PurposeThis study aimed to determine out risk factors for female breast cancer in a low socioeconomic population in Iran.MethodsBetween 2007 and 2009, a total of 25,592 women who were ensured by the Imam Khomeini Relief Foundation participated in this screening program. The characteristics of patients diagnosed with breast cancer (n=111) were compared with those of control cases (n=25,481). In this study, we used relogit analysis (rare event logistic regression) with a weighting method using program Zelig.ResultsOf 25,592 women, 3.9/1,000 had breast cancer, from which 38 were diagnosed during screening and 73 had already been diagnosed. The mean and standard deviation of age in breast cancer patients and in healthy controls were 49.18±8.86 years and 46.65±9.40 years, respectively. The findings based on the multivariate model revealed that the past history of ovarian cancer, hormone therapy, and first relatives with breast cancer were associated with increased risk for breast cancer. However, the use of oral contraceptive pills was found to be associated with reduced risk for breast cancer.ConclusionDue to the rarity of the event in the population, relogit with a weighting method was used to investigate the major risk factors for breast cancer. These factors include oral contraceptive pill use, a history of ovarian cancer of the person under study, first relatives with breast cancer and hormone therapy.

Background The management of COVID-19 in organ transplant recipients is among the most imperative, yet less discussed, issues based on their immunocompromised status along with their vast post-transplant medication regimens. No conclusive study has been published to evaluate proper anti-viral and immunomodulator medications effect in treating COVID-19 patients to this date. Method This retrospective study was conducted in Shiraz Transplant Hospital, Iran from March 2020 to May 2021 and included COVID-19 diagnosed patients based on SARS-CoV-2 RT-PCR positive test who had been hospitalized for at least 48 h before enrolling in the study. Clinical and demographic information of patients, along with their treatment course and the medication used were evaluated and analyzed using multiple regression analysis. Results A total of 245 patients with a mean age of 49.59 years were included with a mortality rate of 8.16%. The administration of Remdesivir as an anti-viral drug (P value < 0.001) and Tocilizumab as an immunomodulator drug (P value < 0.001) could reduce the hospitalization period in the hospital and the intensive care unit, as well as the mortality rates significantly. Meanwhile, the patients treated with Lopinavir/Ritonavir experienced a lower chance of survival (OR < 1, P value = 0.04). No significant difference was observed between various therapeutic regimens in clinical complications such as bacterial coinfections, cardiovascular and gastrointestinal adverse reactions, and liver or kidney dysfunctions. Conclusion The administration of Remdesivir as an anti-viral and Tocilizumab as an immunomodulatory drug in solid-organ transplant recipients could be promising treatments of choice to manage COVID-19.

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Background:Infant growth is defined as a positive change in body size over a period of time, and is a sensitive predictor of social health. The most effective way to determine child growth is by measuring birth weight and constructing a weight growth trajectory. Many studies were conducted on the effects of different factors on birth weight, but investigations of these effects on growth trajectory are really sparse. This study analyzes longitudinal data to determine factors affecting growth trajectory and used LMS chart for comparing children.Materials and Methods:In a cohort study, 256 neonates born in 2004 in Maku, Iran, were recruited and were followed until 2009.The weight of the neonates were measured at 12 occasions from birth, until the age of 5 years. A growth curve model was used to determine the affecting factors. The parametric LMS method was used to construct the reference centiles curve of the weight (5th, 50th, 95th percentiles).Findings:The findings show that while controlling the other factors, birth region, breast feeding duration, mother’s education and infants’ gender significantly influenced the longitudinal weight rate. However, other variables did not reveal any significant association with growth. The growth charts increased rapidly from birth to 5 years of age for both sexes. It was observed that male children grew faster than females, through the first year of age up to 5 years.Conclusion:Although every child has a growth potential, this capacity could be influenced by various factors and can be compared with other infants through a growth chart. We used longitudinal data to obtain the risk factor of growth trajectory. LMS method was also used for description of growth. Thereafter, the weight chart of Shiraz, southern Iran’s corresponding infants, was compared.

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.