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

Melissa officinalis L. ethanolic extract inhibits the growth of a lung cancer cell line by interfering with the cell cycle and inducing apoptosis

Melissa officinalis is a plant from the family Lamiaceae, native in Europe particularly in the Mediterranean region. Given our interest in identifying extracts and compounds capable of inhibiting tumor cell growth, and given the antioxidant content and the high consumption of Melissa officinalis in Portugal, this study aimed to test the tumor cell growth inhibitory activity of five different extracts of this plant (aqueous, methanolic, ethanolic, hydromethanolic and hydroethanolic) in three human tumor cell lines: MCF-7, AGS and NCI-H460. All extracts decreased cell growth in all cell lines in a concentration-dependent manner. The ethanolic extract was the most potent one, presenting a GI50 concentration of approximately 100.9 μg mL−1 in the NCI-H460 lung cancer cells. This extract was characterized by LC-DAD-ESI/MS regarding its phenolic composition, revealing rosmarinic acid as the most abundant compound. The GI75 concentration of this extract affected the cell cycle profile of these cells. In addition, both the GI50 and the GI75 concentrations of the extract induced cellular apoptosis. Moreover, treatment of NCI-H460 cells with this extract caused a decrease in pro-caspase 3 and an increase in p53 levels. This study emphasizes the relevance of the study of natural products as inhibitors of tumor cell growth.This work was financed by the FEDER - Fundo Europeu de Desenvolvimento Regional through the COMPETE 2020 – Operational Programme for Competitiveness and Internationalisation (POCI), Portugal 2020, and by Portuguese funds through FCT – Fundação para a Ciência e Tecnologia/ Ministério da Ciência, Tecnologia e Inovação in the framework of the project “Institute for Research and Innovation in Health Sciences” (POCI-01-0145-FEDER-007274). The authors are also grateful to FCT and FEDER under Programme PT2020 for financial support to CIMO (UID/AGR/00690/2013) and L. Barros contract; and to FEDER-Interreg España-Portugal programme for financial support through the project 0377_Iberphenol_6_E.info:eu-repo/semantics/publishedVersio

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)

Antioxidants in different parts of oleaster as a function of genotype

Introduction: Fruits of oleaster (Elaeagnus angustifolia L.) were used in traditional medicine to treat various diseases. The aim of this study was to evaluate and compare the phenol and flavonoid contents and antioxidant activity of methanol extracts from the fruit peel, flesh and seed of seven genotypes of oleaster. Methods: The phenol and flavonoid contents were determined using spectrophotometric methods. Antioxidant and antiradical activities were determined using reducing power, ferric-reducing antioxidant potential (FRAP) and ability to scavenge DPPH radical assays. Results: Significant differences (P ˂ 0.05) were found in phenol and flavonoid contents and antioxidant activity among components of fruit and between various genotypes. Conclusion: Results indicated that oleaster has good fruit quality varying among different genotypes. Seeds of fruits have excellent antioxidant activity and phenolic contents in comparison to flesh and peel

Exploring the interactions between caffeic acid and human serum albumin using spectroscopic and molecular docking techniques

Ultraviolet-visible (UV–Vis) and fluorescence spectroscopy along with molecular docking were used to explore the interaction between human serum albumin (HSA) and caffeic acid (CA). CA is one of the major representatives of hydroxycinnamic acids in plants and is commonly present in plant-based foods. The mechanism by which CA quenched HSA fluorescence was determined to be static, and the values obtained for thermodynamic parameters indicated that the CA and HSA interaction was spontaneous. Hydrogen bonds and van der Waals forces were the main driving forces stabilizing the complex. The binding constant was in the order of 104/M and the number of binding sites for CA on HSA was calculated to be close to one. The results of fluorescence and UV–Vis spectroscopy showed that CA induced conformational changes in HSA structure. The distance of CA and the tryptophan residue of HSA, was determined to be ~2 nm by using Forster resonance energy transfer theory. The mode of binding and the binding site of CA on albumin were examined by performing molecular docking calculations. CA interacted with albumin in subdomain IA, and non–covalent interactions stabilized the complex. CA showed a high affinity for albumin, and thus this phenolic compound would be distributed in the body upon interacting with HS