Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (ARMS) and Scorpions. Results: A point mutation, G719X, in exon−18 with three different profiles, G719A, G719S, and G719C was significantly diffused in EGFR. L858R in the same exon and T790M in exon−20 was also detected. While no deletions in exon −19, and no substitutions or insertions in exon −20 were found. Moreover, no significant differences (P≤0.05) in EGFR mutations were seen between males (28.57%) and females (30.76%). In contrast, EGFR mutations were significantly (P≤0.05) prevalent in smoker’s males (26.6%) than females 6.6%). Conclusion: Using the bronchial wash samples was efficient for detection of mutations in lung cancer. Moreover, Iraqi patients with NSCLC were discriminated in EGFR genotype; the point mutation G179X in exon−20 was dominant and L858R in the same exon and T790M in exon−20 were detected while no mutations in exon− 19 and −20 were investigated.
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
... Show MoreGenetic polymorphisms of genes whose products are responsible for activities, such as xenobiotic metabolism, mutagen detoxification and DNA-repair, have been predicted to be associated with the risk of developing lung cancer (LC). The association of LC with tobacco smoking has been extensively investigated, but no studies have focused on the Arab ethnic- ity. Previously, we examined the association between genetic polymorphisms among Phase I and Phase II metabolism genes and the risk of LC. Here, we extend the data by examining the correlation of OGG1 Ser326Cys combined with CYP1A1 (Ile462Val and MspI) and GSTP1 (Ile105Val and Ala103Val) polymorphisms with the risk of LC. Polymerase chain reaction- restriction fragment length polymorphism (
... Show MoreTo determine the relationship between Helicobacter pylori infection and Multiple Sclerosis (MS) disorder, 20 patients with MS aged (25-60) years have been investigated from the period of 2016/12/1 to 2017/3/1 and compared to 15 apparently healthy individuals. All study groups were carried out to measure anti H.pylori IgA and H.pylori IgG antibodies by enzyme linked immunosorbent assay (ELISA) technique. There was a significant elevation (p<0.05) in the concentration of anti H.pylori IgG and IgA antibodies (Abs) compared to control group, and there was no significant difference (p>0.05) in the concentration of IgA and IgG (Abs) of H.pylori according to gender, and there was no significant difference (p>0.05) in the concentration of IgA and I
... Show MoreHuman cytomegalovirus (HCMV) infection is ubiquitous and successfully reactivated in patients with immune dysfunction as in patient with multiple myeloma (MM), causing a wide range of life-threatening diseases. Early detection of HCMV and significant advances in MM management has amended patient outcomes and prolonged survival rates.
The aim of the study was to estimate the frequency of active HCMV in MM patients.
This is a case–control study involved 50 MM patients attending Hematology Center, Bag
Abstract A descriptive study to assess the quality of life (QOL) for patients with colorectal cancer. The study was conducted from Baghdad Teaching Hospital, Al-Yarmouk Teaching Hospital and Radiation Hospital and Nuclear medicine for the period from 1st July/2004 to 1st September/2004. The sample selected by purposive random of (50) patients diagnosed with colorectal cancer and all of them who were under chemotherapy treatment. A questionnaire was prepared for the purpose of the study and comprised of three parts including: 1- Socio-demographical characteristics. 2- Clinical characteristics. 3- and QOL