The Possible Impact of Obesity on Androgen, Progesterone and Estrogen Receptors (ERα and ERβ) Gene Expression in Breast Cancer Patients

Background Obesity has been associated with increased mortality from hormone dependant cancers such as breast cancer which is the most prevalent cancer in women. The link between obesity and breast cancer can be attributed to excess estrogen produced through aromatization in adipose tissue. The role of steroid hormone receptors in breast cancer development is well studied but how obesity can affect the expression pattern of steroid hormones in patients with different grades of breast cancer was the aim of this study. Methods In this case-control study, 70 women with breast cancer participated with different grades of obesity (36 none obese, BMI < 25 kg/m 2 and 34 obese, BMI ≥ 25 kg/m 2 ). The mean age of participants was 44.53 ± 1.79 yr (21–70 yr). The serum level of estrogen, progesterone and androgen determined by ELISA. Following quantitative expression of steroid hormone receptors mRNA in tumor tissues evaluated by Real-time PCR. Patients with previous history of radiotherapy or chemotherapy were excluded. SPSS 16 was used for data analysis and P < 0.05 considered statistically significant. Results The difference in ERα, ERβ and PR mRNA level between normal and obese patients was significant ( P < 0.001). In addition, the expression of AR mRNA was found to be higher than other steroid receptors. There was no significant relation between ERβ gene expression in two groups ( P = 0.68). We observed a significant relationship between ERα and AR mRNA with tumor stage and tumor grade, respectively ( P = 0.023, P = 0.015). Conclusion According to the obtained results, it is speculated that obesity could paly a significant role in estrogen receptors gene expression and also could affect progression and proliferation of breast cancer cells

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70�75. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80�98, but similar accuracy of 70. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95 compared to radiologists (70). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership. © 2021, The Author(s)

Performance study of dimensionality reduction methods for metrology of nonrigid mechanical parts

The geometric measurement of parts using a coordinate measuring machine (CMM) has been generally adapted to the advanced automotive and aerospace industries. However, for the geometric inspection of deformable free-form parts, special inspection fixtures, in combination with CMM’s and/or optical data acquisition devices (scanners), are used. As a result, the geometric inspection of flexible parts is a consuming process in terms of time and money. The general procedure to eliminate the use of inspection fixtures based on distance preserving nonlinear dimensionality reduction (NLDR) technique was developed in our previous works. We sought out geometric properties that are invariant to inelastic deformations. In this paper we will only present a systematic comparison of some well-known dimensionality reduction techniques in order to evaluate their accuracy and potential for non-rigid metrology. We will demonstrate that even though these techniques may provide acceptable results through artificial data on certain fields like pattern recognition and machine learning, this performance cannot be extended to all real engineering metrology problems where high accuracy is needed

The Effect of Nitrogen and Zinc Levels on Essential Oil Yield and some Morphological Traits of Hypericum perforatums

To study the effects of nitrogen and zinc fertilizer on the morphological traits and essential oil yield of St. John’s wort (Hypericum perforatum) a greenhouse experiment in a factorial randomized complete block design with three replications was conducted at University of Tabriz, Iran in 2012. Treatments consisted of three levels of zinc sulphate with a concentration of zinc fertilizer (zero, 3 and 6 parts per thousand) and four levels of nitrogen fertilizer (zero, 50, 100, 150 kg/ha). One half of the fertilizers were applied 20 days after planting of plants and the rest 40 days after transplanting. Traits evaluated were plant height, inflorescence number, leaf area, plant fresh and dry weights and plant essential oil content. The results showed that the traits under study were affected by rate of fertilizer applications. Highest plant height, number of inflorescences, leaf area and essential oil yield were obtained by using 150 kg/ha of nitrogen and applying zinc with 0.006 concentration. Highest fresh and dry weights of above ground parts were also produced by using 150 kg/ha of nitrogen fertilizer along with zinc fertilizer 0.003