To investigate the activity and role of certain enzyme markers in 30 patients female with breast cancer (non-treated, treated, and treatment with recovered).The serum activity of enzyme tumor markers (ALP, GPT and GOT) of (30) patients with breast cancer, and (7) healthy control subjects by using statistical analysis: There is significant difference higher in activity of serum enzyme tumor markers (ALP, GPT, and GOT) in all patients as compared with healthy control

Chronic inflammation can induce proliferative events and posttranslational DNA modifications in prostate tissues through oxidative stress. The miR-155 expression is abnormally increased in tumors. Therefore, this study aims to explore the clinical significance of serum TNF-α and IL-1α as well as miR-155 expression in prostate cancer (PC) patients.

A total of 50 blood samples from patients diagnosed with prostate cancer were collected from the Oncology Department, Baghdad Teaching Hospital, along with samples from 50 healthy volunteers. Serum TNF-α and IL-1α levels in Iraqi males with prostate cancer were estimated by using enzyme-linked immunosorbent

assay (ELISA). The total RNA was extracted from all subjects and con

     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470

The aim of the research is to identify the relationship between health anxiety associated with Coronavirus (Covid 19) and its relationship to health behavior among Baghdad University employees, as well as to identify the differences in health anxiety and health behavior according to the variables (gender, occupation, and age). To achieve the objectives of the research, a scale was designed to measure the health anxiety in addition to the adoption of the health behavior scale prepared by (Renner & Schwarzer, 2005). The two scales were applied to a sample of (277) academics and (206) employees, while the number of students was (667). The sample was chosen by electronic application from a number of colleges at Al-Jadiriyah Complex. Afte

     Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing

This study was carried out to describe the gene expression of the micro RNA 122a gene with the development of diabetes in Iraq. The difference in gene expression between patients and healthy controls was properly considered. In this study, blood was isolated from 121 individuals divided into two groups as follows: 80 samples of diabetic patients and 41 samples from a healthy control. miRNA was isolated and transformed into cDNA, and the expression of mi122a was measured by qRT-PCR. The researchers looked at the relationship between age and gender and the occurrence of diabetes, as well as how they compared to controls. When comparing the mean gene expression level (Ct) of patient groups to the corresponding Ct means in the control group, th

Background: Maxillary sinusitis is one of the most common infections of humans. Sinusitis can be defined as an inflammation of the membrane lining of any sinus, especially one of the
paranasal sinuses.
Objective: To determine the causative microorganisms of chronic maxillary sinusitis.
Patients: Forty five chronic sinusitis patients were involved in the present study.
Methods: Sampling method were sinus specimens (aspiration or injection aspiration).
Results: Haemophilus species, Streptococcus pneumoniae (S.pneumoniae) and Moraxella catarrhalis (M.catarrhalis) were the most frequent isolates; in addition Penicillium and
Cladosporium species were isolated from some chronic sinusitis patients.
Conclusion: Chr

Background: Maxillary sinusitis is one of the most common infections of humans. Sinusitis can be defined as an inflammation of the membrane lining of any sinus, especially one of the
paranasal sinuses.
Objective: To determine the causative microorganisms of acute maxillary sinusitis.
Patients: Forty five acute sinusitis patients were involved in the present study.
Methods: Sampling methods were per-oral nasopharyngeal swabs.
Results: Haemophilus species, Streptococcus pneumoniae (S.pneumoniae) and Moraxella catarrhalis (M.catarrhalis) were the most frequent isolates.
Conclusion: The most causative agents of acute maxillary sinusitis were bacterial isolates, which were Haemophilus species followed by S.pne