Iraqi Journal of Science

New 7-ethyl-4-methyl coumarin derivatives bearing an azo methene group (C=N) were synthesized by a four-step process. The reaction between 7-ethylphenol and ethyl acetoacetate in the presence of sulfuric acid afforded 7-ethyl-4-methylcoumarin 1 in 90% yield. Nitration conditions (nitric acid and sulfuric acid) were then applied to product 1 to produce 6-nitro-7-ethyl-4-methycoumarin 2 and 8-nitro-4-methyl-7-ethylcoumarin 3. The temperature, the amount of starting materials, and the time of reaction were essential factors in obtaining a high yield of isomer 2 compared to the second isomer 3. The nitro groups at compounds 2 and 3 were then reduced with iron metal in an acidic medium to form the corresponding amino compounds 4 and 5, which were converted to Schiff base derivatives 6-17 via reaction with different substituted aromatic aldehydes. The synthesized compounds were characterized by FT-IR, 1H NMR, and 13C NMR spectroscopy. Evaluating the biological activities of the synthetic compounds was carried out against two bacteria: gram-positive bacteria (Staphylococcus aureus) and gram-negative bacteria (Escherichia coli) at 1×10-3 M; compound 15 showed a broad spectrum against these two types of bacteria compared to Vancomycin and Amikacin and against fungi compared to nystatin as a standard drug, but compound 7 showed stronger activities against fungi than the other synthesized compounds. In contrast, the rest of the compounds showed moderate activity against fungi compared with the same standard drag; in the antioxidant study, some new compounds showed powerful antioxidants compared with ascorbic acid as a stander reference by calculating the IC50. Compound 8 showed very good results as an antioxidant agent compared with the same standard.

A low-cost, effective green porous composite was prepared and used for the sorption of Zn+2 ions from aqueous solutions. The effect of contact time, sorbent dosage, initial ion concentration, solution pH, and temperature on the sorption of Zn+2 ions was investigated. The results showed higher removal efficiency of Zn+2 ions (83%), which was achieved at a contact time of 120 minutes, a normal pH of 6.4, and a temperature of 313 K. The Langmuir isotherm model successfully described the experimental results of the sorption process with a theoretical maximum sorption capacity of 4.819 mg/g. The kinetics of sorption showed that the pseudo-second-order model fitted the experimental results better than the pseudo-first-order model. The intra-particle diffusion kinetics indicated that the boundary film was the rate-determining step of the sorption process. The thermodynamic parameters showed that the sorption of Zn+2 ions was spontaneous and endothermic under the studied conditions. The solidification of spent composite by kaolin was able to keep the leachate cations at an acceptable level in the solution after 12 weeks of exposure to a salty medium.

In this work, the preparation of some new oxazolidine and thiazolidine derivatives has been conducted. This was done over two steps; the first step included the synthesis of Schiff bases A1-A5 in 72-88% yields by the condensation of isonicotinic acid hydrazide and aldehydes. The second step includes the cyclization of derivatives A1-A5 with glycolic acid and thioglycolic acid to obtain the desired products, oxazolidine derivatives B1-B5 (44-60% yields) and thiazolidine derivatives C1-C5 (41-61% yields), respectively. The structure of the prepared compounds was characterized using FT-IR, 1H NMR, and 13C NMR spectroscopy. Some of the produced compounds were tested for antioxidant properties.

Ethyl acetate was used to extract bioactive compounds from the fruits of the Iraqi sumac plant Rhus coriaria. This sumac fruit extract was then utilized as a natural reducing agent to convert graphene oxide into reduced graphene oxide (rGO) nanosheets through a green synthesis method. Gas chromatography-mass spectrometry (GC-MS) analysis of the ethyl acetate sumac fruit extract identified phytochemicals that likely facilitated the biosynthesis of the rGO nanosheets. The biosynthesized EERCF-rGO was characterized by using UV-Vis 234 nm. FTIR spectrum was used to know functional groups of EERCF-rGO, XRD spectra of EERCF-rGO displayed sized at 26.91 nm, Raman spectra of EERCF-rGO showed D peak at (1335 cm-1) and a G peak at (1592 cm-1). The morphology of EERCF-rGO was determined using FESEM, which offers plates shape. Also, the TEM of EERCF-rGO showed a size at 12 nm. The biosynthesized of EERCF-rGO appeared to have anticancer activity in vitro cytotoxicity by MTT assay, IC50 is (189.67 μg/mL) against human breast cancer (MCF-7) cell line. The use of biological extracts as reducing agents to produce reduced graphene oxide is an inexpensive, nontoxic, and environmentally friendly approach. The reduced graphene oxide biosynthesized using extracts from the fruits of the Iraqi Rhus coriaria plant demonstrated anticancer properties. This method of green synthesis and the resulting anticancer biomaterial show potential for further development.

Diabetes mellitus is a disease characterized by chronic high blood sugar levels resulting from defects in insulin secretion, insulin action, or both. Prolonged high blood glucose can increase production of reactive oxygen species, leading to elevated oxidative stress. The enzyme paraoxonase-1 (PON1), made in the liver and transported on high-density lipoprotein (HDL), acts as an antioxidant and can counter oxidative stress. This study aimed to compare PON1 activity between healthy controls, obese type 2 diabetics, and non-obese type 2 diabetics. There were 30 healthy controls, 30 obese type 2 diabetics (group I), and 30 non-obese type. Additionally, fasting lipid profiles, fasting plasma glucose, uric acid, albumin, and total bilirubin levels were determined using a clinical chemistry analyzer. The goal was to evaluate differences in PON1 activity as well as metabolic parameters between the three study groups. The results In comparison to the healthy control group, the diabetes group had Significantly higher blood sugar, serum cholesterol, serum triglycerides, serum LDL, and serum uric acid levels, Compared to the healthy control group, PON was Significantly lower in group II patients, Conclusion that Obese type 2 DM patients had significantly lower PON1 and HDL-C levels, which may indicate That the overweight has lessened biochemical functions for these substances. As dyslipidemia, insulin resistance, and high blood pressure are thought to be crucial factors in the cause of metabolic conditions in obese people, the reduced paraoxonase level may increase their likelihood of developing these conditions.

Hypothyroidism, a condition where the thyroid gland does not produce enough thyroid hormone to maintain health, is most often caused by iodine deficiency in areas where iodine levels are low. However, in regions with adequate iodine, Hashimoto\'s disease is the leading cause of hypothyroidism and thyroid failure. Neudesin in neuropeptide hormone, which is released by the brain and fat tissue, has both metabolic effects and neural. The study included ninety participants divided in two groups: in this study, the initial group comprised 30 female and 30 male individuals diagnosed with hypothyroidism, while the second group consisted of 15 female and 15 male participants who were healthy controls. The age range for both groups spanned from 30 to 60 years. The findings indicated that the hypothyroidism group exhibited significantly elevated levels of WHR, BMI, TC, TG, LDL, VLDL, TSH, and Neudesin compared to the control group. Particularly noteworthy, Neudesin displayed remarkable proficiency in distinguishing hypothyroidism patients from the control group, achieving an outstanding AUC 0.965. Using the findings, we deduced that the evaluation. Neudesin indicators may be beneficial for the diagnosis and response to therapy. The most sensitive and specific marker for hypothyroidism individuals is neudesin.

This study focused on the eco-friendly and economical synthesis of copper sulfide nanoparticles at certain temperatures using myrtle leaf extract. Green synthesis methods are mostly considered to be safe and efficient in the nanoparticle synthesis field. The nanoparticles were characterized using FT-IR, AFM, UV-visible spectrophotometer, FESEM with EDX, and XRD. where the crystal size in XRD was 21.4 nm. The antibacterial properties of CuS nanoparticles were evaluated against two bacterial strains, namely, Staphylococcus aureus and Escherichia coli, using various concentrations. The Minimum Inhibitory Concentration (MIC) was determined for both bacteria at a range of CuS NPs conc. from 0.5229 × 10-1 M to 0.3268 × 10-2 M.

This study evaluated the corrosion inhibiting properties of natural tannins extracted from gray Avicennia marina bark and Cordia myxa bark for carbon steel alloys. Such alloys are widely used in shipbuilding construction where corrosion poses challenges. Tannin extracts were tested as green inhibitors across varying concentrations in 0.5 M hydrochloric acid solution, a corrosive medium. Trials were conducted at different temperature parameters to determine the inhibition efficiencies. The amount of hydrogen gas released over time was quantified to compare corrosion rates in the absence and presence of tannin inhibitors. The Langmuir and Freundlich isotherm models for adsorption were also studied, as well as the kinetic and thermodynamic functions, where the change in free energy and activation energy were calculated. The results of the study showed a promising possibility to use the compounds under study as corrosion inhibitors for carbon steel with high efficiency up to more than 83% at a temperature of 25 degrees Celsius. The results showed an increase in the free energy change (ΔGcorro) values for the corrosion reaction in the presence of the inhibitors under study compared to corrosion in the absence of inhibitors, The free energy change (ΔGcorro) values at 25 degrees Celsius were (184.4538, 184.9246, 111.9616) kJ.mol-1 in the presence of the Avicennia marina inhibitor, Cordia myxa inhibitor and blank) consecutively. As a result, the speed of the corrosion reaction will decrease in the presence of inhibitors.

During the COVID-19 epidemic, improperly discarded face masks turned out to be a severe source of environmental contamination that threatened lives. Bacteria are a crucial degrading agent for face masks. The objective of this study was to isolate and characterize potential face mask-degrading bacteria from discarded face masks at Parangtritis Beach in Yogyakarta, Java, Indonesia. Mineral Salt Media (MSM) containing face mask (black duckbill, blue surgical and white KF94) was used to select bacterial growth ability. The process of face mask degradation was also assisted by scanning electron microscopy (SEM) examination. Eight bacterial strains, designated AP1 through AP8, were obtained using an enrichment screening technique. AP1-AP3, AP4-AP5, and AP6-AP8 were identified phenotypically as Bacillus sp., Pseudomonas sp. and Staphylococcus sp. respectively. Bacillus sp. (AP1) displayed the highest growth (OD600 0.78) on all test conditions among the eight isolates. Moreover, this strain could be grown on MSM-face mask media with a pH range of 5 - 9; temperature 5 – 50°C, and NaCl concentration of 2.5 - 7.5%. The SEM study revealed morphological alterations in the face masks inner, middle and outer layers after interaction with Bacillus sp. This research proved that Bacillus strains could be viable candidates for face mask biodegradation.

The emergence of antimicrobial-resistant ESBL-producing Escherichia coli and Klebsiella pneumoniae among community-acquired uropathogens represents an important therapeutic challenge. Henceforth, the purpose of the study was to investigate the antibiotic susceptibility patterns of ESBL-producing Escherichia coli and Klebsiella pneumoniae among community-acquired urinary tract infections and then identify the risk factors. Escherichia coli and Klebsiella pneumoniae strains were isolated from patients with symptomatic community-acquired urinary tract infections. The ESBL-producing and antimicrobial susceptibility tests were then performed using the broth microdilution technique. Among 826 Escherichia coli and Klebsiella pneumoniae uropathogens, 17.43% were found to be ESBL producers. According to logistic regression analysis, the risk factors for ESBL-producing community-acquired urinary tract infections are urinary tract infections in the preceding 6 months and recurrent urinary tract infections. All ESBL producers were sensitive to meropenem, followed by imipenem (98.61%), amikacin sulfate (90.28%), and nitrofurantoin (80.56%). Significant ESBL-positive Escherichia coli and Klebsiella pneumoniae were identified which were found to be associated with recurrent UTIs. Meropenem was effective against both ESBL-positive and -negative bacteria.

Nanotechnology is an emerging technology that has led to the nanomedicine development, which involves nanoparticles that exist in the natural world. Nanoparticle is an ultrafine unit with dimensions measured in nanometers. They are created as a result of human activities, and have recently been used in various biomedical applications, consumer products, and commercially, etc. Magnesium oxide nanoparticles (MgO NPs), among the known metal oxides, have attracted a vast scientific interest. Therefore, human health can be at risk of continuous exposure. However, the toxic effects of MgO NPs should be assessed with their increased applications. The present study aimed to investigate the physiological and histological, alterations induced by the MgO nanoparticles in hepatocytes of male rats through oral route with (250, and 1000) mg/kg for (14, 28, and 56) days. Fifty-four male mature rats, (2.5 - 3 months old) were divided randomly into nine groups of six rats each, gavaged with MgO NPs (which were purchased from US Research Nanomaterials, Inc). The results obtained revealed highly significant increase (p<0.01) in aspartate aminotransferase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), and total bilirubin. The histopathological examinations showed sinusoidal dilatation, atrophy of hepatocytes, depletion of glycoprotein, focal area of necrosis, inflammatory cells infiltration and apoptosis. MgO NPs demonstrated potential harm to liver tissue, and human health.

Lung cancer is the first killer among all the known malignancies. Late detection of this malignancy is a major contributor to advanced-stage diagnosis and poor outcomes. Pulmonary microbiota has been recently reported as one of lung cancer risk hallmarks that is still to be fully understood. This study aimed to explore the potential role of microbiome in predicting lung carcinogenesis. Microbiome Library Construction of the 16SrRNA variable region (V3–V4) was conducted by gene amplicon sequencing using the Illumina sequencing platform. Microbiome data was analysed using Version QIIME2-202006 software for species annotations. The sequences were denoised by the DADA2 plugin implemented in QIIMETM2. The bacterial amplicon sequence variants (ASVs) were then identified. The results of 16SrRNA sequencing and gene library bioinformatic analysis indicated that Haemophilus, Prevotella, and Streptococcus were on the top of abundant genera. The species Haemophilus influenza has also been identified among the top 12 bacterial species in malignant and non-malignant lung samples. Microbiome-based identification of the human airway microbiota may provide effective predictive biomarkers for lung carcinogenesis.

Due to the increased use of products carrying heavy metals caused by human factors in the streets of the city of Baghdad and their direct impact on the air, water quality and soil pollution, heavy metals pollution is one of the important environmental problems. As heavy metals cannot be degraded, they accumulate biologically, and, therefore, can directly threaten higher organisms including humans. The current study focused on detecting heavy metals (mercury and lead) to determine the adverse effects and risk of developing type 2 diabetes mellitus (T2DM) in humans. One hundred total blood samples were collected from healthy people and the patients of both sexes who were suffering from T2DM, with mean age of 31 to 75 years, after 8 to 12 hours of fasting. The first group - G1 the control group consisted of 20 health people (6 males and 14 females). The second group - G2 included 40 patients (20 males and 20 females) who had T2DM and were not exposed to any pollutants. The third group - G3 included 40 males with T2DM and also who were exposed to smoke from local electricity generators, as these samples were collected from workers or living near generators and were exposed to air pollution for a long time. Blood samples were collected from each individual by drawing blood from a vein to evaluate each of the biochemical tests, as well as detecting of some heavy metals such as mercury and lead. The results showed that there was a highly significant increase (p≤0.01) when both fasting blood sugar (FBS) and HbA1c were analysed. As for the results of the analysis of the types of lipid profile, there is no significant difference in cholesterol, while there was a significant increase (p≤0.05) in both low-density lipoprotein (LDL) cholestrol and very low density lipoprotein (VLDL) cholestrol and a highly significant increase (p≤0.01) for both triglyceride and high density lipoporotein cholesterol (HDL) cholestrol. The results of the liver function analysis showed that there was no significant difference in the results of the enzyme serum glutamic oxaloacetic transaminase (SGOT) as well as total bilirubin, while there was a significant increase (p≤0.05) in serum glutamate pyruvate transaminase (SGPT). Significant increase (p≤0.01) was detected in the alkaline phosphatase (ALP) enzyme for each of the studied groups. The results of the detection of the presence of heavy metals using the atomic absorption spectrophotometer showed that there was a significant increase (p≤0.01) in lead, and as for mercury, no significant difference was noticed.

Papillary thyroid carcinoma (PTC) represents the most prevalent kind of thyroid gland cancer, making up around 80% of all occurrences of thyroid cancer. Evidence shows that Syndecan-1 (SDC-1) expression is lost in a number of benign and malignant epithelial neoplasms, although its expression profile in thyroid gland neoplasms is yet unknown. Therefore, the aim of this study was to assess SDC-1 expression in papillary thyroid carcinoma patients, as well as the relationship between age and gender and SDC-1 expression. To undertake a detailed investigation of SDC-1 in normal and malignant tissues, tissue sections were used to examine SDC-1 expression in 70 tissue samples, 50 distinct PTC (6 males and 44 females) and 20 normal tissue types (10 from each gender) as control. Ages in each group ranged from 20 to 60 years. The results showed a statistically different level of SDC-1 expression in patients and control. There was no significant relationship between SDC-1 expression and gender or age, in accordance with recent research findings which suggest that SDC- 1 might be exploited as a prognostic indicator or a target in cancer treatment in the future.

This study was conducted to evaluate the bacterial contamination in drinking water tanks from 20 randomly distributed primary schools in Habbaniyah district. The most probable number and the total plate count methods were used to determine the numbers of coliform, fecal coliform, streptococci and fecal streptococci. Chemical and physical properties of the water were measured, including residual chlorine, total dissolved solids, temperature and pH. Biochemical methods were used to detect various bacterial species contaminating the water samples and the results were confirmed using the VITEK 2 compact system. High levels of bacterial contamination were indicated. The highest value of coliform numbers reached 12 cells/100 ml. The total plate count recorded the highest values reaching 300 cells/ml. The concentration of residual chlorine was very low or non-detectable in most of samples below the required concentration and the concentrations of total dissolved solids exceeded the permissible limit (855 mg/L). The genetic relationship between environmental isolates of Escherichia coli bacteria from tank water and clinical isolates from children’s health centers were also studied. The results for Escherichia coli demonstrated a similarity ratio between environmental and clinical isolates of 64% by using dendrogram and Enterobacterial Repetitive Intergenic Consensus PCR. Nevertheless, this study recommends the need to monitor drinking water quality that supplies these reservoirs, as well as to prevent and treat problems that result in drinking water contamination and the spread of diseases.

Ten isolates of Klebsiella pneumoniae, seven isolates of Pseudomonas aeruginosa and nine isolates of Staphylococcus aureus, were obtained from 100 urine samples collected from Baghdad hospitals. All isolates were identified biochemically and confirmed by using VITEK 2 and were then tested for their susceptibility towards 6 antibiotics and for phenolic extracts of Thymus vulgaris and Cinnamomum cassia. All bacteria were greatly affected by T. vulgaris, especially K. pneumoniae. Viable count was performed, it was noted that the number of bacterial cells reduced from 1×108 CFU to 1.2× 103, 2×105 and 1.8×106CFU of K. pneumoniae, P. aeruginosa and S. aureus respectively. While C. cassiahad a slight effect on them. K. pneumoniae isolates which were affected by phenolic extract more than the other bacteria under study and at the same time were resistant to more than one type of tested antibiotics. These isolates were taken to detect their ability to form biofilm by using Congo red as screening method for it. The results showed that all isolates produced biofilms. Also, by using microtiter plate method, the results confirmed that all isolates produced biofilm where 7 isolates were strong biofilm producers and 3 were moderate. The strongest isolate was taken to study the effect of T. vulgaris and C. cassia phenolic extract on its biofilm formation by using microtiter plate method with two concentrations (20 and 40 ml/L). The results showed that biofilm reduction was 45% and 73% for T. vulgaris and that for C. cassiait was 15% and 20% after using 20 and 40 ml/L respectively.

ABO blood types and the Rh factor have recently been linked to an increased risk of developing the pandemic coronavirus disease 2019 (COVID-19) in individuals of different ethnic backgrounds. The conducted current study aimed to comprehend the relationship between this polymorphic apparatus and illness risk in Nineveh patients and healthy individuals. Recovery or death and two COVID-19 outcomes were also investigated. Many individuals including COVID-19 Nineveh patients had their ABO blood type and Rh factors determined. Thus, four groups (under therapy, rehabilitative, death, and healthy donors) were created from the research samples. The diagnosis kit RNA (PCR-Fluorescence Probing) was used to diagnose 2019 novel coronavirus (2019nCoV) for patients. The data was gathered from hospital records. The purpose of this study was to highlight significant data related to COVID-19 patients’ infection, symptoms and prognosis with respect to ABO blood type and Rh factor. Results of the current study included links between Rh factor and ABO blood types with increased infection potential, longer incubation times and fatal acute outcomes. Knowing that COVID-19 predisposing factors might help with patient survival, illness treatment and studying viral path physiology. A total of 2480 samples were ، distributed to control group and hospitalized patients with definite COVID-19 infection in isolation in the hospital in Mosul City for the period between 2nd December 2021 and 5th March 2022. The blood samples were obtained from patients and healthy donors by using kit for blood type identification, while the blood types of the deceased were taken from the hospital records. Several statistical tests were also conducted to find out the differences between the aforementioned groups. ANOVA was performed to investigate the correlation of ABO blood types with incidence of COVID-19 patients. The variables adopted in the analysis were age and sex. The study found that the difference between the association of each blood group with that of cases was significantly correlated (p = 0.00001). Out of total 975 patients 30.2% had blood group A, 27.3% had group B, 26.8% had group O and 15.7% had blood group AB. The study found that the number of recovered patients in 18-30 years age group was not significant among male and female population, while number of patients in other age groups in both male and female patients who finally recovered, was significantly lesser among patients with blood groups A and AB. There was a significant number of patients with blood group B in all the age groups except 31-35 years old. The study concluded that blood group B patients were severely affected by COVID-19.

Resistin (RE) is a cysteine rich polypeptide. It has been established that in humans adipocytes, pancreatic cells, muscle, and mononuclear cells all express this 108 amino acid polypeptide. It is important to note that resistin is primarily produced by peripheral blood mononuclear cells (PBMCs) in humans. Its part in the inflammatory processes has been proven. Immunoglobulins IgA and IgM are crucial elements of humoralimmunity. They increase inflammatory responses and the removal of immunological complexes by activating the complement system, sensitizing natural killer cells, phagocytes, and mast cells, and regulating cytotoxic cellular activity. The aim of this study was to determine resistin and immunoglobulin IgA and IgM levels in the serum of type 1 diabetic (T1DM) and type 2 diabetic (T2DM) patients and the healthy control. This respective study was carried out at the National Center for Diabetes Treatment and Research and the consulting clinics in Al-Muqdadiya District and Baquba District in Iraq during the period from January 2022 to April 2022. It included 153 participants; divided into 3 groups (51 patients with T1DM and 52 patients with T2DM, and 50 healthy control). The result of serum resistin showed that there was a highly significant difference when comparing the mean of serum resistin of T1DM with that of T2DM patients group and the control group. While no significant difference was detected when the mean of serum resistin ofthe T2DM patients group with that of the control group was compared. The mean of immunoglobulins IgA and IgM were compared in T1DM , T2DM patients and the healthy controls with significant differences between the three groups. It can, therefore, be concluded that increased serum resistin levels were recorded in T1DM patients than T2DM patients and healthy control. Increased serum immunoglobulins IgA and IgM levels were also noticed in T1DM and T2DM patients compared with control.

A cohort study was conducted among 210 health staff members in Sulaimani city to assess the safety and effectiveness of the Pfizer-BioNTech vaccine. They were divided into two groups: vaccinated and unvaccinated. The vaccinated group received two doses of the Pfizer vaccine, while the unvaccinated group did not receive any vaccines during this study. Vaccine reactogenicity was assessed using a self-report form. Whereas vaccine immunogenicity was assessed by testing the anti-receptor binding domain IgG (anti-RBD IgG) antibody. Several adverse effects were observed with each dose. The most frequent adverse effects were pain at the inoculation site, tiredness, myalgia and fever. The average adverse effects per person in the first and second doses were 5.3 (SD 3.3) and 6.3 (SD 3.5) respectively (p = 0.005). The immunized group's anti-RBD IgG antibody levels were greatly improved after taking the first and second doses (32.50 and 44.92 binding antibody units (BAU)/mL respectively). After eight months of taking the two doses, the antibody level dropped to 17.00 BAU/mL. This study indicates that the vaccine can be safe and effective for enhancing antibody production.

Koi herpesvirus 3 (KHV-3) is nominated as an emerging, highly devastating, and contagious viral disease with severe economic losses in the carp aquaculture industries worldwide. Carp rearing under intensive culture conditions is constantly vulnerable to infection by various pathogens in the water. Water temperature has a major influence on the onset and severity of the disease. Since common carp have gained importance in the Iraqi fish industry, the investigation and control of associated diseases, including KHV, are warranted. We confirmed the first detection of the KHV from the collected specimens from the suspected cages in the Sulaymaniyah region. Also, an analysis of the thymidine kinase (TK) gene sequence of the KHV showed that the virus strain had a point mutation (proline, P193) instead of threonine, T193. The isolated KHV strains were subdivided into three clades with three distinct genotypes: Asian (Iranian, Indonesian, Japanese, and Chinese), European (UK), and North American (USA and Mexico) genotypes. The virus sequences were more closely related to the Iranian and Indonesian genotypes. This report describes the first isolation and genetic characterization of koi herpesvirus (KHV) in the Sulaymaniyah province, which is important for efficacious management and vaccination programs in the future to control KHV infection in carp cages in Northern Iraq.

This study was designed to investigate the relationship between HLA-G gene
SNPs in exons 2 and 8 and papillary thyroid carcinoma (PTC). Blood samples from
100 patients, categorized into pre- and post-radioactive therapy groups, and 50
healthy subjects were analyzed via PCR and direct sequencing for allele and
genotype frequencies. PTC was eight times more prevalent in women. Thyroid
assessment hormones exhibited reduced T3 levels pre- and post-radioactive iodine
therapy and increased T4 levels post-therapy (49.80 ng/ml vs. control 16.84 ng/ml).
Notably, TSH levels significantly decreased before and after therapy in PTC patients
versus controls (P = 0.005). HLA-G gene SNPs were linked to PTC risk: in exon 2,
genotype AA posed risk (OR = 16.10 and 10.58; P = 0.001) pre- and post-therapy,
while homozygous TT in exon 8 increased risk (OR = 7.30; P = 0.001; OR = 4.41; P
= 0.018), and heterozygous CT was protective (OR = 0.27; P = 0.016) before
therapy. These findings suggest homozygous SNPs contribute to the genetic risk
factors for PTC in the Iraqi population.

fections caused by Staphylococcus aureus are frequent in both public and medical settings. The objective of the current study was to assess the prevalence of the SEA and SEB genes in S. aureus clinical isolates. A hundred-sixty samples were obtained from several sources including wounds, urine samples, burns and respiratory tract, during period between October 2022 - January 2023, from Kadhimiya Hospital, Karkh General Hospital and Yarmouk Hospital. The samples were then subjected to several examinations including microscopic examination (gram staining), cultural characteristics on mannitol salt agar, hemolysis characteristic on blood agar and biochemical tests (oxidase, catalase, and coagulase), to confirm S. aureus isolates. These isolates were also subjected to several antibiotics to select the multi-drug resistant (MDR) isolates. Based on the results, high percentages of S. aureus isolates were found to be sensitive to antibiotics, All the isolates (100%) were sensitive to vancomycin (VA), 95.5% were sensitive to both chloramphenicol (C) and rifampin (RA), 93.2% were sensitive to clindamycin (DA), trimethoprim (TR), levofloxacin (LE), followed by 90.9, 88.6 and 75% were sensitive to oxacillin (OX), gentamicin (CN) and doxycycline (DO) respectively. However, 47.75 and 38.6% of isolates were resistant to azithromycin (AZM) and cefoxitin (CX) respectively. The frequency of SEA and SEB genes was estimated by polymerase chain reaction (PCR). The results revealed that, out of 27 S. aureus isolates tested, 8 had a single band for the SEA gene (102 bp), while 9 had a band for the SEB gene (164 bp). The indicated sfinding that the majority of S. aureus harboured both Sea and Seb ge

The current study aimed to reveal the biosynthesis technique of Fe2O3 nanoparticles by using prodigiosin pigment production from Serratia marcescens. One hundred and twenty samples were obtained from burn and wound infections to isolate Pseudomonas aeruginosa. In addition, the optimum conditions for Fe2O3 nanoparticles biosynthesis were characterized via different methods, including ultraviolet-visible (UV-Vis), atomic force microscope (AFM), fourier transforms infrared (FT-IR) spectroscopy analysis, X-ray diffraction (XRD), and field emission scanning electron microscopy (FE-SEM). In particular, spherical shape particles were observed utilizing FE-SEM technique. In addition, the analysis of AFM revealed Fe2O3 NPs with an average diameter size of 50.73 nm. The influences of varied concentrations of iron nanoparticles (6.25, 12.5 , 25 , 50, 100and 200 mg/ml) on a bacterial isolate of Pseudomonas aeruginosa were examined. The maximum inhibition zone of P. aeruginosa was 24 mm at a concentration of 200 mg/ml of Fe2O3 NPs, whereas the minimum inhibition zone were 10 mm located at 25 mg/ml Fe2O3 NPs concentrations.

The study investigated the antibacterial properties of chromium nanoparticles using Pulse laser ablation technology. The nanoparticles were produced using a Q-switched Nd:YAG pulsed laser of 532 nm and pulsed laser energies ranging from 400mJ to 700mJ at a frequency of 6 Hz. The properties of the nanoparticles were characterized using various techniques, including XRD, AFM, SEM, and EDS, at laser energies of 400 and 700 mJ. The resulting chromium powder exhibited a crystalline structure, with average crystalline sizes of 42.25 and 52.53 nm for laser energies of 400 and 700 mJ, respectively. EDX analysis revealed the presence of chromium, carbon, and oxygen at different concentrations for both energy levels. FE-SEM images showed the shape and aggregation of the nanoparticles, with average diameters of 99.06 and 115.6 nm for laser energies of 400mJ and 700 mJ, respectively. The antibacterial properties of chromium nanoparticles were tested on Staphylococcus aureus and Escherichia coli bacteria. The number of bacteria colonies killed varied at different laser energy levels between 400 and 700 mJ. Nevertheless, complete eradication of Escherichia coli was achieved specifically at a laser energy level of 700 mJ.

Oil and gas extraction greatly impacts the environment due to the existence of natural radionuclides in crude oil. Uncontrolled dealing with the extraction can lead to radiological contamination. This research aims to determine the radioactivity concentration of naturally occurring radioactive materials (NORM) and risk assessment of contaminated soil in oil fields. Three oil fields in Al- Basra government southern of Iraq were investigated; 45 soil samples were collected from several locations, the first group of soil samples from Al Rumaila southern oil field, the second group was collected from Khudair Almay storage site, and the third group collected from a waste pit near an oil-well in Majnoon oil field. High purity germanium (HPGe) detector was used to determine the radioactivity concentrations of radionuclides of the series 238U and 232Th as well as 40K. The mean concentration of 226Ra, 228Ra, and 40K was found in the soil samples of the first group: 5.625×104±1.5%, 5.157×103±1.43%, and 1.744×103±5.7% Bq kg-1, respectively, 1.07×105±1.5%, 7.90×103±1.43% and 8.33×102 ±5.7% Bq kg-1, respectively for the second group and 4.85×102±5.7%, 5.5×101±9% and 1.65×103±15.9% Bq kg-1, respectively for the third group.
The hazard indices, total radium equivalent, representative gamma level index, and annual effective dose equivalent were calculated to estimate the potential radiological health risk in these oil fields. The results fluctuate as acceptance in some samples of the third group and not acceptance in most of the first and the second group soil samples.
The statistical analyses of data show a high positive correlation coefficient (r= 0.996) with significant p-value (p≤0.05) and coefficient of determination (R2= 0.993) between 226Ra and 228Ra in the first and second groups soil samples, whereas there is no correlation in the third group.

Geophysics is one of the branches of Earth sciences and deals with studying the Earth's interior by studying the variation of physical properties within rock layers. Applied geophysics depends on procedures that involve the measurements of potential fields, such as the gravitational method. One of the significant oil fields in southern Iraq is represented by the Nahr Omar structure. A power spectrum analysis (SPA) technique was used to collect gravity data within the chosen oil field area in order to confirm the salt dome in the subsurface layers. The analysis of SPA resulted from six surfaces representing the gravity variation values of the depths (m)14300, 3780, 3290, 2170, 810, and 93.5. Gravity surfaces have been converted to density surfaces to determine density and velocity models from these six depth slices. The inverted data were consistent with the Nafe and Drake standard curves of converting densities to average velocities. The current study illustrated the benefit/assist of the inversion technique of gravity data to average velocity, to a large extent, in detecting the salt dome effects on the subsurface strata for the Nahr Omar oilfield. In comparison, the decrease in the average velocity of the salt dome area is related to this effect. The acquired inversion results were supported by available seismic data within the Nahr Omar area and utilized the seismic attributes processing technique.

The natural radioactivity in the sediments of the Tigris River was measured using gamma spectrometry using an HPGe detector. Ten sediment samples were collected, and the target radionuclides are 226Ra, 232Th and 40K. Radioactivity in sediment was investigated in clay and silt fractions beside the hazard indices. The 226Ra concentration ranged between 6.5 and 11.2 Bq/kg, 232Th from 4.1 to 9.6 Bq/kg, and 40K from 133.8 to 260.2 Bq/kg. The average values of the natural radionuclides of these elements are within the worldwide average or less and comparable to those of the rivers in other countries; accordingly, the radiological hazard indices such as absorbed dose rate, annual effective dose equivalent, hazard indices and gamma index are below the recommended international values, indicating no significant hazard. The study concluded a positive relationship between the radioactivity and clay and silt content.

The aim of this paper is to prove some weak and strong convergence results of a novel modified Picard-S hybrid iteration to converge to fixed point involving nearly non-expansive mapping in the setting of uniformly convex Banach space. Our results generalize and improve the known results of the existing literature.

The aim of this paper is to design a neural network for solving the singular perturbation problems by using neural networks. The modified neuro system using a polynomial of second degree is to replace each component in the training set. The foundation of this approach is to swap off each x in the input vector training set xj⃗⃗⃗ =(x1 ,x2 ,…,xn) , xj∈[a,b] , the polynomial will be as ξ(x) =???? 2 (x2+x+1), ????∈(a ,b). The appropriate value is determined within a certain range, which has a significant impact on the accuracy of the solution. The numerical results show that the modified neuro system method is better and more accurate than usual artificial neural network method, the main reason for this point is connected with the chosen value of . Finally, a method of updating the neural network is clarified by the numerical results of some examples that are compared to the usual artificial neural network method and through which the accuracy of the solution and the rapidity of convergence is proved.

The main purpose of this paper, is to characterize new admissible classes of linear operator in terms of seven-parameter Mittag-Leffler function, and discuss sufficient conditions in order to achieve certain third-order differential subordination and superordination results. In addition, some linked sandwich theorems involving these classes had been obtained

In this study, we introduce a new application to a spectral approach for solving
two-dimensional (2D) time-fractional Zakhrov-Kuznetsov equations (TFZKEs) with
initial conditions (ICs) and boundary conditions (BCs). When the regular magnetic
domain is present, this equation represents a model that illustrates the conduct of
weakly nonlinear ionic phonetic waves in a plasma that provides cool ions and an
electronically isothermal environment. The fundamental qualifiers of fractional
derivatives are characterized in the Caputo concept. We propose a new numerical
approach that relies on shifted Chebyshev polynomials (SCPs) as test functions and
uniformly grid points for time and space. In the field of fractional calculus, we have
to introduce several schemes to evaluate a solution to the nonlinear fractional
problems. This new technique is a preferable attempt. The results show that the
current method is quite effective, and robust which provides excellent accuracy, and
is appropriate for implementation to solve many significant fractional differential
equations.

The goal of fusing multi-focus images is to obtain one image that has all the significant features from each input. The fusion process is needed because of the limitations of the optical lens depth of field that is used to capture images, so images of various focused regions are produced. In this paper, a multi-focus image fusion algorithm is proposed. It is based on utilizing the biorthogonal wavelet transform to extract details and edges from the input images by making the approximation subband equal zero and applying an inverse transform to get images that have only edges, lines, and details. The average gradient metric, which is used to represent sharpness and clarity, is calculated as an activity measurement for each NxN block of the resulted edge images and is used to merge the corresponding blocks of the multi-focus input images to produce the fused one. Consistency verification is used to improve the fusion process. The performance of the proposed method was evaluated and contrasted with a number of other state-of-the-art fusion methods. Experimental results clearly show that the suggested approach is a feasible and efficient multi-focus imaging technique.

The detection of extremism is a field of research aimed at discovering extremism and hate speech on social networking sites through the use of natural language processing tools. Although this field is wide, there is little research in Arabic that has dealt with this topic compared to research in English, although a very large percentage of social media users are Arabs and interact in Arabic. In this research, the most important research works related to the field of extremism detection in the Arabic language using machine learning algorithms will be reviewed, and related works will be compared in terms of methodology and results.

The Internet of Things (IoT) has had a substantial impact on a number of
industries, including smart cities, the medical field, the automotive industry, and
logistics tracking. But along with the IoT's advantages come security worries that
are proliferating more and more. Deep learning-based Intelligent Network Intrusion
Detection Systems (NIDS) have been created to detect continually evolving network
threats and trends in order to address this issue. Six alternative deep learning
algorithms, including CNN, RNN, and DNN architectures, are used in this research
to present a novel anomaly-based solution for IoT networks. Three distinct intrusion
detection datasets were used to evaluate the algorithms, and the findings revealed
that the hybrid method performed better than the others in terms of accuracy. In
particular, the hybrid algorithm had a 99.13% accuracy on the UNSW-NB15 dataset,
an 89.01% accuracy on the IoTID20 dataset, and a 90.83% accuracy on the
BoTNeTIoT-L01-v2 dataset. These results point to the suggested hybrid algorithm
as superior to previous algorithms and a potential intrusion detection method for
Internet of Things networks.

The World Health Organization (WHO) has classified coronavirus as a global health emergency. Chest X-rays have been proven to be beneficial in both diagnosing and monitoring various lung diseases, including COVID-19. In this study, a COVID-19 disease detection framework is provided based on the methods used in machine learning and the Extra Tree algorithm to reduce the features extracted from the images. Using the Gray-Level Co-occurrence Matrix (GLCM) algorithm and the Extra Tree algorithm to choose the features of the work, a set of features is extracted from the images. This set of features is then put into the XGBoost algorithm to be classified. The proposed system was evaluated using two different sets of databases: the large database with 9544 images and the small database with 800 images. All image sizes were set to 300 x 300 pixels. The proposed system achieved a classification accuracy score of 90.04% using the large data set and 99.37% using the small set.

Survival analysis is a statistical method used to analyze time-to-event data. The existence of states where event results become invisible after a given time is one of the primary difficulties with this regulation. This is considered censorship. This kind of data may be found in a variety of applications, such as mechanical system failure rates, patient mortality rates during clinical trials, and the length of unemployment in a community. Assessing the so-called survival and hazard functions is one of the primary goals of survival analysis. Many learning-machine algorithms have also been developed to tackle censored data and other difficult problems with real-world data.
Machine-learning techniques enhance survival analysis by incorporating flexible modeling approaches, handling high-dimensional data, capturing nonlinear relationships, and accounting for censorship. They provide powerful tools to extract meaningful insights and make accurate predictions in time-to-event analysis across various healthcare, finance, and engineering domains. The standard statistical approaches and machine learning techniques advanced for survival analysis are structured in this unified framework, as is the implementation of some machine learning techniques applying to the GBSG2 survival analysis dataset.

Deep learning has become the driving force behind many contemporary technologies and has been successfully applied in many fields. The core objective of this study is to comprehend, investigate, and stipulate significant insights into the deep learning applications in various supply chain management functions through a bibliometric analysis. The literature for the study is gathered from the Scopus database. A bibliometric analysis is conducted using R Studio. It has been found that researchers from China are publishing the most research findings in this area. The study observed drift in concentrated areas of the field from 2007 to 2022. This analysis introduces a paradigm shift in trend techniques. Compared to using computer simulation, long- and short-term memory and reinforcement learning techniques are frequently used for the analysis. Also, the evolution of behavioral research in supply chain-related areas is given prominence compared to research related to agriculture and human interventions, which predominated.

Clustering Organizing items into groups based on their properties such that the items in the same group are similar and those in other groups are distinct is known as clustering and is one method of unsupervised learning. The primary benefit of clustering is that, with little or no prior information, fascinating patterns and structures can be discovered directly from very large data sets. The most representative algorithms, the K-Means algorithm, the nearest neighbor algorithm, and Lloyd's algorithm, were explored and evaluated in this study based on their basic strategies. The proposed algorithms proved highly efficient in classifying data, as k-means results were high by creating data points and classifying that data into 10 groups, while the nearest neighbor algorithm proved highly effective in predicting new groups in light of pre-existing groups for new data points, and finally Lloyd's algorithm achieved high results through several iterations to reach the target groups. Using the random function, 100 random values for inputs as x and 100 for outputs as y are generated, and these values are grouped as points. Calculations of the mean and standard deviation for the data set indicate that 68% of the data points will fall within one standard deviation, 95% within two standard deviations, and 99.7% within three standard deviations. In the nearest neighbor algorithm, confidence in data points within a specified standard deviation number is determined by the coverage factor, or k value. For k = 10, 97% of the data points are expected to fall within one standard deviation. At Lloyd's, a normal distribution curve appears when generating calibration or measurement data.

COVID-19 is a universal infectious disease recognized first by people with influenza and bacterial pneumonia symptoms in Wuhan, Hubei Province. Currently, a new mutated disease has the same symptoms as COVID-19 and influenza and causes dangerous infections in the body. Due to the fact that these two diseases share some diagnostic features and symptoms in common with one another, healthcare workforces require aid and support in predicting patients\' conditions. This was done by using machine learning methods in diagnosis. From this point, this paper proposes a diagnostic model to detect patients\' symptoms and classify them into one of five disease groups, utilizing Neighborhood Component Analysis (NCA) as a feature selection method and the Hidden Naïve Bayes (HNB) method as a multiclass classifier. This paper suggests the model consists of two significant phases: the pre-processing phase (cleaning, normalization, and discretization) and the classification phase. Conducting the COVID-19 dataset, the experimental findings showed that the suggested multi-class model had 89% accuracy for disease diagnoses. Furthermore, according to the patient’s symptoms, the proposed classification model led to a good diagnosis for the mutated COVID-19 disease.

In machine learning, the classification task is about building a model to predict a class of elements based on their attributes and set of examples. This work aims to classify people based on their names. Two models were developed; the former is based on a single feature that is represented by a name. Whereas the latter is built upon nine features derived from the name itself, which are: is_longname, is_vowelend, is_vowelbegin, 2_gramend, 2_grambegin, 1_gramend, 1_grambegin, is_contain_abo, and is_contain_abed. Furthermore, two datasets were utilized: the first was collected from the Ministry of Labor and Social Affairs, while the second was gathered from the Iraqi university website. There are a lot of strange IRAQI names in two datasets, as well as spelling errors, which represent a real challenge in the classification process. Five machine learning methods were applied and tested within the developed models, including Random Forest, Naive Bayes, Logistic Regression, Multilayer Perceptron, and Extreme Gradient Boost. Ultimately, the experimental results demonstrate an increase in accuracy when applying the model to the original dataset, which includes names and their frequencies. The Multilayer Perceptron has achieved 97% accuracy in one feature model, while the Extreme Gradient Boost has achieved 97% accuracy in the multi-feature model. On the other hand, the results do not exceed 79% when the models are applied to the unique dataset (names without their frequencies).

The Food and Agriculture Organization (FAO) claims that plants provide more than 80% of the world's food supply. The report (2023), found that plant diseases are responsible for an estimated 40% of global crop losses each year. Therefore, it was necessary to find a solution that detects and classifies plant diseases early in the plant growth stage, using deep learning techniques. In this review, we will discuss the techniques for detecting plant diseases using leaves that have been studied in previous years. An overview of the articles cited in this review reveals that EfficientNet outperforms all other CNN models for plant leaf disease classification. It achieves an impressive accuracy rate that surpasses even the best-performing VGG, ResNet, and DenseNet models. DenseNet is also a good option, especially when computing resources are limited.

In this work, the effect of the ionospheric variables on the astronomical radio signal was studied. Florida station, for the year 2014, was selected and the actual observation data of the Jupiter signal from the Radio JOVE Data Archive was used. The Radio Jove Pro software was used to determine the number of radio emissions from Jupiter that were supposed to occur. The data of the electron density and temperatures of the ionosphere for altitudes 200-500 kilometers were taken from the International Reference Ionosphere (IRI) site, where 116 supposed events were obtained, and after applying determinants for the angle of elevation of Jupiter and the elevation of the sun, this number was reduced to 45. This work aims to focus on the role of the ionosphere in blocking the radio signal coming from Jupiter. It was found that 28 events were actually observed, while 17 events were not observed. The possibility of observing is better after sunset and depends on Jupiter's height above the horizon relative to the observer. The value of F2 peak density during the night time and along the year was about 1011 to 1012 m-3 and at a height of about 270-360 km. Approximately 82% of the observations occurred when the F2 peak density was less than 6×1011 m-3 and the height was greater than 300 km, and approximately 82% of the cases of non-observation occurred when the F2 peak density was greater than 6×1011 m-3 and the height was less than 300 km. The probability of observation is greatly affected by the relationship between the value of the electron density and its height.

In recent years, interest in renewable energies has increased due to their clean, renewable, and inexhaustible characteristics. Iraq faces a significant challenge in the shortage of electric power. The green economy, especially in renewable energy, has become one of the world's main pillars, especially after the remarkable rise in greenhouse gas emissions (GHG) from fossil fuel combustion for electricity production. This study evaluates wind farm feasibility, assessing costs, revenue, and financial viability. It identifies potential hazards and difficulties, enabling developers to develop strategies to mitigate risks during project creation and operation. This paper aims to undertake a feasibility study to assess the viability of constructing a wind farm with a capacity of 340 megawatts at the Al-Razaza location in Karbala province. The study site utilized authentic data from the meteorological tower installed on the premises. The data employed spanned three years, encompassing a comprehensive and integrated dataset for the study. RETScreen, a powerful model renowned for its energy efficiency analysis and sustainable decision-making capabilities, was employed. Through the application of RETScreen, a meticulous assessment and analysis of the intricate project components were facilitated. A study of risk analysis was conducted to ascertain the impact of various parameters on the payback period of the project. The findings showed that the capacity factor of the wind farm is 22.3%, which is relatively low, the simple payback period is 10.3 years, and the energy production cost is 0.059$/KWh.