The current study was designed for using banana peels to remove zinc, chromium and nickel from industrial waste-water. Three forms of these peels (fresh, dried small pieces and powder) were tested under some environmental factors such as pH, temperature and contact time. Current data show that banana peels are capable of removing zinc, chromium and nickel ions at significant capacity. Furthermore, the powder of banana peels had highest capability in removing all zinc, chromium and nickel ions followed by fresh peels whilst dried peels had the lowest bioremoving capacity again for all metals under test. The highest capacity was for chromium then nickel and finally zinc. All these data were significantly (LSD peel forms = 2.761 mg/l, LSD metal ions = 1.756 mg/l) varied. In case of chromium, these figures were 65.0 ± 1.0 mg/l, 54.0 ± 2.0 mg/l and 41.7 ± 1.5 mg/l for powder, fresh and dried peels respectively. Regarding nickel ions, these data were 56.7 ± 1.5 mg/l for peel powder, 47.7 ± 2.2 mg/l for fresh peel and 47.7 ± 2.2 mg/l for dry peel. While for zinc ions, the biosorption capacity was 51.7 ± 0.8 mg/l, 41.0 ± 1.0 mg/l and 34.7 ± 0.9 mg/l for powder, fresh and dry peels respectively. However, some examined factors were found to have significant impacts upon bioremoval capacity of banana peels such as pH, temperature, and contact time where best biosorption capacity was found at pH 4, at temperature 50 Cº and contact time of 1 hour. It is true that banana peels were varied significantly in case of metal ions and increasing examined factor (pH, temperature. And contact time). Regarding pH, the highest bioremoval ability was found at pH 5 for all heavy metals, but with the sequence of Cr, Ni, and Zn and the data were 59.4 ± 0.83 mg/l, 54.0 ± 0.0 mg/l and 39.1 ± 1.86 mg/l respectively. Similar pattern of bioremoval capacity was detected for temperature which was 50 C º where it was 66.7 ± 2.91 mg/l for chromium, 57.7 ± 1.12 mg/l for nickel and 52.0 ± 1.12 mg/l for zinc. However, in case of contact time, the capacity of biosorbing of these metals was again similar to those of pH and temperature factors where it was found to be 74.0 ± 1.76 mg/l , 66.0 ± 2.25 mg/l and 66.0 ± 1.95 mg/l for Cr, Ni, and Zn respectively but at 1 hour contact time.
Type 2 diabetes mellitus which abbreviate as T2DM is a complex endocrine and metabolic disorder arisingfrom genetic and environmental factors interaction which in turn induce various degrees of insulin functionalalteration on peripheral tissues. Globally, T2DM has develop into a public health problem. Therefore, Thestudy included (75) patients(37 female and 38 males) suffering from T2DM who visit al-kadhimiya teachinghospital with age range 20-80 years and (70) as healthy controls with age range 20-70 years. All studiedgroups were evaluated CMV IgG by ELISA,B. urea, S. Creatinine, cholesterol and triglyceride the resultsshowed that B.urea, S.creatinine and serum cholesterol showed a non-significant differences between studiedgroup,
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Elevated Interleukin-13 (IL-13) may play an important role in the pathophysiology of COVID-19, yet, the attenuated response did not notice across all severe cases. Susceptibility to asthma in specific populations is associated with several SNPs of multifunctional cytokines, such as IL-13, IL-31 and IL-33. This prospective case-control study is designed to investigate the extent of genetic susceptibility in subsets of Iraqi patients with COVID-19 by targeting the variants of interleukin IL-13rs20541 polymorphism in relation to disease susceptibility and severity of clinical presentation. One hundred samples were obtained from the throat, nasopharyngeal and nasal swabs enrolled in this study. Eighty samples of the throat, nasopharyngeal and
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Determining risk indicators for dental implants is an essential strategy for preventing peri-implant diseases and effective diagnosis of dental implant success. To investigate the impact of certain potential factors on the osseointegrated dental implant. Eighty-four individuals were included in our study, 50 cases as a patient’s group and 34 participants as a control group. All cases were diagnosed based on certain criteria, 30 (60%) of patients had peri-implantitis, 20 (40%) with severe periimplantitis, 36(72%) were generalized, and 15 (30%) as localized peri-implantitis cases. The study has indicated that 44.7% of dental implants were in the anterior maxilla, followed by (27.3%) posterior maxilla, (17.4%) posterior mandible, and (10.4%)
... Show MoreThis research includes the synthesis of some new N-Aroyl-N \ -Aryl thiourea derivatives namely: N-benzoyl-N \ -(p-aminophenyl) thiourea (STU1), N-benzoyl-N \ -(thiazole) thiourea (STU2), N-acetyl-N ` -(dibenzyl) thiourea (STU3). The series substituted thiourea derivatives were prepared from reaction of acids with thionyl chloride then treating the resulted with potassium thiocyanate to affored the corresponding N-Aroyl isothiocyanates which direct reaction with primary and secondary aryl amines, The purity of the synthesized compounds were checked by measuring the melting point and Thin Layer Chromatography (TLC) and their structure, were identified by spectral methods [FTIR,1H-NMR and 13C-NMR].These compounds were investigated as a
... Show MoreRetreatment Efficacy of Continuous Rotation Versus Reciprocation Kinematic Movements in Removing Gutta-Percha with Calcium Silicate-Based Sealer: SEM Study, Raghad Noori Nawaf*, Ra
The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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