Diagnostic and prognostic role of TFF3, Romo-1, NF-кB and SFRP4 as biomarkers for endometrial and ovarian cancers: a prospective observational translational study

Purpose: This study aimed to evaluate trefoil factor 3 (TFF3), secreted frizzled-related protein 4 (sFRP4), reactive oxygen species modulator 1 (Romo1) and nuclear factor kappa B (NF-κB) as diagnostic and prognostic markers of endometrial cancer (EC) and ovarian cancer (OC). Methods: Thirty-one patients with EC and 30 patients with OC undergone surgical treatment were enrolled together with 30 healthy controls in a prospective study. Commercial ELISA kits determined serum TFF-3, Romo-1, NF-кB and sFRP-4 concentrations. Results: Serum TFF-3, Romo-1 and NF-кB levels were significantly higher in patients with EC and OC than those without cancer. Regarding EC, none of the serum biomarkers differs significantly between endometrial and non-endometrioid endometrial carcinomas. Mean serum TFF-3 and NF-кB levels were significantly higher in advanced stages. Increased serum levels of TFF-3 and NF-кB were found in those with a higher grade of the disease. Regarding OC, none of the serum biomarkers differed significantly among histological subtypes. Significantly increased serum levels of NF-кB were observed in patients with advanced-stage OC than those with stage I and II diseases. No difference in serum biomarker levels was found between those who had a recurrence and those who had not. The sensibility and specificity of these four biomarkers in discriminating EC and OC from the control group showed encouraging values, although no one reached 70%. Conclusions: TFF-3, Romo-1, NF-кB and SFRP4 could represent new diagnostic and prognostic markers for OC and EC. Further studies are needed to validate our results

Self-stabilizing algorithms for Connected Vertex Cover and Clique decomposition problems

In many wireless networks, there is no fixed physical backbone nor centralized network management. The nodes of such a network have to self-organize in order to maintain a virtual backbone used to route messages. Moreover, any node of the network can be a priori at the origin of a malicious attack. Thus, in one hand the backbone must be fault-tolerant and in other hand it can be useful to monitor all network communications to identify an attack as soon as possible. We are interested in the minimum \emph{Connected Vertex Cover} problem, a generalization of the classical minimum Vertex Cover problem, which allows to obtain a connected backbone. Recently, Delbot et al.~\cite{DelbotLP13} proposed a new centralized algorithm with a constant approximation ratio of $2$ for this problem. In this paper, we propose a distributed and self-stabilizing version of their algorithm with the same approximation guarantee. To the best knowledge of the authors, it is the first distributed and fault-tolerant algorithm for this problem. The approach followed to solve the considered problem is based on the construction of a connected minimal clique partition. Therefore, we also design the first distributed self-stabilizing algorithm for this problem, which is of independent interest

ABCS antenna for wireless body area network at 26 GHz

Abstract The paper presents the design and investigation of a wearable textile antenna (receiver) and transmitter antenna operating in the wireless body area network (WBAN) of 26 GHz band for 5G mobile networks. The wearable antenna with an overall size of 30 mm x 40 mm x 1.26 mm achieves good impedance matching, high gain, and directive radiation pattern. Both antennas were designed using CST Microwave Studio to validate the simulation results. A rectangular radiating patch comprises a Shieldit electrotextile situated on one side of a non-conductive substrate panel with the ground plane. The bed sheet cotton fabric is used as the non-conductive substrate due to its widespread use in daily clothing with a dielectric constant is 3.2 and the loss tangent is 0.0027. In addition, the wearable antenna successfully achieved the high gain and efficiency of 12 dB and 90.83% respectively. Moreover, the antenna operating at 26 GHz with -40.48 dB return loss, which is less than the -10 dB in requirement. The simulated results show that this proposed wearable antenna is best suited for wireless body area network applications. Hence, the wearable antenna is simple, compact and easy to fabricate

Mini double ridge horn antenna for free space measurement

Abstract This paper presents the design of a double ridge horn antenna (DRHA) with an operating frequency of 2.4 GHz to 9.8 GHz for free space measurement. The DRHA is designed using CST Studio Suite. The DRHA is built with metallic grid sidewalls, ridges, the shield of coax, cavity back, and bell section. Furthermore, the DRHA exhibits improved radiation patterns with a maximum E-plane beamwidth of 102 degrees and H-plane beamwidth of 92 degrees, and maximum gain up to 16.3 dBi. The simulated results were analyzed and discussed in this paper. This double ridge horn antenna exhibits improved radiation patterns and gains. This shows that the double ridge horn antenna can fulfill the higher demands in antenna applications and in free space material measurement. The antenna presents desirable results throughout the operating frequency

Microstrip sensor based on ring resonator coupled with double square split ring resonator for solid material permittivity characterization

Abstract This paper analyzes a microwave resonator sensor based on a square split-ring resonator operating at 5.122 GHz for permittivity characterization of a material under test (MUT). A single-ring square resonator edge (S-SRR) is coupled with several double-split square ring resonators to form the structure (D-SRR). The function of the S-SRR is to generate a resonant at the center frequency, whereas D-SRRs function as sensors, with their resonant frequency being very sensitive to changes in the MUT’s permittivity. In a traditional S-SRR, a gap emerges between the ring and the feed line to improve the Q-factor, but the loss increases as a result of the mismatched coupling of the feed lines. To provide adequate matching, the microstrip feed line is directly connected to the single-ring resonator in this article. The S-SRR’s operation switches from passband to stopband by generating edge coupling with dual D-SRRs located vertically on both sides of the S-SRR. The proposed sensor was designed, fabricated, and tested to effectively identify the dielectric properties of three MUTs (Taconic-TLY5, Rogers 4003C, and FR4) by measuring the microwave sensor’s resonant frequency. When the MUT is applied to the structure, the measured findings indicate a change in resonance frequency. The primary constraint of the sensor is that it can only be modeled for materials with a permittivity ranging from 1.0 to 5.0. The proposed sensors’ acceptable performance was achieved through simulation and measurement in this paper. Although the simulated and measured resonance frequencies have shifted, mathematical models have been developed to minimize the difference and obtain greater accuracy with a sensitivity of 3.27. Hence, resonance sensors offer a mechanism for characterizing the dielectric characteristics of varied permittivity of solid materials

A free-space measurement system for microwave materials at Kuband

Abstract One of the non-resonant techniques is the free-space measurement technique, which is popular due to its many advantages compared to the other techniques. It allows the transmission and reflection measurements without any physical contact with the sample. This paper discusses the free-space material measurement system in Ku-band which uses the NRW algorithm and Keysight (Formerly Agilent) 85071E software in determining the dielectric properties of materials. The permittivity and permeability of Teflon, FR4, PVC, ABS, Acrylic, polypropylene, polycarbonate, and epoxy were determined using free space measurement setup. For the first, a free-space measurement for Ku-Band is setup. It consists of a vector network analyzer, two horn antennas, sample holder, and Keysight 85071E software. The different role of transmission and reflection measurements on the achievable results is analyzed about experimental uncertainties and different noise scenarios. Results from the two strategies are analyzed and compared. Good agreement between simulation, measurement, and literature was obtained

Time-frequency methodologies in neurosciences

This chapter presents a number of time-frequency (t,f) techniques that can provide advanced solutions to several problems in neuro-sciences with focus on the monitoring of brain abnormalities using EEG and other physiological modalities (t,f) characteristics as a diagnosis and prognosis tool. The methods presented illustrate the improved performance obtained by using a time-frequency approach to process EEG data, including a focus on detecting abnormalities in sick newborns in a Neonatal Intensive Care Unit (NICU) as well as mental health issues in elderlies. The chapter starts by presenting methods for the assessment of Newborn EEG and ECG abnormalities using a time-frequency identification approach (Section 16.1). Next, the important question of (t,f) modeling of nonstationary signals is discussed with illustration on newborn EEGs (Section 16.2); Then, the use of (t,f) features for nonstationary signal classification is illustrated on an application to newborn EEG burst-suppression detection (Section 16.3); an application relevant to the elderly is described where a time-varying analysis of brain networks uses the EEG for the detection of Alzheimer disease (Section 16.4). Another method of time-frequency analysis is described that involves EEG noise reduction using the empirical mode decomposition(Section 16.5). Finally the chapter concludes with a discussion on other perspectives of using advanced (t,f) methods for medical diagnosis and prognosis in other areas of neurosciences (Section 16.6).Scopu