FLI1 is a member of ETS family of transcription factors that regulate a variety of normal biologic activities including cell proliferation, differentiation, and apoptosis. The expression of FLI1 and its correlation with well-known breast cancer prognostic markers (ER, PR and HER2) was determined in primary breast tumors as well as four breast cancer lines including: MCF-7, T47D, MDA-MB-231 and MDA-MB-468 using RT-qPCR with either 18S rRNA or ACTB (β-actin) for normalization of data. FLI1 mRNA level was decreased in the breast cancer cell lines under study compared to the normal breast tissue; however, Jurkat cells, which were used as a positive control, showed overexpression compared to the normal breast. Regarding primary breast carcinoma

Background: The physiologic, biochemical and anatomic changes that occur during pregnancy are extensive and may be systemic or local. However, most of these changes return to pre pregnancy status six weeks postpartum. The aim of the study was to investigate the effect of dental caries among preterm postpartum women and it's relation to baby birth weight and salivary interleukin-6 (IL-6). Materials and methods: 66 postpartum women were examined, 33 preterm postpartum women (study group) and 33 full term postpartum women (control group). Dental caries was recorded using, decayed, missing and filled surfaces index, also assess the decayed lesion by severity. Salivary samples were taken from all subjects to estimate salivary IL-6 levels. Babie

TNF-α-induced osteoclastogenesis is central to post-menopausal and inflammatory bone loss, however, the effect of phytoestrogens on TNF-α-induced bone resorption has not been studied. The phytoestrogens genistein, daidzein, and coumestrol directly suppressed TNF-α-induced osteoclastogenesis and bone resorption. TRAP positive osteoclast formation and resorption area were significantly reduced by genistein (10(-7) M), daidzein (10(-5) M), and coumestrol (10(-7) M), which was prevented by the estrogen antagonist ICI 182,780. TRAP expression in mature TNF-α-induced osteoclasts was also significantly reduced by these phytoestrogen concentrations. In addition, in the presence of ICI 182,780 genistein and coumestrol (10(-5) -10(-6) M) augmente

Activation of farnesoid X receptor (FXR) markedly attenuates development of atherosclerosis in animal models. However, the underlying mechanism is not well elucidated. Here, we show that the FXR agonist, obeticholic acid (OCA), increases fecal cholesterol excretion and macrophage reverse cholesterol transport (RCT) dependent on activation of hepatic FXR. OCA does not increase biliary cholesterol secretion, but inhibits intestinal cholesterol absorption. OCA markedly inhibits hepatic cholesterol 7α‐hydroxylase (Cyp7a1) and sterol 12α‐hydroxylase (Cyp8b1) partly through inducing small heterodimer partner, leading to reduced bile acid pool size and al

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte