Diversity and relationship between Iranian ethnic groups: Human dopamine transporter gene (DAT1) VNTR genotyping

The 40-bp VNTR polymorphism in the 3′ untranslated region of the human DAT1 (dopamine transporter 1) was analyzed in the Iranian ethnic groups in order to examine the influence of geographical and linguistic affiliation on the genetic affinities among the Iranian population. A total of 449 subjects belonging to nine ethnic groups from the Iranian population were included in the study. The screening of 898 chromosomes showed five alleles (6, 7, 8, 9, 10, and 11), of which allele 10 revealed the highest frequency in most regions. Allele 8 was predominant in one ethnicity and occurred more frequently in the center of Iran. This study shows that the DAT1 distribution in Iran has a different pattern from those in other studies, which can contribute to an understanding of differentiation and diversity of Iranian ethnic groups. This polymorphism could represent the genetic diversity among the various ethnic groups of Iran. © 2007 Wiley-Liss, Inc

The cientificWorldJOURNAL Research Article Beta-Thalassemia in Iran: New Insight into the Role of Genetic Admixture and Migration

Iran with an area of 1.648 million km 2 is located between the Caspian Sea and the Persian Gulf. The Iranian population consists of multiethnic groups that have been influenced by various invasions and migration throughout history. Studies have revealed the presence of more than 47 different β-globin gene mutations responsible for β-Thalassemia in Iran. This paper is an attempt to study the origin of β-Thalassemia mutations in different parts of Iran. Distribution of β-Thalassemia mutations in Iran shows different patterns in different areas. β-Thalassemia mutations have been a reflection of people and area in correlation with migration and origin of ancestors. We compared the frequencies of β-globin mutations in different regions of Iran with those derived from neighboring countries. The analysis provided evidence of complementary information about the genetic admixture and migration of some mutations, as well as the remarkable genetic classification of the Iranian people and ethnic groups

Generation and bioenergetic analysis of cybrids containing mitochondrial DNA from mouse skeletal muscle during aging

Mitochondrial respiratory chain defects have been associated with various diseases and normal aging, particularly in tissues with high energy demands including skeletal muscle. Muscle-specific mitochondrial DNA (mtDNA) mutations have also been reported to accumulate with aging. Our understanding of the molecular processes mediating altered mitochondrial gene expression to dysfunction associated with mtDNA mutations in muscle would be greatly enhanced by our ability to transfer muscle mtDNA to established cell lines. Here, we report the successful generation of mouse cybrids carrying skeletal muscle mtDNA. Using this novel approach, we performed bioenergetic analysis of cells bearing mtDNA derived from young and old mouse skeletal muscles. A significant decrease in oxidative phosphorylation coupling and regulation capacity has been observed with cybrids carrying mtDNA from skeletal muscle of old mice. Our results also revealed decrease growth capacity and cell viability associated with the mtDNA derived from muscle of old mice. These findings indicate that a decline in mitochondrial function associated with compromised mtDNA quality during aging leads to a decrease in both the capacity and regulation of oxidative phosphorylation

Metabolomics Study of the Diagnosis and Prognosis of Severe Traumatic Brain Injury (sTBI)

Traumatic brain injury (TBI) is defined as neurologic injury resulting from an external mechanical force, which is associated with long-term neurological and cognitive disability and affects individuals of all ages, ethnicities, and socioeconomic characteristics. TBI severe enough to cause hospitalization occurs in over 10 million people each year worldwide. TBI is the most common cause of death and long-term disability in children and young adults in developed countries. Currently, clinical assessment and neuroimaging (e.g. CT and MRI) are the most reliable techniques used for the diagnosis and prognosis of TBI. Unfortunately, this approach has considerable shortcomings when considering sensitivity and specificity of TBI diagnosis and prognosis. Disease stratification and the prediction of outcomes and are key problems for the management of severe TBI (sTBI).This study showed that metabolomics can be applied for the diagnosis and prognosis of sTBI using nuclear magnetic resonance (NMR) spectroscopy and direct infusion tandem mass spectrometry (DI-MS/MS). The results are promising for the diagnose sTBI when compared to orthopedic injury (OI) controls and for the prognostication sTBI outcomes at 3, 6, and 12-months post-injury particularly in predicting poor outcomes from the good outcome. We also investigated the metabolomics on animal models for sTBI. Using the cortical controlled impact (CCI) mouse model of sTBI demonstrated a difference in metabolic profiles of CCI and CCI mice receiving a plastic CAP to cover the skull in the craniotome area (CCI+CAP) mice when compared to sham controls. Our result revealed that the highest degree of metabolic alterations occurs at 8 hours post-injury and that the CCI+CAP mice have a prolonged brain injury compared to CCI mice (assessed at 7 days post-injury)

Plasma Metabolomics of Community Acquired Pneumonia (CAP) For Prognosis of Patients at Highest Risk of Mortality

Community acquired pneumonia (CAP) is an acute disease with clinical features of lower respiratory tract infections. Early and accurate prognoses, and effective treatment, where possible, in the management of CAP are essential to decrease mortality. Metabolomic analysis is defined as the comprehensive study of low molecular-weight metabolites present in biological samples that enable the qualitative and/or quantitative measurement and analysis of multiple metabolites We showed the feasibility of metabolomics using nuclear magnetic resonance (NMR) spectroscopy, gas chromatography mass spectrometry (GC-MS) and direct infusion tandem mass spectrometry (DIMS/MS) as most common analytical platforms in association with multivariate statistical analysis to separate non-survivors and survivor of bacteria CAP and H1N1 pneumonia patients for prognosis of mortality. In conclusion, this study may not only be instrumental in predicting the mortality of CAP patients within 24 hours of hospitalization, but may also allow for the rapid identification of the infective organism in CAP.18 month

Metabolomics: Applications and Promise in Mycobacterial Disease

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors

Beta-Thalassemia in Iran: New Insight into the Role of Genetic Admixture and Migration

Iran with an area of 1.648 million km2 is located between the Caspian Sea and the Persian Gulf. The Iranian population consists of multiethnic groups that have been influenced by various invasions and migration throughout history. Studies have revealed the presence of more than 47 different β-globin gene mutations responsible for β-Thalassemia in Iran. This paper is an attempt to study the origin of β-Thalassemia mutations in different parts of Iran. Distribution of β-Thalassemia mutations in Iran shows different patterns in different areas. β-Thalassemia mutations have been a reflection of people and area in correlation with migration and origin of ancestors. We compared the frequencies of β-globin mutations in different regions of Iran with those derived from neighboring countries. The analysis provided evidence of complementary information about the genetic admixture and migration of some mutations, as well as the remarkable genetic classification of the Iranian people and ethnic groups