Objective To highlight the main demographic characteristics and clinical profiles of female patients registered with breast cancer in Iraq; focusing on the impact of age.Methods This retrospective study enrolled 1172 female patients who were diagnosed with breast cancer at the Main Center for Early Detection of Breast Cancer/Medical City Teaching Hospital in Baghdad. Data were extracted from an established information system, developed by the principal author under supervision of WHO, that was based on valid clinical records of Iraqi patients affected by breast cancer. The recorded information regarding clinical examination comprised positive palpable lumps, bloody nipple discharge, skin changes, bilateral breast involvement, tumor size, lymph node status, and the stage of the disease.Results The mean age at the presentation was 51 years; patients under the age of 50 constituted 46.8%. Overall 9.8% were not married, 22.4% were illiterate whereas 19.2% graduated from universities. About 72% of the patients had more than two children, merely 7.5% delivered their first child after the age of 35 years and only 11% were nulliparous. History of lactation and hormonal therapy was recorded in 57.6% and 19.4% respectively. Family history of cancer was positive in 28.8% and breast cancer specifically in 18.7%. Clinically, the most common presenting symptom was breast lumps (95%) followed by skin changes/ulcerations (6.7%) and bloody nipple discharge (4.3%).Bilateral breast involvement was encountered in 4.7%. More than two-thirds of the patients (68.2%) had palpable axillary lymph nodes; classifying 40.5% into advanced stages III and IV. In general stages I–IV comprised 12%, 47.5%, 31.9%, and 8.6% respectively. Upon stratifying the studied sample with respect to age at diagnosis, it was observed that the frequency of unmarried patients was significantly higher among younger women under the age of 50 years, whereas illiteracy and nulliparity features were statistically lower (p < 0.05).Conclusion A considerable proportion of breast cancer patients in Iraq still present with locally advanced disease at the time of diagnosis. That justifies the necessity to promote public awareness educational campaigns to strengthen our national early detection program. Excluding the marital status, level of education and number of parity, there was no statistical difference regarding the impact of age on the demographic and clinical profiles of breast cancer among premenopausal versus postmenopausal Iraqi patients.
Background: Iron homeostasis is crucial to many physiological functions in the human body, such as cellular activity, erythropoiesis, and the innate immune response. Iron deficiency anemia may occur from obesity's ability to disturb iron homeostasis. Obesity may be seen as a pre-inflammatory condition with mild, ongoing systemic inflammation. Additionally, an increase in hepcidin levels by chronic inflammation causes iron insufficiency in obese people. For this reason, this current experiment is designed to investigate the iron profile and some hematological and inflammatory parameters in obese adults in the Kurdistan region-Iraq.
Subjects and Methods: The cross-sectional study w
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The aim of the current research is to identify the Effect of the alternative evaluation strategy on the achievement of fourth-grade female students in the subject of biology. The researchers adopted the zero hypothesis to prove the research objectives, which is there is no statistically significant difference at the level (0.05) between the average scores of the experimental group who study according to the alternative evaluation strategy and the average scores of the control group who study in accordance with the traditional method. The researchers selected the experimental partial adjustment design of the experimental and control groups with the post-test. The researchers intentionally selected (Al-fed
... Show MoreColorectal cancer (CRC) is the most common gastrointestinal malignancy and one of the top ten common cancers worldwide with approximately 2 million cases. There are multiple risk factors that could lead to CRC emergence; of which are genetic polymorphisms. Excision repair cross-complementing group 2 (ERCC2) gene encodes for ERCC2 enzyme which plays a crucial role in maintaining genomic integrity by removing DNA adducts. Several studies suggested that there could be a link between genetic polymorphisms of ERCC2 gene and the risk of CRC development. Hence the present study aims to validate the relationship between the following ERCC2 single nucleotide polymorphisms (rs13181, rs149943175, rs530662943, and rs1799790) and CRC susceptibility. A t
... Show MoreThis study is carried out to investigate the prevalence of Coxiella burnetii (C. burnetii) infections in cattle using an enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) assay targeting IS1111A transposase gene. A total of 130 lactating cows were randomly selected from different areas in Wasit province, Iraq and subjected to blood and milk sampling during the period extended between November 2018 and May 2019. ELISA and PCR tests revealed that 16.15% and 10% of the animals studied were respectively positive. Significant correlations (P<0.05) were detected between the positive results and clinical data. Two positive PCR products were analyzed phylogenetically, named as C. burnetii IQ-No.5 and C. burnet
... Show MoreAutorías: Mariam Liwa Abdel Fattah, Liqaa Abdullah Ali. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 4, 2023. Artículo de Revista en Dialnet.
One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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