Comparison of the effects of eicosapentaenoic acid with docosahexaenoic acid on the level of serum lipoproteins in helicobacter pylori: A randomized clinical trial

Background: Helicobacter pylori infection is the most common chronic bacterial infection around the world and an important cause of gastrointestinal disorders, which might be involved in the pathogenesis of some extragastrointestinal disturbances as well as changes in serum lipid profile. Hypolipemic properties of omega-3 fatty acids have been studied in several studies. Objectives: The present study aimed to compare the effects of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) supplementation on the level of serum lipoproteins in H. pylori. Patients and Methods: In a randomized, double-blinded, placebo-controlled clinical trial in Iran, 105 Helicobacter pylori were randomly allocated to receive 2 g of daily EPA (35 patients), DHA (35 patients), or medium-chain triglyceride (MCT) oil as placebo (33 patients) along with conventional tetra-drug H. pylori eradication regimen for 12 weeks. Results: From 105 included patients, 97 (31 in EPA, 33 in DHA, and 33 in control groups) completed the study and were included in final analysis. The levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and the ratios of TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C were not significantly different among the three groups, while the level of triglyceride (TG) was statistically different. DHA (-16.6 ± 30.34) and control (+ 15.32 ± 56.47) groups were statistically different with regard to changes in TG levels (P = 0.000). Conclusions: There was no difference between the effects of 2 g of EPA or DHA supplementation for 12 weeks on the levels of total cholesterol, LDL-C, HDL-C, TC/HDL-C, TG/HDL-C, and LDL-C/HDL-C; however, it had a desirable effect on the level of TG in a way that the effect of DHA was clearer. © 2015, Iranian Red Crescent Medical Journal

Relapse, mortality, and the associated factors in children with acute lymphoblastic leukemia; a competing risks analysis

Background: Acute lymphoblastic leukemia (ALL) is the most frequent form of malignant neoplasia diagnosed in ages 0 to 14 years old. Efforts have not yet converted into a better prospect. Bone marrow relapse is still the leading cause of person-year of life lost in this malignancy. Objectives: This study aimed at identifying the associated risk factors for relapse and mortality for pediatric patients with ALL in standard and high-risk groups. Methods: This study included a cohort of pediatric (0-16 years old) patients with ALL referred to Sheikh Hospital, Mashhad, Iran from 2007 to 2016. The demographic, clinical, and laboratory information were considered. Hazard ration (HR) with 95 highest posterior density region was obtained, using a Bayesian competing risks model. Results: Of 424 patients with a mean age of 5.56 ± 3.75 years, 172 (40) were female. Median follow-up time was 43.29 months, 10.6 had a relapse, and 17.2 had mortality related to ALL. Relapse-free survival rates at 1, 3, and 5 years were 97, 91, and 88, respectively. Overall survival rates were 86, 83, and 82, respectively. In the standard-risk group, tumor lysis syndrome (TLS) significantly increased either the relapse risk HR: 13.47 (2.05-67.54) or mortality risk HR: 19.57 (2.24-32.18). In the high-risk group, the higher level of hemoglobin, platelet, and lactic acid dehydrogenase was significantly associated with higher relapse risk. TLS was associated with a higher risk of mortality in high-risk groups. Conclusions: It was suggested that TLS was a predictor for the disease relapse as well as mortality in pediatric patients with ALL. However, further evaluation on the larger population of patients is demanded to ascertain the precision of such parameters in leukemic management strategies. © 2021, Author(s)

An improper form of Weibull distribution for competing risks analysis with Bayesian approach

In survival analysis, individuals may fail due to multiple causes of failure called competing risks setting. Parametric models such as Weibull model are not improper that ignore the assumption of multiple failure times. In this study, a novel extension of Weibull distribution is proposed which is improper and then can incorporate to the competing risks framework. This model includes the original Weibull model before a pre-specified time point and an exponential form for the tail of the time axis. A Bayesian approach is used for parameter estimation. A simulation study is performed to evaluate the proposed model. The conducted simulation study showed identifiability and appropriate convergence of the proposed model. The proposed model and the 3-parameter Gompertz model, another improper parametric distribution, are fitted to the acute lymphoblastic leukemia dataset. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group

The Communication Function Classification System: Cultural adaptation, validity, and reliability of the Farsi version for patients with cerebral palsy

Background This study developed a Farsi language Communication Function Classification System and then tested its reliability and validity. Methods Communication Function Classification System is designed to classify the communication functions of individuals with cerebral palsy. Up until now, there has been no instrument for assessment of this communication function in Iran. The English Communication Function Classification System was translated into Farsi and cross-culturally modified by a panel of experts. Professionals and parents then assessed the content validity of the modified version. A backtranslation of the Farsi version was confirmed by the developer of the English Communication Function Classification System. Face validity was assessed by therapists and parents of 10 patients. The Farsi Communication Function Classification System was administered to 152 individuals with cerebral palsy (age, 2 to 18 years; median age, 10 years; mean age, 9.9 years; standard deviation, 4.3 years). Inter-rater reliability was analyzed between parents, occupational therapists, and speech and language pathologists. The test-retest reliability was assessed for 75 patients with a 14 day interval between tests. Results The inter-rater reliability of the Communication Function Classification System was 0.81 between speech and language pathologists and occupational therapists, 0.74 between parents and occupational therapists, and 0.88 between parents and speech and language pathologists. The test-retest reliability was 0.96 for occupational therapists, 0.98 for speech and language pathologists, and 0.94 for parents. Conclusions The findings suggest that the Farsi version of Communication Function Classification System is a reliable and valid measure that can be used in clinical settings to assess communication function in patients with cerebral palsy. © 2015 Elsevier Inc. All rights reserved

Evaluation of parametric models by the prediction error in colorectal cancer survival analysis

Aim: The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error's technique. Background: Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. Patients and methods: A total of 600 colorectal cancer patients who admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, were followed at least for 5 years and have completed selected information for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric models including Log normal, Weibull and Log logistic regression. For selecting the best model, the prediction error by apparent loss was used. Results: Log rank test showed a better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated and indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors, according to Weibull model. Conclusion: In this study, according to prediction error Weibull regression showed a better fit. Prediction error would be a criterion to select the best model with the ability to make predictions of prognostic factors in survival analysis. © 2015 RIGLD, Research Institute for Gastroenterology and Liver Diseases

Cause-specific hazard regression estimation for modified Weibull distribution under a class of non-informative priors

In time to event analysis, the situation of competing risks arises when the individual (or subject) may experience p mutually exclusive causes of death (failure), where cause-specific hazard function is of great importance in this framework. For instance, in malignancy-related death, colorectal cancer is one of the leading causes of the death in the world and death due to other causes considered as competing causes. We include prognostic variables in the model through parametric Cox proportional hazards model. Mostly, in literature exponential, Weibull, etc. distributions have been used for parametric modelling of cause-specific hazard function but they are incapable to accommodate non-monotone failure rate. Therefore, in this article, we consider a modified Weibull distribution which is capable to model survival data with non-monotonic behaviour of hazard rate. For estimating the cumulative cause-specific hazard function, we utilized maximum likelihood and Bayesian methods. A class of non-informative types of prior (uniform, Jeffrey�s and half-t) is introduced for Bayes estimation under squared error (symmetric) as well as LINEX (asymmetric) loss functions. A simulation study is performed for a comprehensive comparison of Bayes and maximum likelihood estimators of cumulative cause-specific hazard function. Real data on colorectal cancer is used to demonstrate the proposed model. © 2021 Informa UK Limited, trading as Taylor & Francis Group