Significant correlation of angiotensin converting enzyme and glycoprotein IIIa genes polymorphisms with unexplained recurrent pregnancy loss in north of Iran

Background: Spontaneous abortion is considered as the most complex problem during pregnancy. Thrombophilia is resumed as a cause of recurrent pregnancy loss (RPL). Glycoprotein IIIa (GPIIIa) gene is involved in thrombosis and abortion. Angiotensin converting enzyme (ACE) converts angiotensin I to angiotensin II and is involved in thrombosis. The most common polymorphism in this gene is the insertion/deletion (I/D). Objective: In this study, we analyzed the association between ACE I/D and GPIIIa c.98C >T polymorphisms in women with unexplained RPL from the north of Iran. Materials and Methods: Sample population consisted of 100 women with unexplained RPL and 100 controls. The ACE I/D and GPIIIa c.98C>T polymorphisms were genotyped by TETRA-ARMS PCR. The association between genotypes frequency and RPL were analyzed using χP2P and exact fisher tests. Associated risk with double genotype combinations was also investigated by binary logistic regression. Results: There was significant association between ACE DD genotype and RPL (OR=2.04; 95% CI=0.94-4.44; p=0.036). ACE D Allele was also significantly associated with the RPL (OR=1.59; 95% CI=1.05-2.41; p=0.013). No significant association was observed between GPIIIa c.98C>T polymorphism and RPL. Conclusion: ACE I/D polymorphism may probably be a prognostic factor in female family members of women with the history of recurrent abortion. © 2016, Research and Clinical Center for Infertitlity. All Rights Reserved

Optimization for Probabilistic Machine Learning

We have access to great variety of datasets more than any time in the history. Everyday, more data is collected from various natural resources and digital platforms. Great advances in the area of machine learning research in the past few decades have relied strongly on availability of these datasets. However, analyzing them imposes significant challenges that are mainly due to two factors. First, the datasets have complex structures with hidden interdependencies. Second, most of the valuable datasets are high dimensional and are largely scaled. The main goal of a machine learning framework is to design a model that is a valid representative of the observations and develop a learning algorithm to make inference about unobserved or latent data based on the observations. Discovering hidden patterns and inferring latent characteristics in such datasets is one of the greatest challenges in the area of machine learning research. In this dissertation, I will investigate some of the challenges in modeling and algorithm design, and present my research results on how to overcome these obstacles. Analyzing data generally involves two main stages. The first stage is designing a model that is flexible enough to capture complex variation and latent structures in data and is robust enough to generalize well to the unseen data. Designing an expressive and interpretable model is one of crucial objectives in this stage. The second stage involves training learning algorithm on the observed data and measuring the accuracy of model and learning algorithm. This stage usually involves an optimization problem whose objective is to tune the model to the training data and learn the model parameters. Finding global optimal or sufficiently good local optimal solution is one of the main challenges in this step. Probabilistic models are one of the best known models for capturing data generating process and quantifying uncertainties in data using random variables and probability distributions. They are powerful models that are shown to be adaptive and robust and can scale well to large datasets. However, most probabilistic models have a complex structure. Training them could become challenging commonly due to the presence of intractable integrals in the calculation. To remedy this, they require approximate inference strategies that often results in non-convex optimization problems. The optimization part ensures that the model is the best representative of data or data generating process. The non-convexity of an optimization problem take away the general guarantee on finding a global optimal solution. It will be shown later in this dissertation that inference for a significant number of probabilistic models require solving a non-convex optimization problem. One of the well-known methods for approximate inference in probabilistic modeling is variational inference. In the Bayesian setting, the target is to learn the true posterior distribution for model parameters given the observations and prior distributions. The main challenge involves marginalization of all the other variables in the model except for the variable of interest. This high-dimensional integral is generally computationally hard, and for many models there is no known polynomial time algorithm for calculating them exactly. Variational inference deals with finding an approximate posterior distribution for Bayesian models where finding the true posterior distribution is analytically or numerically impossible. It assumes a family of distribution for the estimation, and finds the closest member of that family to the true posterior distribution using a distance measure. For many models though, this technique requires solving a non-convex optimization problem that has no general guarantee on reaching a global optimal solution. This dissertation presents a convex relaxation technique for dealing with hardness of the optimization involved in the inference. The proposed convex relaxation technique is based on semidefinite optimization that has a general applicability to polynomial optimization problem. I will present theoretical foundations and in-depth details of this relaxation in this work. Linear dynamical systems represent the functionality of many real-world physical systems. They can describe the dynamics of a linear time-varying observation which is controlled by a controller unit with quadratic cost function objectives. Designing distributed and decentralized controllers is the goal of many of these systems, which computationally, results in a non-convex optimization problem. In this dissertation, I will further investigate the issues arising in this area and develop a convex relaxation framework to deal with the optimization challenges. Setting the correct number of model parameters is an important aspect for a good probabilistic model. If there are only a few parameters, model may lack capturing all the essential relations and components in the observations while too many parameters may cause significant complications in learning or overfit to the observations. Non-parametric models are suitable techniques to deal with this issue. They allow the model to learn the appropriate number of parameters to describe the data and make predictions. In this dissertation, I will present my work on designing Bayesian non-parametric models as powerful tools for learning representations of data. Moreover, I will describe the algorithm that we derived to efficiently train the model on the observations and learn the number of model parameters. Later in this dissertation, I will present my works on designing probabilistic models in combination with deep learning methods for representing sequential data. Sequential datasets comprise a significant portion of resources in the area of machine learning research. Designing models to capture dependencies in sequential datasets are of great interest and have a wide variety of applications in engineering, medicine and statistics. Recent advances in deep learning research has shown exceptional promises in this area. However, they lack interpretability in their general form. To remedy this, I will present my work on mixing probabilistic models with neural network models that results in better performance and expressiveness of the results

Preventive and Curative Effect of Omega-3 Supplementation on Bone Mineral Density in People Aged 60 Years and Older: A Review Article

Osteoporosis and osteopenia are common worldwide problems leading to potentially life-threatening consequences. Omega-3 supplementation for treating osteoporosis is less studied and less valued by physicians. We aimed to ascertain the appropriate dosage of omega- 3 supplementation to prevent osteoporosis. Google scholar database was searched in May 2017 using the keywords: n-3 fatty acids, omega-3 polyunsaturated fatty acids, essential fatty acids, eicosapentaenoic fatty acids, docosahexaenoic acid, docosapentaenoic acid, alpha-linolenic acid, linoleic acid, osteopenia, osteoporosis, bone density, and fracture. We reviewed English language reports of randomized controlled trials with intake of omega-3 polyunsaturated fatty acids, in which subjects were over 60 years and supplemented with a quantified dosage of omega-3; and outcome was indicated by bone mineral densitometry medical record of fractures and radiological imaging, and serum biomarker to evaluate bone metabolism. We reviewed 110 papers, which only eight articles met our conclusion criteria and concluded with curative effects. Three articles came up with no prophylactic or curative effect of omega-3 supplementation, three articles suggested a dosage of omega-3 supplement that non significantly increased bone mineral densitometry or decreased absorption, and thus, had prophylactic effects. One article just concluded the positive effects, not defining the exact results. It is suggested that a dosage of 4.5 to 6 g/d of eicosapentaenoic acid and docosahexaenoic acid can have curative effects, while 900-1000 mg/d can have prophylactic outcomes. N-3 fatty acids have positive effects on bone density, but to confine definitive dosage and formulation of omega-3 supplementation for reducing the risk of osteoporosis, further investigations are required

COVID -19–Associated Acute Necrotizing Encephalopathy: A Case Report

Abstract COVID-19 is a pandemic disease in which most patients have pulmonary symptoms. However, several cases of CNS involvement associated with COVID-19 have been reported. Acute necrotizing encephalopathy of childhood (ANEC) is a rare CNS complication of viral infections such as influenza, herpes virus, and COVID-19, leading to high mortality and morbidity rates. Several cases of COVID-19-associated acute necrotizing encephalopathy (ANE) have been reported since March 2020 in adults, with just a few cases in pediatrics.This article reports a 5-month-old child who presented with seizures, with the final diagnosis of ANE as a complication of COVID-19.MRI findings of ANEC, as reported in most COVID-19-associated ANEC case reports, involve bilateral, symmetric, multifocal lesions in the central thalami. Moreover, the brainstem, cerebral whitematter, and cerebellum could be affected.The prognosis of COVID-19-associated ANE is poor, leading to neurologic dysfunction or mortality. COVID-19-associated ANE cases must be reported, especially in pediatrics, with detailed clinical history, laboratory data, and radiologic findings to introduce diagnostic criteria, prognosis, and a management protocol

Rural areas of the Central County of Rostam Township

Culture is the most important issue affecting the development of human societies. The term of cultural development is common in recent years and can give an end to the conflict between tradition and modernity, between places and habitat. The purpose of this study is to identify and analyze the development process in rural areas after culture. Central County of Rostam Township can be remarkable in terms of great capabilities, especially in agriculture and patient talent strategic location and it’s potential. Population statistics in this study included 53 villages of central county of Rostam township. Method used in this study, is descriptive method – analysis. Method of data collection is library – documents and field studies too and for data analysis software SPSS and Arc GIS and in analysis and assumptions to achieve the result of “Morris inharmonious index”. The results showed that there is a significant relationship between the level of villages having a population conditions and cultural facilities. Between planning and social conditions of cultural geography found no significant relationship. Also, there was no significant relationship between social and geographical factors and cultural planning

Cholecysto-hepatic duct serving as the only drainage pathway of bile from the intrahepatic to the extrahepatic biliary system in an infant: a case report

Abstract Background Cholecystohepatic duct is a rare anomaly of the biliary system which involves drainage of bile into the gallbladder which may be associated with agenesis of the common hepatic duct or common bile duct. Case presentation A 2.5-month-old infant presented to our emergency department with icterus. He had a history of esophageal atresia and imperforate anus which had been treated surgically by thoracotomy, esophagostomy, gastrostomy and colostomy placement. Following imaging studies by ultrasound and MRCP, the diagnosis of common hepatic duct agenesis was made. Cholecystohepatic duct was present as the solitary drainage pathway of bile from the intrahepatic to extrahepatic biliary system. Conclusions Cholecystohepatic ducts need a high index of suspicion to be diagnosed on preoperative hepatobiliary imaging. As they may be asymptomatic, they are predisposed to iatrogenic injury during hepatobiliary surgeries

Significant correlation of angiotensin converting enzyme and glycoprotein IIIa genes polymorphisms with unexplained recurrent pregnancy loss in north of Iran

Background: Spontaneous abortion is considered as the most complex problem during pregnancy. Thrombophilia is resumed as a cause of recurrent pregnancy loss (RPL). Glycoprotein IIIa (GPIIIa) gene is involved in thrombosis and abortion. Angiotensin converting enzyme (ACE) converts angiotensin I to angiotensin II and is involved in thrombosis. The most common polymorphism in this gene is the insertion/deletion (I/D). Objective: In this study, we analyzed the association between ACE I/D and GPIIIa c.98C >T polymorphisms in women with unexplained RPL from the north of Iran. Materials and Methods: Sample population consisted of 100 women with unexplained RPL and 100 controls. The ACE I/D and GPIIIa c.98C>T polymorphisms were genotyped by TETRA-ARMS PCR. The association between genotypes frequency and RPL were analyzed using χP2P and exact fisher tests. Associated risk with double genotype combinations was also investigated by binary logistic regression. Results: There was significant association between ACE DD genotype and RPL (OR=2.04; 95% CI=0.94-4.44; p=0.036). ACE D Allele was also significantly associated with the RPL (OR=1.59; 95% CI=1.05-2.41; p=0.013). No significant association was observed between GPIIIa c.98C>T polymorphism and RPL. Conclusion: ACE I/D polymorphism may probably be a prognostic factor in female family members of women with the history of recurrent abortio

Delivery of the second twin: influence of presentation on neonatal outcome, a case controlled study

Abstract Background Spontaneous vaginal twin delivery after 32nd week of gestation is safe when first twin presenting cephalic. Aim of this study is to identify obstetric factors influencing the condition of second twin and to verify whether non-cephalic presentation and vaginal breech delivery of the second twin is safe. Methods This is a retrospective case controlled cohort study of 717 uncomplicated twin deliveries ≥32 + 0 weeks of gestation from 2005 to 2014 in two tertiary perinatal centers. Obstetric parameters were evaluated in three groups with descriptive, univariate logistic regression analysis for perinatal outcome of second twins. Results The three groups included twins delivered by elective cesarean section ECS (n = 277, 38.6%), by unplanned cesarean section UPC (n = 233, 32.5%) and vaginally (n = 207, 28.9%). Serious adverse fetal outcome is rare and we found no differences between the groups. Second twins after ECS had significant better umbilical artery UA pH (p < 0.001) and better Apgar compared to UPC (p = 0.002). Variables for a fetal population “at risk” for adverse neonatal outcome after vaginal delivery (UA pH < 7.20, Apgar 5´ < 9) were associated with higher gestational age (p = 0.001), longer twin-twin interval (p = 0.05) and vacuum extraction of twin A (p = 0.04). Non-cephalic presentation of second twins was not associated (UA pH < 7.20 OR 1.97, CI 95% 0.93–4.22, p = 0.07, Apgar 5´ < 9 OR 1.63, CI 95% 0.70–3.77, p = 0.25, Transfer to neonatal intermediate care unit p = 0.48). Twenty-one second twins (2,9%) were delivered by cesarean section following vaginal delivery of the first twin. Even though non-cephalic presentation was overrepresented in this subgroup, outcome variables were not significantly different compared to cephalic presentation. Conclusions Even though elective cesarean means reduced stress for second twins this seems not to be clinically relevant. Non-cephalic presentation of the second twin does not significantly influence the perinatal outcome of the second twin but might be a risk factor for vaginal-cesarean birth