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bsj-4726
Bioremediation Potential of Chlorella vulgaris and Nostoc paludosum on azo Dyes with Analysis of Metabolite Changes
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               Microalgae have been used widely in bioremediation processes to degrade or adsorb toxic dyes. Here, we evaluated the decolorization efficiency of Chlorella vulgaris and Nostoc paludosum against two toxic dyes, crystal violet (CV) and malachite green (MG). Furthermore, the effect of CV and MG dyes on the metabolic profiling of the studied algae has been investigated. The data showed that C. vulgaris was most efficient in decolorization of CV and MG: the highest percentage of decolorization was 93.55% in case of MG, while CV decolorization percentage was 62.98%. N. paludosum decolorized MG dye by 77.6%, and the decolorization percentage of CV was 35.1%. Metabolic profiling of C. vulgaris and N. paludosum were performed using NMR spectroscopy. Based on 1D and 2D NMR data, 43 compounds were identified in the polar extract of C. vulgaris, while 34 polar metabolites were successfully determined in N. paludosum. The identified compounds included carbohydrates, amino acids, organic acids, dipeptides, steroids and phenols. Statistical analysis was carried out to recognize the pattern of metabolite variation between control and dye treated samples. Principal component analysis (PCA) and hierarchical cluster analysis showed that samples treated with MG are clearly separated from the control in both types of algae. Based on heat map data, the level of carbohydrates and amino acids concentrations are strongly affected by bioremediation of MG dye compared with CV dye. In conclusion, the present study proved that CV and MG dyes are considered as stress factors and the studied algae species exert their bioremediation activity without the dyes being absorbed into the cells.

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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Positron Interactions with Some Human Body Organs Using Monte Carlo Probability Method
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In this study, mean free path and positron elastic-inelastic scattering are modeled for the elements hydrogen (H), carbon (C), nitrogen (N), oxygen (O), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K) and iodine (I). Despite the enormous amounts of data required, the Monte Carlo (MC) method was applied, allowing for a very accurate simulation of positron interaction collisions in live cells. Here, the MC simulation of the interaction of positrons was reported with breast, liver, and thyroid at normal incidence angles, with energies ranging from 45 eV to 0.2 MeV. The model provides a straightforward analytic formula for the random sampling of positron scattering. ICRU44 was used to compile the elemental composition data. In this

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Clinical And Diagnostic Research
Thiopurine S-Methyltransferase Polymorphism in Iraqi Paediatric Patients with Acute Lymphoblastic Leukaemia
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Publication Date
Mon Jun 01 2020
Journal Name
Pjmhs Jun
Non-polio Enterovirus Isolation in children with acute Flaccid Paralysis in Iraq
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Aim This study is an overview of NPEV investigated during AFP surveillance programs for the period 2010–2017 in Iraq. Methods Stool samples from 4296 AFP cases and 2933 healthy contacts among children less than 15 years of age were processed for virus isolation as a part of AFP surveillance for the Global Polio Eradication Program in Iraq at National Polio Laboratory. NPEV detection was performed by virus isolation on cell culture according to WHO recommendations. Results The NPEV isolation rate was 14% of total AFP cases and 14.5% of healthy contacts. The infection rate was higher in males than females with a male/female ratio of 1.5: 1. The highest NPEV infection rate was observed among the children aged 1-2 years and decrease significa

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Publication Date
Mon Dec 02 2024
Journal Name
Al-iraqia Journal Of Scientific Engineering Research
Visible Light Communication System Integrating Road Signs with the Vehicle Network Grid
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Publication Date
Sat Dec 01 2018
Journal Name
Annals Of Medicine And Surgery
Plasma d-dimer level correlated with advanced breast carcinoma in female patients
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Publication Date
Sat Dec 13 2014
Journal Name
Environmental Monitoring And Assessment
Estimating the health risks associated with air pollution in Baghdad City, Iraq
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Publication Date
Sat Sep 01 2018
Journal Name
Iraqi Journal Of Science
Polymer optical fiber sensor side-pumped with polymer clad doped lasing compounds
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Publication Date
Sat Dec 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Dynamic Virtual Network Embedding with Latency Constraint in Flex-Grid Optical Networks
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Publication Date
Wed Sep 30 2020
Journal Name
Neuroquantology
Superconducting Compound Hg0.8Sb0.2Ba2Ca2Cu3O8+δ Compared with Hg0.8Sb0.2Ba2Ca1Cu2O6+δ to Evaluate Transition Temperature
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The high temperature superconductor’s compounds are one of the hot spot field of science, due to their applications in industries. Hg0.8Sb0.2Ba2Ca2Cu3O8+δ and Hg0.8Sb0.2Ba2Ca1Cu2O6+δ, were manufactured using a doable-step of solid state reaction method. The samples were sintered at 800 ° C. The transition temperatures Tc are found from electrically resistively by using four probe techniques. The resistivity become zero when the transition temperature Tc(offset) have 131 and 119 K, and the onset temperature Tc(onset) have 139 K for Hg0.8Sb0.2Ba2Ca2Cu3O8+δ and 132 K for Hg0.8Sb0.2Ba2Ca1Cu2O6+δ. Analysis of X-ray diffraction showed a tetragonal structure with lattice parameters changes for all samples.

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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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