INVESTIGATION OF ALUMINUM TOXICITY AMONG WORKERS IN ALUMINUM INDUSTRY SECTOR

The study was conducted to evaluate urine aluminum concentration among a total of 150 participants (80 aluminum technicians and 70 non- aluminum technicians as a control). Data were collected through a previously prepared questionnaire which consists of two parts. The first part concerned with demographic data such as age and nationality. The second part concerned with occupational data such as working hours, working years, smoking, and diseases. The mean concentration of aluminum is 51.62+ 29.59 μg/l and the mean concentration of group control 16.32 + 12.49 μg/l. The following variables were associated significantly with aluminum concentration: age, weekly working hours, smoking and daily smoking packets.According to our study, aluminum workers have high concentrations of urine aluminum compared with other studies, in addition to that the incidence of diseases in relation to exposure is low, simply because: 1-Self reported questionnaires may be not a proper way to collect data about diseases. 2- Traditional surveillance approaches used in public health practice are difficult to apply to metals poisoning because adverse health effects related to metal exposure may not be clinically diagnosed, except at very high exposure levels, and are not usually listed as reportable diseases.Finally Special safety precautions and educational programs are also needed to limit the aluminum exposure in this industrial group

Solving Cell Placement Problem Using Harmony Search Algorithms

Cell placement is a phase in the chip design process, in which cells are assigned to physical locations. A placement algorithm is a way that satisfies the objectives and minimizes the total area while keeping enough space for routing. Cell placement is an NP-complete problem of very large size. In order to solve this problem, diversified heuristic algorithms are used. In this work, a new algorithm is proposed based on the harmony search algorithm. The harmony search algorithm mimics music improvisation process to find the optimal solution. Cell placement problem has many constraints, so in this work, the harmony search algorithm is modified to adapt to these constraints. Experiment results show that this algorithm is efficient for solving cell placement and is characterized by good performance, solution quality and likelihood of optimality

THE APPROACH TO THE ANALYSIS OF ELECTRICAL FIELD DISTRIBUTION IN THE SETUP OF PAPER INSULATED ELECTRODES IN OIL

Article presents the problem of the approach to the analysis of electrical field distribution in the model insulating system, which, in the author's experimental research, was used to the assessment of the influence of paper insulation on the mechanism of electrical discharge initiation in mineral oil. The main assumptions of the planned numerical works based on the finite element method were described and scientific aim of the numerical analysis were characterized in this paper. Both the assumptions and the scientific aim were related to the conclusions from the experimental works, especially to the measured times to initiation of the discharges developing in mineral transformer oil, indicating on the important role of the oil quality in the process of discharge initiation in the system of the insulated by paper electrodes immersed in oil

The Chemical Speciation of Trace-Metals in Street Dusts of Irbid, Jordan

Abstract Street dust samples were collected from different locations in Irbid city, Jordan. The concentrations of Pb, Cu, Zn, Cd, Ni, Mn, Cr and Al in these samples were determined usin

ADAMTS19-associated heart valve defects: Novel genetic variants consolidating a recognizable cardiac phenotype

Recently, ADAMTS19 was identified as a novel causative gene for autosomal recessive heart valve disease (HVD), affecting mainly the aortic and pulmonary valves. Exome sequencing and data repository (CentoMD) analyses were performed to identify patients with ADAMTS19 variants (two families). A third family was recognized based on cardiac phenotypic similarities and SNP array homozygosity. Three novel loss of function (LoF) variants were identified in six patients from three families. Clinically, all patients presented anomalies of the aortic/pulmonary valves, which included thickening of valve leaflets, stenosis and insufficiency. Three patients had (recurrent) subaortic membrane, suggesting that ADAMTS19 is the first gene identified related to discrete subaortic stenosis. One case presented a bi-commissural pulmonary valve. All patients displayed some degree of atrioventricular valve insufficiency. Other cardiac anomalies included atrial/ventricular septal defects, persistent ductus arteriosus, and mild dilated ascending aorta. Our findings confirm that biallelic LoF variants in ADAMTS19 are causative of a specific and recognizable cardiac phenotype. We recommend considering ADAMTS19 genetic testing in all patients with multiple semilunar valve abnormalities, particularly in the presence of subaortic membrane. ADAMTS19 screening in patients with semilunar valve abnormalities is needed to estimate the frequency of the HVD related phenotype, which might be not so rare

TGFBR1 Variants Can Associate with Non-Syndromic Congenital Heart Disease without Aortopathy

BACKGROUND: Congenital heart diseases (CHD) are the most common congenital malformations in newborns and remain the leading cause of mortality among infants under one year old. Molecular diagnosis is crucial to evaluate the recurrence risk and to address future prenatal diagnosis. Here, we describe two families with various forms of inherited non-syndromic CHD and the genetic work-up and resultant findings. METHODS: Next-generation sequencing (NGS) was employed in both families to uncover the genetic cause. In addition, we performed functional analysis to investigate the consequences of the identified variants in vitro. RESULTS: NGS identified possible causative variants in both families in the protein kinase domain of the TGFBR1 gene. These variants occurred on the same amino acid, but resulted in differently substituted amino acids (p.R398C/p.R398H). Both variants co-segregate with the disease, are extremely rare or unique, and occur in an evolutionary highly conserved domain of the protein. Furthermore, both variants demonstrated a significantly altered TGFBR1-smad signaling activity. Clinical investigation revealed that none of the carriers had (signs of) aortopathy. CONCLUSION: In conclusion, we describe two families, with various forms of inherited non-syndromic CHD without aortopathies, associated with unique/rare variants in TGFBR1 that display altered TGF-beta signaling. These findings highlight involvement of TGFBR1 in CHD, and warrant consideration of potential causative TGFBR1 variants also in CHD patients without aortopathies

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)