Breast cancer is the most repeatedly detected cancer category and the second reason cause of cancer-linked deaths among women worldwide. Tumor bio-indictor is a term utilized to describe possible indicators for carcinoma diagnosis, development and progression. The goal of this study is to evaluate part of some cytokines and biomarkers for both serum and saliva samples in breast cancer then estimate their potential value in the early diagnosis of breast cancer by doing more researches in saliva, and utilizing saliva instead of blood (serum and plasma) in sample collection from patients. Serum and salivary samples were taken from 72 patients with breast cancer and 45 healthy controls, in order to investigate the following

The aim of this study is to investigate oxidative stress and leptinlevel in obese women. Fifty obese women with BMI value of ≥ 30.0 kg/m² and thirty healthy women with BMI<24.9 kg/m² were involved in the study during their attendance at Gastroenterology and Liver Hospital/ Medical city in Baghdad province from October 2018 to February 2019. The age range for all women was 30-50 years. Blood samples were collected from each participant for the evaluation of the levels of leptin hormone, Super Oxide Dismutase (SOD), and Malondialdehyde (MDA). The results of BMI, leptin hormone, MDA and SOD showed highly significant increase (P<0.01) in obese women in comparison with the control group.

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     One of the most prevalent illnesses in developing countries is myocardial infarction (MI), which develops when the heart's blood supply is suddenly interrupted and causes tissue damage. It is connected to several metabolic risk factors, including diabetes, hypertension, and obesity. The objective of this study was to assess how oxidative stress (OS) contributed to the pathophysiology of MI and T2DM. The current study examined 152 male samples, including 52 MI patients without T2DM (G1), 50 MI patients with T2DM (G2), and 50 seemingly healthy men as controls (C). The levels of fasting blood sugar (FBS), C-reactive protein (CRP), total oxidant status (TOS), total antioxidant status (TAS), oxidative stress index (OSI), malondialdeh

Background: lung function is closely related to anthropometric variables. Anegative relationship between different adiposity markers and lung function has been well established.

Background: Although, different protocols of chemotherapy are recommended for the treatment of metastatic breast cancer, still response rates are variable.
Objectives: The aim of this study is to investigate the effects and correlation of different chemotherapy administered to metastatic breast cancer patients on serum levels of some biomarkers.
Patients and methods: Thirty metastatic breast cancer patients were enrolled in the study. The patients received different protocols of chemotherapy. Blood samples were taken from the patients before and after the last cycle of each protocol and from 20 healthy control and serum levels of biomarkers IL-6, leptin, CA 15-3 and p53 were estimated by Elisa.
Results: The mean serum levels of

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

      Eight-hydroxyguanosine  (8-OHdG) is considered as one of the principle forms of oxygen radicals that stimulated the oxidative stress and has been extensively utilized as a biomarker for oncogenesis. The primary goal of the present study was to investigate the alteration in the levels of 8-OHdG, antioxidant profile and proinflammatory cytokines levels in patients with lung carcinoma. Blood samples were collected from 40 cases with lung cancer (stage III) admitted before the treatment, for health examination at the Nanakaly Hospital in Erbil city and 45 healthy samples of controls with ages ranging between 38-69 years for both groups. Circulating concentration of 8-OHdG, tumor necrosis factor and interleukin-6 were evaluated by

Background: Chronic obstructive pulmonary disease (COPD) is a preventable disease with some significant extra pulmonary effects that may contribute to the severity in individual patients.. The systemic manifestations  of  COPD  include  a number of endocrine disorders , such  as  those  involving the pituitary, the thyroid , the gonads, the adrenals  and  the pancreas.  The  mechanisms  by which COPD  alters  endocrine function are incompletely understood  but likely involve  hypoxemia , hypercapnia , systemic inflammation and  glucocorticoid  administration.

Objective:  To evaluate the relationship bet

Background: Human colorectal carcinogenesis is a complex, multistep and multigenetic process. Matrix metalloproteinase-2 and cyclooxygenase-2 are key enzyme in degradation of extracellular material, are over expressed in several epithelia like colorectal adenocarcinoma.
Objectives: This study was designed to detect the expression of matrix metalloproteinase-2 and cyclooxygenase-2 in patients with colorectal carcinoma and their correlation with age, gender, tumor grade and presence or absence of muscle invasion.
Materials and methods: Immunohistochemical staining of MMP-2 and COX-2 was determined in 40 tissue samples from colorectal patients, from teaching laboratories in Baghdad medical city. In additi

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a