Kurdistan Journal of Applied Research (KJAR)
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Enhancement of Phosphorus Sorption onto Peanut shell by Means of Aluminum and Iron Oxide Coatings
No full textPhosphorus serves as a crucial nutrient for the organisms. Still, the discharge of high levels of phosphate concentrations into limited aquatic environments leads to the phenomenon known as eutrophication, which subsequently leads to the degradation of the overall water quality. In this study the capacity of the peanut shell, Al-coated and Fe-coated is determined in relating by adding 10, 20, and 30, 40 mg P L-1 as (KH2PO4) to 1.0 g of each sample and shaking for 2, 6, 12, and 24 h and at pH ranged between 4.6, 5.2, 6.3 and 7.8. At the end of each period, the suspension was filtered and analyzed which presents the concentration of equilibrium P. The maximum P adsorption of 28.9±0.07 and 50.6±0.49% was recorded for 24 h of incubation and at pH (4.6) with an Al-coated peanut shell respectively. The findings of this study demonstrated that the adsorption of phosphorus (P) increased as the duration of incubation increased. At the same time, it decreased with a decrease in the pH of the solution. Furthermore, the outcomes indicated that the Freundlich model provided the best fit for the data, based on higher R2 values ranging from 0.9867 to 0.9938, in comparison to the range of 0.9659 to 0.9904 observed for the Longmuir model. These results suggested that peanut shells coated with Al can be used to remove high amounts of P in the solution. Also, the removal resulted from the physical adsorption of P rather than a chemical reaction.
Prevalence and Associated Risk Factors of Childhood Obesity and Overweight in Erbil City, Kurdistan, Iraq: A Household Survey
Get PDFThe prevalence of overweight and obesity has increased dramatically during the last three decades. It is estimated that 170 million youngsters (under the age of 18) worldwide are overweight. Obesity is commonly regarded as one of the most significant public health problems of the early 21st century due to its rapid rise and severe public health consequences. According to previous research, the prevalence rate of obesity and overweight increased in Erbil. This study aims to determine the prevalence of childhood overweight and obesity rates in Erbil. This study was a household survey based on a cross-sectional study that was conducted, and a multi-stage sampling strategy was used to choose the study sample. A questionnaire was conducted to collect the data, SPSS was used for analysing the data, and Chi-square (X²), was used to identify any kind of association between different variables in the study, whereas the p-value was considered (0.05). Regarding ethical approval, the study was conducted under the guidelines of the ethical approval research committee in the College of Medicine. In this research, the prevalence rate of obesity and overweight was 30.4%, 7.7% of them were obese, and 22.7% were overweight. The obesity rates between males and females were not different. Lifestyle and habit had an impact on increasing weight gain, though the number of meals, outside eating, fast foods, and snacks between meals were statistically not significant. However, eating without hunger was significant the p-value was (0.008). On the other hand, the children's habit of using electronic devices, smartphones, and other devices was significant, either using their mobile phone or their parent's phone. In conclusion, in Erbil city, the prevalence rate of obesity and overweight was dramatically increased, as well as the lifestyle factor that contributed to the development of these conditions, especially among those children exposed to multi-screen devices. According to these results, families have to improve their lifestyles and decrease the use of electrical devices by children
Fecal Microbiota Transplantation: A Systematic Review of Therapeutic Potential, Preparation Techniques, and Delivery Methods Across Medical Conditions
Get PDFFecal microbiota transplantation (FMT) is revolutionizing the treatment of gastrointestinal disorders by leveraging the gut microbiome in innovative ways. This systematic review evaluates the clinical effectiveness and safety of FMT across various medical conditions, offering insights into its therapeutic potential and limitations. A comprehensive search of PubMed, Web of Science, Scopus, Embase, and ClinicalTrials.gov from January 2000 to December 2023 identified 97 relevant studies on FMT's efficacy, safety, and microbiome changes after eliminating duplicates. FMT has demonstrated high success rates, particularly in treating recurrent and refractory Clostridium difficile infections (CDI), with up to 90% effectiveness, establishing it as a primary treatment for antibiotic-resistant cases. FMT’s applications are expanding to inflammatory bowel diseases (IBD), including ulcerative colitis and Crohn's disease, as well as metabolic disorders and neuropsychiatric conditions. Remission rates for IBD range from 37-45%, with outcomes influenced by donor characteristics, stool preparation, and disease subtype. with mild, self-limiting side effects such as transient diarrhea and abdominal cramping. However, rare serious adverse events underscore the need for rigorous donor screening and standardized preparation protocols to mitigate risks. FMT’s ability to restore healthy gut flora highlights its promise in both gastrointestinal and systemic disease management. However, further research is essential to establish optimized procedures, standardized guidelines, and long-term safety data to facilitate its integration into mainstream medical practice
Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier
Get PDFThere are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent
Antibacterial and Antibiofilm Effects of Green Tea and Salvadora Persica L. Extracts Against Clinically Isolated Porphyromonas gingivalis: An in Vitro Study
Get PDFThe combination of an aqueous extract of green tea (GT) and Salvadora persica L. (SP) was proved to prevent the growth of Gram +ve facultative anaerobes and reduce biofilm formation on teeth surfaces. Porphyromonas gingivalis (P. gingivalis) is amongst the red complex pathogenic bacteria that cause periodontal disease. Thus, the aim of the current study was to examine the antibacterial and antibiofilm effects of the above combination in reducing the growth of P. gingivalis. Subgingival dental biofilm samples were collected from patients with severe periodontitis to isolate and confirm the presence of P. gingivalis. Gas mass chromatography-mass spectrometry was used for phytochemical analysis. The maceration method was used to extract the GT and SP. Disc diffusion and broth dilution methods were performed to determine antibacterial and minimum inhibitory concentrations (MIC) of SP, GT aqueous extract, and their combination in contrast to clinically isolated P. gingivalis. Further, the antibiofilm activity of the extracts and their combination was assessed using the tube adhesion technique. The findings showed that only the GT aqueous extract was effective against P. gingivalis, while the SP aqueous extract demonstrated no effectiveness. The MIC of GT was 6.25mg/mL. The aqueous extract of SP showed a greater antibiofilm effect than the aqueous extract of GT at the lowest concentrations of 6.25mg/mL and 12.5mg/mL, respectively. In conclusion, the antibacterial property of the SP and GT extracts combination against P. gingivalis was attributed to GT only. While SP extract displayed no inhibitory role against P. gingivalis, it could potentially reduce the biofilm attachment of P. gingivalis better than GT extracts
Predict Diabetes Using Voting Classifier and Hyper Tuning Technique
Get PDFToday, diabetes is one of the most common chronic diseases in the world due to the people’s sedentary lifestyle which led to many health issues like heart attack, kidney frailer and blindness. Additionally, most of the people are unrealizable about the early-stage diabetes symptoms to prevent it. The above reasons were encouraging to develop a diabetes prediction system using machine learning techniques. The Pima Indian Diabetes Dataset (PIDD) was utilized for this framework as it is common and appropriate dataset in .CSV format. While there were not any duplicate or null values, however, some zero values were replaced, four outlier records were removed and data standardization were performed in the dataset. In addition, this project methodology divided into two phases of model selection. In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. Then six different algorithms (Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and Naïve Bayes) were applied. In the second phase, the four best performed algorithms (with best estimated parameters for each of them) were chosen and used as an input for the voting classifier, because it applies to find the best algorithm between a group of multiple options. The result was satisfying, and Random Forest was achieved 98.69% in second stage, while its accuracy level was 81.04% in the previous one and it utilized to predict diabetes via a simple graphic user interface. 
CAG Expansion in Androgen Receptor Gene of Infertile Men in Erbil Governorate
Get PDFSpermatogenesis and male phenotypic development during puberty are mainly done by androgen and their function is regulated by the androgen receptor (AR) gene. This gene has a polymorphism site in Exon1 which encode androgen receptor and have various length of CAG trinucleotide repeat which causes the production of polyglutamine chain in different length of the N-terminal domain of AR protein which reduces producing sperm by disrupting spermatogenesis.
The aim is to determine the relation of infertility in male with the increased frequency of CAG repeats in the AR gene, and the correlation between CAG repetition and hormonal changes.
The case-control research was carried out in the Immunogene center and IVF center in the maternity teaching hospital Erbil-Kurdistan region-Iraq. The convenience sample included 50 men, 30 infertile and 20 fertile over one year starting from March 2021 to March 2022.
Men with infertility had CAG repeats in their AR gene, ranging from (17-26) repeats, with a mean (21.3 ±0.16). In infertile men, CAG expansion was longer than the fertile men. The motility and normal morphology of sperm in infertile men have negative relation while sperm count and concentration have a positive relation with CAG expansion. The relation of hormones (Testosterone, LH, and FSH) with CAG repetition was statistically not significant.
In conclusion, CAG expansion was longer in infertile (case) men compared with fertile (control) men. Polyglutamine effect on increasing sperm abnormal morphology and immotility which is the reason for infertility but statistically not significant and it will not affect hormonal assay in infertile men
Nutritional and General Awareness of Vitamin D Status among Adult Population in Sulaymaniyah Governorate, Kurdistan Region, Iraq: A Cross-Sectional Study
No full textThe health benefits of vitamin D are widely acknowledged by scientific and public health specialists. In Iraq, vitamin D deficiency and inadequacy are highly prevalent. However, public knowledge on this problem is scarce. This research aimed to evaluate the participants' knowledge about vitamin D, particularly their nutrition-related understanding and behavior, along with their overall attitude toward sun exposure. A descriptive cross-sectional survey of Iraqi adults over 18 was performed with a collection of serum 25(OH)D result between June and September 2022. The observed serum vitamin D indicated that only 25% of the participant had healthy (>30 ng/ml) vitamin D levels. The majority of participants, 90.9%, were aware of vitamin D. Media and primary health care centers were major vitamin D information sources (57.3 and 32%, respectively). Despite believing sunshine is the principal source of vitamin D (90.2%), respondents lacked understanding about the duration (26.3%) and frequency (30%) of sun exposure. In addition, less than 10% of individuals attributed vitamin D shortage to kidney and liver problems, fat malabsorption, obesity, and bariatric surgery. Nevertheless, more than two-thirds (83.3%) of participants defined the positive role of vitamin D in preventing osteoporosis and immune system strengthening (61%). Furthermore, nutritional awareness among the participants was variable. Approximately, 75% misrepresented the percentage of vitamin D supplied by food, over 50% believed that fruit and vegetables are vitamin D sources, 43% of vegetarians are not at risk for vitamin D deficiency, and rather plants considered (70.3%) as an approach to lessen vitamin D deficiency. Additionally, optimal daily intake was recognized by only 10%. Similarly, only 18.3% was aware of the optimal level of serum vitamin D. Notably, 54% was entirely ignorant of the benefits of dietary fortification. Although 55% of the surveyed population used vitamin D supplements, and 76% acknowledged it through their doctor's recommendation, exceeding two-thirds incorrectly anticipated that drinking tea would impair vitamin D absorption. Therefore, 90% would buy supplements without a prescription if needed. Implementing nutrition education initiatives, encouraging healthy lifestyles, and supporting vitamin D examination should be included in Iraq's health care system
Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model
Get PDFWith the increase in the volume of news articles and headlines being generated, it is becoming more difficult for individuals to keep up with the latest developments and find relevant news articles in the Kurdish language. To address this issue, this paper proposes a novel data augmentation approach for improving the performance of Kurdish news headline classification using back-translation and a proposed deep learning Bidirectional Long Short-Term Memory (BiLSTM) model. The approach involves generating synthetic training data by translating Kurdish headlines into a target language in this context English language and back-translating them to the Kurdish language, resulting in an augmented dataset. The proposed BiLSTM model is trained on the augmented data and compared with baseline models SVM (Support-Vector-Machines) and Naïve Bayes an trained on the original data. The experimental results demonstrate that the proposed BiLSTM model outperforms the baseline model and other existing models, achieving state-of-the-art performance on the Kurdish news headline classification task. The findings suggest that the combination of back-translation and a proposed BiLSTM model is a promising approach for data augmentation in low-resource languages, contributing to the advancement of natural language processing in under-resourced languages. Moreover, having a Kurdish news headline classification model can improve access to news and information for Kurdish speakers. With the classification model, they can easily and quickly search for news articles that interest them based on their preferred categories, such as politics, sports, or entertainment
Enhanced AdaBoostM1 with Multilayer Perceptron for Stock Price Prediction
Get PDFStock market investment has gained significant popularity due to its potential for economic returns, prompting extensive research in financial time series forecasting. Among the predictive models, various adaptations of the AdaBoostM1 algorithm have been applied to stock market prediction, either by tuning parameters or experimenting with different base learners. However, the achieved accuracy often remains suboptimal. This study addresses these limitations by introducing an enhanced version of AdaBoostM1 (ADA), implemented on the Waikato Environment for Knowledge Analysis (WEKA) platform, to forecast stock prices using historical data. The proposed model, termed AdaBoost with Multilayer Perceptron (ADA-MLP), replaces the commonly used Decision stumps with a set of Multilayer Perceptron (MLP) models as weak learners. The experimental results demonstrate that ADA-MLP consistently outperformed the standard AdaBoostM1 algorithm, achieving an average classification accuracy of 100%, compared to 98.48% by AdaBoostM1—a relative improvement of 1.52%. Additionally, ADA-MLP demonstrated superior performance against other enhanced versions of AdaBoost presented in prior studies, achieving an average of 5.3% higher accuracy. Statistical significance testing using the paired t-test confirmed the reliability of these results, with p-values < 0.05. The experiments were conducted on the Yahoo finance dataset from 25 years of historical data spanning from January 1995 to January 2020, comprising 6295 samples, ensuring a robust and comprehensive evaluation. These findings highlight the potential of ADA-MLP to enhance financial forecasting and offer a reliable tool for stock market prediction. Future research could explore extending this approach to other financial instruments and larger datasets to further validate its effectiveness