Glycogen storage DISEASES are a group of inborn error of metabolism and characterized by accumulation of glycogen in various tissues. The overall incidence of glycogen storage DISEASES is estimated 1 per 20, 000-43, 000 live births. There are twelve distinct DISEASES that are commonly considered to be glycogen storage DISEASES and classified based on enzyme deficiency and affected tissue.3TWe searched all review articles and books in the national and international databases which considered as inherited metabolic disorders and the GENETIC associations of these disorders.3TA large number of enzymes intervene in the synthesis and degradation of glycogen which is regulated by hormones. Several hormones, including insulin, glucagon and cortisol regulate the relationship between glycolysis, glycogenosis, and glycogen synthesis.3TThese DISEASES are divided into three major groups: disorders that affected liver, disorders that affected muscle and those which are generalized. Glycogen storage DISEASES are called by a Roman numerical that reflects the historical sequence of their discovery by an enzyme defect or by the author's name of the first description.

Complex inherited DISEASES affected by an interaction between collective effects of the genotype at one or multiple loci either to increase or to lower susceptibility to disease, combined with a variety of environmental exposures that may trigger, accelerate, exacerbate, or protect against the disease process. The new aspects of GENETIC techniques have been opened for diagnosis and analysis of inherited disorders. While appropriate Mendelian laws is applied to estimate the recurrence risk of single gene DISEASES, using empirical recurrence risks are the most important and available method to evaluate pedigree of complex (multifactorial), chromosomal, and unknown etiology disorders. Although, generally, empirical recurrent risks are not accurate, either because of the difference of gene frequencies and environmental factors among populations or heterogeneity of disease; using results of plenty family population studies, computerized estimating programs, genotyping technologies, and Genome-wide association studies (GWASs) of single nucleotide polymorphisms (SNPs), can make it possible nowadays to estimate these risks. The specific family situation and importance recurrence risks of some common complex GENETIC DISEASES will be presented in this review and some important multifactorial disorders’ recurrence risks will be summarized to help GENETIC counselors for supporting families and representing better view of GENETIC disorders.

Consanguineous marriages have been practiced for thousands of years in many communities throughout the world. Birth defects contribute significantly to infant mortality rate (IMR), and they may be associated with consanguinity. Therefore, the objective of this review is to compile an overview of the findings on consanguinity and GENETIC DISEASES in the population of Brazil. We collect data from PubMed (January 2000 to July 2020), and data from the Modell Global Database of Congenital Disorders (MGDb) estimates for 2010-2014 in the Americas. The PubMed database yielded 199 results that met the inclusion/ exclusion criteria search. We observe a higher risk ratio (RR: 4. 16, 95% CI 4. 07-4. 25) for consanguineous marriages compared with two other Brazilian studies. The highest coefficient of consanguinity was found in the city of Lagoa (F = 0. 01182), state of Paraí, ba. In the same period (2010-2014), the coefficient of consanguinity was 0. 0027, while the IMR was 20. 3/1, 000 live births in Brazil. The Kruskal-Wallis test used to compare Brazil and others country’, s estimates was also statistically significant (H = 73. 55, p<0. 0001). Fifteen GENETIC DISEASES associated with consanguinity have been observed. Among these, a new mutation in pycnodysostosis disease (#265800, CTSK gene, c. 953G>A), and Raine syndrome (#259775, FAM20C, c. 1487C>T,p. P496L) are the DISEASES that stand out. With these results, we believe that the public health system should be working directly with the local communities in actions that include the creation of banks of genes/mutations related to consanguineous couples, neonatal screening and health education.

Background: Preimplantation GENETIC diagnosis - PGD is currently an established procedure allowing GENETIC research of oocyte or embryo before implantation to the uterus. Spinal muscular atrophy (SMA) is a neurodegenerative disorder, being the second most common lethal autosomal recessive disease in Caucasians, after cystic fibrosis. There are three clinically different types of which type I (Werding-Hoffman Disease) is the most severe form. Around 98% of clinical cases of SMA are caused by the homozygous absence of a region of exons 7 and 8 of the telomeric copy of the SMN gene (SMN1) on chromosome 5 (5q13.3). We performed PGD for SMA in 5 couples who had already given birth to a child affected with SMA.Materials and Methods: All patients underwent standard IVF procedures associated with intracytoplasmic sperm injection. 6-8 cell embryos were biopsied on day 3 of culture. Single cell nested PCR-RFLP protocol for PGD of SMA was used for detection of mutation.Results: In course of IVF-PGD procedures all patients were performed transfer of embryos without SMN1 deletions. Four of five couples delivered healthy babies.Conclusion: Preimplantation GENETIC diagnosis (PGD) of monogenic disorders is very efficient method, especially for patients when a previous child is homozygous for GENETIC disorders. It opens a new horizon of treatment for GENETIC disease carriers.

Introduction: Mobile personal health is a rapidly growing area of health information technology. Mobile personal health users are able to manage their own health data and communicate with doctors in order to improve healthcare quality and efficiency. In recent years, information and communication technologies improvements, along with mobile Internet, offering anywhere and anytime connectivity, play a key role on modern healthcare solutions. Moreover, data on GENETIC DISEASES are no exception to this set of data. Therefore, appropriate algorithm and methods of data analysis should be designed.Methods: The main objective of this research is to investigate the effective factors on more efficiency of medical data. In this article, in addition to analyzing and evaluating the best data mining algorithms used in the medical field, a new combined approach has been provided in order to predict the risk of transmitting GENETIC DISEASES. A questionnaire was developed for this task, based on the rigorous study of scientific literature concerning pregnancy and applications available on the market, with 12 data items. The data items contain calendars, GENETIC DISEASES and cardiovascular DISEASES information, health habits, counters, diaries, mobile features, security, backup, configuration and architectural design.Results: Health telematics is a growing issue that is becoming a major improvement on patient lives, especially in elderly, disabled, and chronically ill.The results of the patients clustering were obtained using the risk of transmitting GENETIC DISEASES and according to the criteria of similarity in the ways of transmission as well as using a decision tree to predict whether the individual with the related characteristics has the likelihood to transmit the disease or not.300 participants were recruited, 92% routinely used and 91% owned a mobile phone.99% were willing to receive mobile health (m-health) advice, and 79% favored mobile medication reminders.65.2% would send home recorded information on their blood pressure, weight, medication use and lifestyle to a doctor.81.9% trusted the confidentiality of m-health data, while 77.1% had no concerns about the privacy of their information.Conclusion: M-health system proposes healthcare delivery anytime and anywhere, overcoming geographical, temporal, and even organizational barriers with low and affordable costs. This study reviewed the state-of-the-art on mhealth system and technologies.