Influences of Antroquinonol and 4-Acetylantroquinonol B on Inflammatory Tumorigenesis in the MCF-7 Breast Cancer Cell Line with or without TNF-α Stimulation

Breast cancer (BC) is one of the most common cancers among women worldwide that ~25% of new cancer cases diagnosed every year would be BC; moreover, ~15% of cancer deaths per year caused by BC makes it the leading cause of cancer death among women worldwide. To date, though the cause of a large proportion of BC are still unclear, recent studies have revealed that a supportive breast tissue microenvironment is critical for the development and progression of BC, especially the communication with immune cells within breast tissue. Therefore, breast inflammatory microenvironment is currently received a substantial attention in the prevention and treatment of BC. Research on breast cancer immunology suggests that inflammatory mediators, estrogen and several inflammation-related tumorigenic pathways are potentially contributors for inflammatory breast tumorigenesis. It is evidenced that elevated levels of inflammatory mediators, such as cytokines, chemokines, prostaglandins, and enhanced estrogen production while suffering from chronic inflammation is responsible for not only activating oncogenic pathways, for example NF-κB, STAT3 and Wnt signaling pathways, but also reducing the efficacy of cancer-specific immunity against tumor cells. Accordingly, targeting the chronic inflammatory status in breast tissue has become a promising strategy for breast cancer therapy. Recently, due to the annoying side effects accompanying by traditionally anticancer drugs, there is an increased interest in finding out natural sources to treat BC. Herein, we report that antroquinonol (AQ) and/or 4-acetylantroquinonol B (4-AAQB) isolated from Antrodia Camphorata were able to modulate the expression of several inflammatory mediators, IL-6 and IFN-γ in particular, and downregulate the aromatase expression and Wnt signaling responses induced by inflammatory status. Taken together, the present findings provide new insights into the role of AQ and 4-AAQB in inflammatory breast tumors and also suggest them as promising candidates for breast cancer immunotherapy

Signal Amplification Assisted by Multiple Sideband Interference in 1D Waveguide QED Systems

This study theoretically investigates signal amplification resulting from multiple Rabi sideband coherence in a one-dimensional waveguide quantum electrodynamical system. Specifically, we explore the behavior of a transmon while strongly driven by a coherent microwave field through a semi-infinite waveguide. To understand the underlying mechanisms of amplification, we develop a theory that explicitly takes into account multiple dressed sidebands under a strong driving field, and analyze the reflection amplitude of the probe signal. Our findings show that amplification can be related to either population inversion or multiple sideband constructive interference in some cases without population inversion. We further examine the effect of qubit dephasing during the amplification process

Stimulatory Effect of 5-Hydroxytryptamine (5-HT) on Rat Capsaicin-Sensitive Lung Vagal Sensory Neurons via Activation of 5-HT\u3csub\u3e3\u3c/sub\u3e Receptors

5-hydroxytryptamine (5-HT) is an inflammatory mediator known to be released in lung. Capsaicin-sensitive lung vagal (CSLV) afferents function as a primary sensor for detecting chemical stimuli and produce consequent reflexes during lung inflammation. To characterize the effect of 5-HT on CSLV afferents, responses of cardiorespiratory reflexes and single-unit C-fiber afferents to right-atrial injections of 5-HT were investigated in anesthetized Sprague-Dawley rats. Bolus injection of 5-HT (8 μg/kg) caused an immediate augmented breath and apnea, accompanied by hypotension and bradycardia. These initial responses were then followed by a brief pressor response and a more sustained depressor response. After a perineural treatment of both cervical vagi with capsaicin to block the conduction of C fibers, 5-HT still triggered the augmented breath, but no longer evoked the apnea, bradycardia and hypotension, indicating an involvement of C-fiber activation. The remaining augmented breath induced by 5-HT after perineural capsaicin treatment was totally eliminated by vagotomy. To further study the effect of 5-HT on CSLV afferents, activities arising from these afferents were determined using the single-fiber recording technique. Right-atrial injection of 5-HT evoked an intense discharge in CSLV afferents in a dose-dependent manner. The highest dose of 5-HT (16 μg/kg) activated 79% (19/24) of CSLV afferents which were also sensitive to capsaicin (0.8 μg/kg). The pretreatment of tropisetron, a selective antagonist of the 5-HT3 receptor, completely blocked CSLV-afferents stimulation induced by 5-HT but did not affect that by capsaicin. Furthermore, a similar afferent response of CSLV afferents was mimicked by phenylbiguanide, a selective agonist of the 5-HT3 receptor. In isolated rat lung vagal C neurons, 5-HT induced intense calcium transients in a dose-dependent manner. The highest concentration (3 μM) of 5-HT activated 67% (18/27) of the CSLV neurons. The 5-HT-induced response was totally abolished by pretreatment of tropisetron. In conclusion, 5-HT exerts an intense stimulatory effect on lung C-fiber terminals mediated through an activation of the 5-HT3 receptor, which may contribute to the airway hypersensitivity under lung inflammation

Combining radiofrequency ablation and ethanol injection may achieve comparable long-term outcomes in larger hepatocellular carcinoma (3.1–4 cm) and in high-risk locations

AbstractRadiofrequency ablation (RFA) is more effective for hepatocellular carcinoma (HCC) < 3 cm. Combining percutaneous ethanol injection and RFA for HCC can increase ablation; however, the long-term outcome remains unknown. The aim of this study was to compare long-term outcomes between patients with HCC of 2–3 cm versus 3.1–4 cm and in high-risk versus non-high-risk locations after combination therapy. The primary endpoint was overall survival and the secondary endpoint was local tumor progression (LTP). Fifty-four consecutive patients with 72 tumors were enrolled. Twenty-two (30.6%) tumors and 60 (83.3%) tumors were of 3.1–4 cm and in high-risk locations, respectively. Primary technique effectiveness was comparable between HCC of 2–3 cm versus 3.1–4 cm (98% vs. 95.5%, p = 0.521), and HCC in non-high risk and high-risk locations (100% vs. 96.7%, p = 1.000). The cumulative survival rates at 1 year, 3 years, and 5 years were 90.3%, 78.9%, and 60.3%, respectively, in patients with HCC of 2–3 cm; 95.0%, 84.4%, and 69.3% in HCC of 3.1–4.0 cm (p = 0.397); 90.0%, 71.1%, and 71.1% in patients with HCC in non-high-risk locations; and 92.7%, 81.6%, and 65.4% in high-risk locations (p = 0.979). The cumulative LTP rates at 1 year, 3 years, and 5 years were 10.2%, 32.6%, and 32.6%, respectively, in all HCCs; 12.6%, 33.9%, and 33.9% in HCC of 2–3 cm; 4.8%, 29.5%, and 29.5% in HCC of 3.1–4 cm (p = 0.616); 16.7%, 50.0%, and 50.0% in patients with HCC in non-high-risk locations; and 8.8%, 29.9%, and 29.9% in patients with HCC in high-risk locations (p = 0.283). The cumulative survival and LTP rates were not significantly different among the various subgroups. Combining RFA and percutaneous ethanol injection achieved comparable long-term outcomes in HCCs of 2–3 cm versus 3.1–4.0 cm and in high-risk versus non-high-risk locations. A randomized controlled or cohort studies with larger sample size are warranted

Geometrically Local Quantum and Classical Codes from Subdivision

A geometrically local quantum code is an error correcting code situated within $\mathbb{R}^D$, where the checks only act on qubits within a fixed spatial distance. The main question is: What is the optimal dimension and distance for a geometrically local code? This question was recently answered by Portnoy which constructed codes with optimal dimension and distance up to polylogs. This paper extends Portnoy's work by constructing a code which additionally has an optimal energy barrier up to polylogs. The key ingredient is a simpler code construction obtained by subdividing the balanced product codes. We also discuss applications to classical codes

Progressive amorphization of GeSbTe phase-change material under electron beam irradiation

Fast and reversible phase transitions in chalcogenide phase-change materials (PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental interests, but also make PCMs based random access memory (PRAM) a leading candidate for non-volatile memory and neuromorphic computing devices. To RESET the memory cell, crystalline Ge-Sb-Te has to undergo phase transitions firstly to a liquid state and then to an amorphous state, corresponding to an abrupt change in electrical resistance. In this work, we demonstrate a progressive amorphization process in GeSb2Te4 thin films under electron beam irradiation on transmission electron microscope (TEM). Melting is shown to be completely absent by the in situ TEM experiments. The progressive amorphization process resembles closely the cumulative crystallization process that accompanies a continuous change in electrical resistance. Our work suggests that if displacement forces can be implemented properly, it should be possible to emulate symmetric neuronal dynamics by using PCMs

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training