Seroma formation continues to be a problem for patients undergoing surgery of the breast or axilla for invasive breast cancer. In general, seroma usually disappears within several weeks; however, some patients are troubled by chronic seroma. Several techniques have been utilized to minimize seroma formation, with no consistent success. Considering the high prevalence of seroma in breast cancer surgery and the need for frequent visits of the patients, a preliminary study was conducted to determine the feasibility of developing an internal drain for seroma evacuation and test its performance. A special drain was designed at the Department of Biomaterial Research, Breast Cancer Research Institute of Jihad. The performance of the drain in terms of seroma drainage efficacy was tested by passing normal blood through the drain. The efficacy of the drain in terms of cancer cell filtration was then tested by passing a cell suspension containing breast cancer cells through the drain. To confirm the drain efficacy, seromas were taken from five patients and passed through the drain. In the last step, the efficacy of the drain was tested in an external manner on 10 mastectomized cancer patients. In all stages, the seroma output fluid was evaluated in terms of the presence of cancer cells by cytology, and the existing problems were resolved. Finally, a drain was developed with proper efficacy in seroma passage and 100% filtration of cancer cells. The high efficacy of the designed drain in seroma passage and filtration indicates its appropriateness to be used in the patients’ body. The microfilter placed in the drain is made by the latest technological methods. In the next phase of the study, the internal application of the drain will be tested by implanting it in the surgical site of the patients.

There is a close relationship between the incidence of breast cancer and fat intake in different populations. Overweight and obesity during menopause can increase the risk of breast cancer in women. Hormones like estrogen and leptin are factors that affect the growth and proliferation of breast cells. The main purpose of this study was to evaluate the relationships between body mass index (BMI) and serum levels of leptin and 17β-estradiol in postmenopausal women with breast cancer. Seventy-nine postmenopausal women with breast cancer and Seventy-nine healthy postmenopausal women, aged 50-75 years, were recruited for the study. After obtaining written informed consent and demographic data, blood samples were taken from all participants to assess the serum concentrations of hormones using ELISA. The mean BMI was 25.59 ± 0.34 kg/m2 for the cancer group, 23.21 ± 0.22 kg/m2 for the control group  (P = 0.001). The difference in serum concentration of 17beta-estradiol between the cancer group and the control group was significant (54.27 ± 1.91 and 46.44 ± 1.36 pg/ml, respectively, P = 0.001). The mean serum leptin level was significantly lower in the cancer group (41.05 ± 3.10 ng/ml) than in the control group (53.05 ± 4.83, 53 ng/ml) (P = 0.036). Body mass index in both groups was significantly and positively correlated with serum levels of 17β-estradiol, but there was no significant correlation between BMI and serum leptin concentration. Postmenopausal women with BMI ˃ 25 and high serum concentrations of 17β-estradiol are probably susceptible to breast cancer.

Some factors such as exercise training and pharmacological interventions can slow tumor growth. To determine the underlying mechanisms, we investigated the effect of six weeks of interval exercise training along with ingestion on Bcl-2 and LC3 genes expression in the tumor tissue of breast tumor–bearing mice. Twelve female BALB/c mice were injected with cancer cell line and assigned to four groups: control (CT), training (TT), selenium nanoparticles (ST), and training +SeTT). Interval training program was conducted for 6 weeks, 5 sessions per week. The mice in the ST and SeTT groups received for six weeks. Two days after the last session of intervention (exercise or selenium nanoparticles), the mice were anesthetized and tumor tissues were removed. and LC3 genes was determined with real-time PCR technique. A one-way analysis of variance, followed by the Tukey post hoc test, was used to compare gene expression among the groups. Results indicated a significant decrease in tumor volume in the SeTT group (p< 0.001) and an increase in LC3 expression in the TT (p = 0.006) and SeTT (p = 0.04) groups. Moreover, a significant decrease in Bcl-2 expression was observed in the ST group (p = 0.01). Ingestion of selenium nanoparticles in combination with interval training may exert a synergistic inhibitory effect on tumor growth, with this effect being mediated in part through the changes in Bcl-2 and LC3 genes expression in the tumor tissue.

The PTEN gene, also known as MMAC1 or TEP1, is a tumor suppressor gene. One of the important polymorphisms of this gene is the rs10490920 SNP. The purpose of this study was to determine the PTEN gene expression and its relation to changes in rs10490920 polymorphism in breast cancer. In this study, 40 breast cancer patients and 10 healthy controls were considered. The expression of PTEN and its common polymorphisms in blood samples the participants were investigated using real-time polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP)-PCR techniques. GraphPad and Medclac software were used to analyze the data. The results revealed a significantly decreased PTEN expression in the patients compared with the controls. In the study of polymorphism, it was found that the frequency of normal and mutated homozygous alleles in breast cancer patients was 2% higher than in healthy controls. In fact, samples with the rs10490920 polymorphism in the PTEN gene exhibited increased expression in a MM or mutated state. PTEN gene expression decreases in breast cancer patients. The frequency of IVS4 allele in breast cancer patients is higher than that in the control group.

Breast cancer greatly affects the life expectancy of patients. Since social support can greatly improve complications in chronic diseases, this study was conducted to determine the relationship between social support and hope in patients with breast cancer. In this cross-sectional study, 100 women with breast cancer undergoing chemotherapy in hospitals of Ahvaz Jundishapur University of Medical Sciences were studied. Samples were selected using convenience sampling and according to inclusion criteria. After collecting data using Snyder’s Adult Trait Hope Scale and Sherburne and Stewart’s Social Support Scale, data analysis was done using the SPSS software. According to the findings, 80% of the studied women reported high levels of social support and 53% had moderate levels of life hope. The findings also showed that the participants received the most support in the emotional dimension (30.83 ± 31.40), the least support in the social interaction dimension (11.79 ± 3.60). Findings showed a direct and significant relationship between hope and social support (r = 0.278, P = 0.004). Strengthening and promoting social support in women with breast cancer can be a good way to create hope for life and thus better tolerance of and compromise with problems associated with the disease and its treatments.

Breast cancer is the most common cancer among women in the world. The automatic detection of masses in digital mammograms is a challenging task and a major step in the development of breast cancer CAD systems. In this study, we introduce a new method for automatic detection of suspicious mass candidate (SMC) regions in a mammogram. Mammography is widely used for the early detection and diagnosis of breast cancer. Extracting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this study, we propose a new method for ROI detection in mammography images. After preprocessing the mammogram, an aggregation of discrete wavelet coefficients based on the lifting scheme and the texture characteristics of the mammogram was created. Then, the coefficients were optimized through noise removal and morphological operations, and a canny edge detector was used to segment the mammogram. Finally, to overcome the problem of over segmentation or under segmentation, reduce the false-negative rate, and enhance the detected regions, we used splitting and merging method. The proposed method was evaluated using images from the DDSM database. Sensitivity, , , and FPI were calculated to be 100%, 86.5%, 56%, and 5.4, respectively. Experimental results indicate that the proposed method is able to detect and identify the abnormal regions of the mammogram that are candidates for breast masses. This technique could potentially improve the performance of CAD systems and diagnosis accuracy in mammograms and can be useful for medical staff and students.