (FC) studies are often performed to discern different patterns of brain connectivity networks between healthy and patient groups. Since many neuropsychiatric disorders are related to the change in these patterns, accurate modelling of FC data can provide useful information about disease pathologies. However, analysing functional connectivity data faces several challenges, including the correlations of the connectivity edges associated with network topological characteristics, the large number of parameters in the covariance matrix, and taking into account the heterogeneity across subjects. METHODS: This study provides a new statistical approach to compare the FC networks between subgroups that consider the network topological structure of brain regions and subject heterogeneity. RESULTS: The power based on the heterogeneity structure of identity scaled in a sample size of 25 exhibited values greater than 0.90 without influencing the degree of correlation, heterogeneity, and the number of regions. This index had values above 0.80 in the small sample size and high correlation. In most scenarios, the type I error was close to 0.05. Moreover, the application of this model on real data related to autism was also investigated, which indicated no significant difference in FC networks between healthy and patient individuals. CONCLUSIONS: The results from simulation data indicated that the proposed model has high power and near-nominal type I error rates in most scenarios.

Analysis of injuries and deaths from road traffic accidents in Iran: bivariate regression approach.

BACKGROUNDS: This study aims to estimate and compare the parameters of some univariate and bivariate count models to identify the factors affecting the number of mortality and the number of injured in road accidents. METHODS: The accident data used in this study are related to Kermanshah province in march2020 to march2021. Accidents areas were divided into 125 areas based on density characteristics. In a one-year period, 3090 accidents happened on the suburban roads of Kermanshah province, which resulted in 398 deaths and 4805 injuries. Accident information, including longitude and latitude of accident location, type of accident (fatal and injury), number of deaths, number of injuries, accident type, the reason of the accident, and the kind of accident were all included as population-level variables in the regression models. We investigated four frequently used bivariate count regression models for accident data in the literature. RESULTS: In bivariate analysis, except for the DNM model, there is a reasonable decrease in the AIC measures of the saturated model compared to the reduced model for the other three models. For the injury models, MSE is lowest, respectively for DIBP (137.87), BNB (289.46), BP (412.36) and DNM (3640.89) models. These results are also established for death models. But, in univariate analysis, only injury models almost present reasonable results. CONCLUSIONS: Our findings show that the IDBP model is better suitable for evaluating accident datasets than other models. Motorcycle accidents, pedestrian accidents, left turn deviance, and dangerous speeding were all significant variables in the IDBP death model, and these parameters were linked to accident mortality.

Metabolic Syndrome and Ischemic Stroke in Iran: Incidence and Disability-Adjusted Life Years 1990 to 2019.

Introduction: Metabolic syndrome (MS) is one of the conditions that may increase the risk of ischemic stroke (IS). This study focuses on factors of MS related to the incidence, years of life lost (YLL), and years of life disability (YLD) caused by IS. Materials and Methods: The age-adjusted data (incidence, YLL, and YLD) related to four factors of MS [fasting plasma glucose, body mass index (BMI), low-density lipoprotein, and systolic blood pressure, SBP] were extracted as risk factors for IS during the years 1990 to 2019 from global burden of disease study. ARC GIS V.10 software was used to investigate the geographical distribution and the Cochran-Armitage test was used to investigate the trend of these indices. Results: The highest incidence of IS was reported in Golestan provinces in 2019 [women: 180 (95% UI 153-213), men: 163 (95% UI 141-191), and both genders: 172 (95% UI 149-201)]. High SBP has the highest impact on YLD (96-102 per 100,000 population in some provinces) and YLL (688-824 per 100,000 population in some provinces) in men and women (YLD: 126-156, YLL: 586-785 per 100,000 population in some provinces). High BMI has increased YLD caused by IS from 1990 (35 per 100,000 population) to 2019 (53 per 100,000 population). Conclusion: Considering the importance of IS and the impact that MS has on this disease, it seems that one of the most effective possible measures is to eliminate MS. The factors of MS are mostly related to lifestyle, and MS can be controlled with a suitable plan, which can be considered as primary prevention for IS.

The Global Burden of Metabolic Risk in Cardiovascular Disease: Trends in Disability-Adjusted Life Years from the Global Burden of Disease Study, 2019.

Introduction: Metabolic risk factors significantly elevate the risk of cardiovascular diseases (CVDs) and are recognized as one of the primary contributors to these conditions. This study explored the trends and percentage changes in disability-adjusted life years (DALYs) attributable to metabolic risk factors for CVD from 1990 to 2019. Methods: Data from the Global Burden of Disease study was utilized to depict changes across metabolic risk factors by sociodemographic indices and sex groups. The Mann-Kendall test was employed to ascertain the significance of these trends. Results: The findings indicate that the DALY percentage change over these years was more pronounced in low-middle and middle Socio-demographic Index (SDI) countries than in others. The most substantial percentage increase, approximately 60%, was observed in regions with low SDI, attributed to high body mass index (BMI). On average, the greatest DALYs were associated with high systolic blood pressure (SBP). Moreover, the trend for high low-density lipoprotein cholesterol and high SBP declined in countries with high SDI. Conclusions: It can be concluded that to mitigate the global mortality and morbidity from heart diseases, there needs to be a focused emphasis on managing metabolic risk factors, particularly in low-Middle and middle SDI countries.

The association of preoperative hematologic parameters with short-term clinical outcomes in rectal cancer: A feature importance analysis.

BACKGROUND: Various hematologic parameters have been proposed as prognostic factors in rectal cancer management, but data are conflicting and unclear. This study is designed to investigate the prognostic factor capability of preoperative hematologic parameters with postoperative morbidities and mortality in rectal cancer patients undergoing curative resection. METHODS: All 200 consecutive rectal cancer patients diagnosed at Ghaem University Hospital from 2017 to 2022 were retrospectively evaluated. The receiver operating characteristic (ROC) curves and machine learning (ML) algorithms of Random Forest, Recursive Feature Elimination, simulated annealing, Support Vector Machine, Decision Tree, and eXtreme Gradient Boosting were administered to investigate the role of preoperative hematologic parameters accompanied by baseline characteristics on three clinical outcomes including surgical infectious complications, recurrence, and death. RESULTS: The frequency of infectious complications was correlated with the surgical procedure, while tumor recurrence was significantly influenced by T stage and N stage. In terms of mortality, alongside T and N stage, the status of resection margin involvement was significantly correlated. Based on the ROC analysis, the NLR >2.69, MPV ≤9 fL, and PDW ≤10.5 fL were more classified patients to mortality status. Likewise, the PLT >220 109/L, MPV ≤9 fL, PDW ≤10.4 fL, and PLR >13.6 were correlated with recurrence. However, all factors examined in this study were not significant classifiers for the outcome of surgical infectious complications. The results of ML algorithms were also in line with ROC analysis. CONCLUSION: According to the results of both ROC analysis and ML models, preoperative hematologic parameters are considerable prognostic factors of postoperative outcomes in rectal cancer patients, and are recommended to be monitored by clinicians to prevent unfavorable outcomes.

Adjustment for collider bias in the hospitalized Covid-19 setting.

Background: Causal directed acyclic graphs (cDAGs) are frequently used to identify confounding and collider bias. We demonstrate how to use causal directed acyclic graphs to adjust for collider bias in the hospitalized Covid-19 setting. Materials and methods: According to the cDAGs, three types of modeling have been performed. In model 1, only vaccination is entered as an independent variable. In model 2, in addition to vaccination, age is entered the model to adjust for collider bias due to the conditioning of hospitalization. In model 3, comorbidities are also included for adjustment of collider bias due to the conditioning of hospitalization in different biasing paths intercepting age and comorbidities. Results: There was no evidence of the effect of vaccination on preventing death due to Covid-19 in model 1. In the second model, where age was included as a covariate, a protective role for vaccination became evident. In model 3, after including chronic diseases as other covariates, the protective effect was slightly strengthened. Conclusion: Studying hospitalized patients is subject to collider-stratification bias. Like confounding, this type of selection bias can be adjusted for by inclusion of the risk factors of the outcome which also affect hospitalization in the regression model.

Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.

Background: Automated image segmentation is an essential step in quantitative image analysis. This study assesses the performance of a deep learning-based model for lung segmentation from computed tomography (CT) images of normal and COVID-19 patients. Methods: A descriptive-analytical study was conducted from December 2020 to April 2021 on the CT images of patients from various educational hospitals affiliated with Mashhad University of Medical Sciences (Mashhad, Iran). Of the selected images and corresponding lung masks of 1,200 confirmed COVID-19 patients, 1,080 were used to train a residual neural network. The performance of the residual network (ResNet) model was evaluated on two distinct external test datasets, namely the remaining 120 COVID-19 and 120 normal patients. Different evaluation metrics such as Dice similarity coefficient (DSC), mean absolute error (MAE), relative mean Hounsfield unit (HU) difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The Mann-Whitney U test was used to assess the difference between the corresponding values in the normal and COVID-19 patients. P<0.05 was considered statistically significant. Results: The ResNet model achieved a DSC of 0.980 and 0.971 and a relative mean HU difference of -2.679% and -4.403% for the normal and COVID-19 patients, respectively. Comparable performance in lung segmentation of normal and COVID-19 patients indicated the model's accuracy for identifying lung tissue in the presence of COVID-19-associated infections. Although a slightly better performance was observed in normal patients. Conclusion: The ResNet model provides an accurate and reliable automated lung segmentation of COVID-19 infected lung tissue.A preprint version of this article was published on arXiv before formal peer review (https://arxiv.org/abs/2104.02042).

Bayesian statistics versus classical statistics in survival analysis: an applicable example.

BACKGROUND: Heart disease is the leading cause of death in the world and 17 million people die from cardiovascular diseases around the world each year, so finding factors that affect the survival of these patients is of particular importance. Therefore, finding the best model to analyze patient survival can help to find more accurate results. METHODS: There are different methods to survival analysis that assess one or more risk factors; the classic Kaplan-Meier method, Cox regression, parametric survival models, and newer models such as Bayesian survival. Cox regression is most common and is generally used for time-dependent data, and the main difference between cox regression and Bayesian models is that the prior distribution in Bayesian models can affect the values of the parameters. Some survival analysis models have certain conditions that need to be considered before analyzing the data. In this paper, we use a dataset from Kaggle and discuss these conditions. This dataset contains medical records of 299 patients with heart failure collected at the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad (Punjab, Pakistan) from April to December 2015. RESULTS: This paper discusses that if the effective sample size is not sufficient, Bayesian survival models can be used to achieve more accurate results because this model is not affected by the sample size. The results of both methods are shown on a sample of cardiac data and based on the results of Bayesian Cox regression model, it was observed that Age, Anemia, Ejection fraction, High blood pressure and Serum creatinine were effective on patient survival. CONCLUSION: The Bayesian models are much more accurate to determine survival and determine risk factors when dealing with data on rare diseases or diseases with low mortality, including heart patients whose survival probability is higher than that of cancer patients.

Measles, mumps, and rubella (MMR) vaccine and COVID-19: a systematic review.

This study was performed to investigate published literature about the association between measles, mumps, and rubella (MMR) vaccine and COVID-19. This is a systematic review in which the databases of Chocrane, Pubmed, Scopus, Web of Science as well as reliable journals including Lancet, New England Journal of Medicine, Jama and also Centers for Disease Control and Prevention (CDC) publications were searched.Out of 169 documents discovered during the literature review, 56 ones were somehow related to the association between MMR vaccine and COVID-19, of which 11 ones mentioned the association between these two, and 8 of them contained a hypothesis about this relationship. A quasi-trial study reported the positive effect of the MMR vaccine on reducing the severity of COVID-19 symptoms among those who received it. Also, a cross-sectional study showed an association between the level of Immunoglobulin G (IgG) mumps and COVID-19. Moreover, a genomic data analysis study also reported the effect of Rubella Immunoglobulin G (IgG) level on COVID-19. It seems that due to the similarity of respiratory diseases including measles, rubella, and mumps to COVID-19, MMR vaccine should be investigated more deeply to see if it is effective in order to deal with this novel disease.

Functional Brain Response to Emotional Musical Stimuli in Depression, Using INLA Approach for Approximate Bayesian Inference.

INTRODUCTION: One of the vital skills which has an impact on emotional health and well-being is the regulation of emotions. In recent years, the neural basis of this process has been considered widely. One of the powerful tools for eliciting and regulating emotion is music. The Anterior Cingulate Cortex (ACC) is part of the emotional neural circuitry involved in Major Depressive Disorder (MDD). The current study uses functional Magnetic Resonance Imaging (fMRI) to examine how neural processing of emotional musical auditory stimuli is changed within the ACC in depression. Statistical inference is conducted using a Bayesian Generalized Linear Model (GLM) approach with an Integrated Nested Laplace Approximation (INLA) algorithm. METHODS: A new proposed Bayesian approach was applied for assessing functional response to emotional musical auditory stimuli in a block design fMRI data with 105 scans of two healthy and depressed women. In this Bayesian approach, Unweighted Graph-Laplacian (UGL) prior was chosen for spatial dependency, and autoregressive (AR) (1) process was used for temporal correlation via pre-weighting residuals. Finally, the inference was conducted using the Integrated Nested Laplace Approximation (INLA) algorithm in the R-INLA package. RESULTS: The results revealed that positive music, as compared to negative music, elicits stronger activation within the ACC area in both healthy and depressed subjects. In comparing MDD and Never-Depressed (ND) individuals, a significant difference was found between MDD and ND groups in response to positive music vs negative music stimuli. The activations increase from baseline to positive stimuli and decrease from baseline to negative stimuli in ND subjects. Also, a significant decrease from baseline to positive stimuli was observed in MDD subjects, but there was no significant difference between baseline and negative stimuli. CONCLUSION: Assessing the pattern of activations within ACC in a depressed individual may be useful in retraining the ACC and improving its function, and lead to more effective therapeutic interventions.

Topical Administration of Teucrium polium on Diabetic Foot Ulcers Accelerates Healing: A Placebo-Controlled Randomized Clinical Study.

Diabetic foot ulcer is one of the most devastating complications of uncontrolled diabetes. Although there have been advances in the management of diabetic foot ulcers, still diabetic foot ulcers are a major cause of many amputations in diabetic patients. Teucrium polium (T. polium) is widely used by folk medicine practitioners in Iran for the treatment of diabetic ulcers.The present study was designed to evaluate the safety and efficacy of topical T. polium ointment besides the standard treatment in diabetic foot ulcers.A total of 70 diabetic patients with foot ulcers grade 1 or 2 according to Wagner's scale were enrolled in this study. Patients were randomly divided into two groups. Patients in both groups received standard treatment for diabetic foot ulcers. In addition, group 1 received topical T. polium ointment, and group 2 received topical placebo ointment for 4 weeks. The T. polium and placebo ointments were rubbed twice daily two hours before the conventional dressing. The ulcer size, healing time, and laboratory tests were measured in both groups at baseline and end of the study after 4 weeks.Twenty-nine patients remained in the T. polium group and 26 in the placebo group until the end of the study. The mean surface area of ulcers was 3.52 ± 1.47 and 3.21 ± 1.67 cm2 in T. polium group and placebo group respectively at baseline which decrease to .717 ± .19 and 1.63 ± .72 cm2 respectively at the endpoint. The mean ulcer surface area was significantly lower in T. polium compared with the placebo group (p < .0001) at end of the study. Also, the number of patients that completely recovered in the T. polium group was significantly higher than the placebo group (p < .001) at the end of the study.The addition of topical T. polium ointment to standard treatment significantly improves the healing time of diabetic non-infected foot ulcers.

Trend of Congenital Hypothyroidism Incidence and Its Affecting Factors in Shahr-e-Kord, Western Iran.

BACKGROUND: Congenital hypothyroidism is one of the most common endocrine disrupters and metabolism, and is one of the most important preventable causes of physical and mental disabilities. METHODS: This was a case-control study, in which 54468 infants were screened from 2006 to 2014 in Shahre-Kord, western Iran. To describe the data, central and dispersion indices such as mean and standard deviation was used. For modeling, logistic regression was used. All the tests were performed at the significant level of 5%. RESULTS: Overall, 111 cases were diagnosed with hypothyroidism, which made the prevalence value equal to 2 cases per 1000 births. The prevalence in females and males was 1.9 and 2.2 per 1,000 birth, respectively. The odds ratio for this disorder was 4.47(2.42-9.28) for the neonates with a family history of hypothyroidism and 1.72(1.05-2.82) for those born through cesarean. CONCLUSION: The incidence of this disorder is similar in males and females, and the incidence of this disease in people with a family history is far more than others.

Impact of Mass Screening on the Number of Confirmed Cases, Recovered Cases, and Deaths Due to COVID-19 in Iran: An Interrupted Time Series Analysis.

BACKGROUND: Mass screening for the coronavirus disease 2019 (COVID-19) began in Iran on March 23, 2020, with the purpose of improving early detection of patients for their own health and to prevent onward transmission to others. In this study, we evaluated the impact of the change towards mass screening on new cases reported, cases recovered, and deaths due to COVID-19. METHODS: This study analyzed the daily reports on the number of new cases confirmed by polymerase chain reaction (PCR) testing, cases recovered, and deaths due to COVID-19 provided to the Ministry of Health and Medical Education of Iran. Changes in trends on these outcomes were evaluated using interrupted time series analysis. RESULTS: From February 19 to May 6, 2020, a total of 519544 COVID-19 tests were done and 101650 diagnoses were made (case/ test ratio 19.6%). For the same period, 6418 deaths due to COVID-19 were reported (case fatality ratio 6.3%). The number of cases detected increased significantly over the period of scale-up of mass screening (P=0.003), as did the number of recovered cases (P=0.001). The number of deaths due to COVID-19 did not change before versus after mass screening. CONCLUSION: Following the scale-up of mass screening for COVID-19 in Iran, the rate of new cases detected and reported recovered accelerated significantly. Mass screening is likely to have detected many mild and asymptomatic cases that were infectious. Our data support the role that mass screening, coupled with isolation and contract tracing, can have in slowing the COVID-19 epidemic.

Incidence of Colorectal Cancer and Adenomatous Polyps After a Two-Step Screening in Isfahan Province, Iran in 2018.

PURPOSE: The aim of this study was to estimate the incidence of colorectal cancers in Iran. The results of this study are based on a screening program that is being implemented in Isfahan province. MATERIAL AND METHODS: In this study, about 137,000 people were screened for colorectal cancer and adenomatous polyps in 2 steps during the year 2018. RESULTS: Based on the findings of this study, the incidence of adenomatous polyps was reported to be 389 (95% CI, 365-422) per 100,000 population. The incidence of adenomatous polyps was reported to be 483 (95% CI, 446-520) and 316 (95% CI, 286-346) in men and women, respectively (p < 0.001). Also, the incidence of colorectal cancer was reported to be 33 (95% CI, 23-42) per 100,000 population. The incidence of colorectal cancer in men and women was reported to be 41 (95% CI, 30-51) and 26 (95% CI, 17-34), respectively, indicating a statistically non-significant difference (p = 0.29). CONCLUSION: Based on the result of this study, despite a low participation rate of the target population in the screening program, the incidence of adenomatous polyps and colorectal cancer is relatively high in the average-risk population.

Incidence of adverse reaction in blood donation: a systematic review.

There are a lot of reports related to adverse reactions post blood donation. The present study is designed to investigate the incidence of adverse reactions in blood donation around the world. This research was conducted through searching databases such as PubMed, Web of Science, Scopus, EmBase, Ovid, as well as the specialized journal of TRANSFUSION without any time limit by using the keywords including "Adverse Event", "Adverse Effect", "Adverse Reaction", "Complication", "Side Effect", "Vasovagal Reaction", "Local Reaction", "General Reaction", "Allergic Reaction", "Blood Donor", and "Blood Donation". In the initial search, 7054 documents were found, of which 2517 duplicates were excluded. After screening the remaining 4,537 documents, 97 one were reviewed for quality assessment, of which 30 with the appropriate quality were selected for the review process. The results of the study showed that the reactions caused by blood donation are very different. Most reactions were systemic, and ranged from 0.08 to 13 percent in different countries. The incidence of adverse reactions in blood donation differ across the countries which might be related to the donors' characteristics. The difference did even existed in studies conducted in the same country and the same year. This suggests that many factors can cause adverse reactions in blood donation, and that a wide range of them investigated in one study, most of which were systemic.

Centrality Analysis of Protein-Protein Interaction Networks and Molecular Docking Prioritize Potential Drug-Targets in Type 1 Diabetes.

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic ß- cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic ß-cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic ß-cells (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2 and SRC) QQPPI networks. Thirteen drugs which targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenol-ESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively.

Chronic Kidney Disease in Iran: First Report of the National Registry in Children and Adolescences.

PURPOSE: Knowing the epidemiological aspects of chronic kidney disease (CKD) in children is crucial for early recognition, identification of reversible causes, and prognosis. Here, we report the epidemiological characteristics of childhood CKD in Iran. MATERIALS AND METHODS: This cross-sectional study was conducted during 1991 - 2009. The data were collected using the information in the Iranian Pediatric Registry of Chronic Kidney Disease (IPRCKD) core dataset. RESULTS: A total of 1247 children were registered. The mean age of the children at registration was 0.69 ± 4.72 years (range, 0.25 -18 years), 7.79 ± 3.18 years for hemodialysis (HD), 4.24 ± 1.86 years for continuous ambulatory peritoneal dialysis (CAPD), and 3.4±1.95 years for the children who underwent the renal transplantation (RT) (P < .001). The mean year of follow-up was 7.19 ± 4.65 years. The mean annual incidence of CKD 2-5 stages was 3.34 per million age-related population (pmarp). The mean prevalence of CKD 2-5 stages was 21.95 (pmarp). The cumulative 1-, 5-, and 10-year patients' survival rates were 98.3%, 90.7%, and 84.8%, respectively. The etiology of the CKD included the congenital anomalies of the kidney and urinary tract (CAKUT) (40.01%), glomerulopathy (19.00%), unknown cause (18.28%), and cystic/hereditary/congenital disease (11.14%). CONCLUSION: The incidence and prevalence rate of pediatric CKD in Iran is relatively lower than those reported in Europe and other similar studies. CAKUT was the main cause of the CKD. Appropriate management of CAKUT including early urological intervention is required to preserve the renal function. Herein, the long-term survival rate was higher among the children with CKD than the literature.