Diagnostic values of serum levels of pepsinogens and gastrin-17 for screening gastritis and gastric cancer in a high risk area in Northern Iran

Background: Gastric cancer (GC) is the second cause of cancer related death in the world. It may develop by progression from its precancerous condition, called gastric atrophy (GA) due to gastritis. The aim of this study was to evaluate the accuracy of serum levels of pepsinogens (Pg) and gastrin-17 (G17) as non-invasive methods to discriminate GA or GC (GA/GC) patients. Materials and Methods: Subjects referred to gastrointestinal clinics of Golestan province of Iran during 2010 and 2011 were invited to participate. Serum levels of PgI, PgII and G17 were measured using a GastroPanel kit. Based on the pathological examination of endoscopic biopsy samples, subjects were classified into four groups: normal, non-atrophic gastritis, GA, and GC. Receiver operating curve (ROC) analysis was used to determine cut-off values. Indices of validity were calculated for serum markers. Results: Study groups were normal individuals (n=74), non-atrophic gastritis (n=90), GA (n=31) and GC patients (n=30). The best cut-off points for PgI, PgI/II ratio, G17 and HP were 80 μg/L, 10, 6 pmol/L, and 20 EIU, respectively. PgI could differentiate GA/GC with high accuracy (AUC=0.83; 95%CI: 0.76-0.89). The accuracy of a combination of PgI and PgI/II ratio for detecting GA/GC was also relatively high (AUC=0.78; 95%CI: 0.70-0.86). Conclusions: Our findings suggested PgI alone as well as a combination of PgI and PgI/II ratio are valid markers to differentiate GA/GC. Therefore, Pgs may be considered in conducting GC screening programs in high-risk areas

Centrosymmetric graphs and a lower bound for graph energy of fullerenes

The energy of a molecular graph G is defined as the summation of the absolute values of the eigenvalues of adjacency matrix of a graph G. In this paper, an infinite class of fullerene graphs with 10n vertices, n ≥ 2, is considered. By proving centrosymmetricity of the adjacency matrix of these fullerene graphs, a lower bound for its energy is given. Our method is general and can be extended to other class of fullerene graphs

Recurrent neural network channel estimation using measured massive MIMO data

In this work, we develop a novel channel estimation method using recurrent neural networks (RNNs) for massive multiple-input multiple-output (MIMO) systems. The proposed framework alleviates the need for channel-state-information (CSI) feedback and pilot assignment through exploiting the inherent time and frequency correlations in practical propagation environments. We carry out the analysis using empirical MIMO channel measurements between a 64T64R active antenna system and a state-of-the-art multi-antenna scanner for both mobile and stationary use-cases. We also capture and analyze similar MIMO channel data from a legacy 2T2R base station (BS) for comparison purposes. Our findings confirm the applicability of utilising the proposed RNN-based massive MIMO channel acquisition scheme particularly for channels with long time coherence and hardening effects. In our practical setup, the proposed method reduced the number of pilots used by 25%

Deep Learning-Based Decision Region for MIMO Detection

In this work, a deep learning-based symbol detection method is developed for multi-user multiple-input multiple-output (MIMO) systems. We demonstrate that the linear threshold-based detection methods, which were designed for AWGN channels, are suboptimal in the context of MIMO fading channels. Furthermore, we propose a MIMO detection framework which replaces the linear thresholds with decision boundaries trained with neural network (NN) classifiers. The symbol error rate (SER) performance of the proposed detection model is compared against conventional methods under state-of-the-art system parameters. Here, we report to up to a 2 dB gain in SER performance using the proposed NN classifiers, allowing for exploiting higher-order modulation schemes, or transmitting with reduced power. The underlying gain in performance may be further enhanced from improvements to the NN architecture and hyper-parameter optimization

Evaluation of urban infrastructure on the basis of architectural design and landscape ecology

Ideology for landscape Ecology design must be maintained during the design of urban infrastructure and landscape ecology infrastructure can improve the quality of urban environment. Cities and biological complexes are the outcome of human interaction process in various aspects of social, economic, technological, etc., today, with the increasing development of knowledge; the human manipulation in the environment is increasing. And so humans by destroying the natural resources should face several problems, and that’s why different specialties are arise in order to solve the problems. Landscape architecture, including expertise that despite the long history of its use in human life has not much experience in academia environments as an independent field of study. Achievements of this specialty Activities, since it is rooted in nature by enjoying its talent in various aspects such as social issues, ecological and environmental role and could have a strong position for the city dwellers. This article due to needs of ecology design is focused on the effects of infrastructure production in terms of performance, the structure and materials, etc. And creating reasonable rules for ecological planning and landscape architecture in order to establish the foundations for the future development of ecology design methods for infrastructure.Keywords: landscape architecture; ecology; urban infrastructure

Centrosymmetric graphs and a lower bound for graph energy of fullerenes

The energy of a molecular graph G is defined as the summation of the absolute values of the eigenvalues of adjacency matrix of a graph G. In this paper, an infinite class of fullerene graphs with 10n vertices, n ≥ 2, is considered. By proving centrosymmetricity of the adjacency matrix of these fullerene graphs, a lower bound for its energy is given. Our method is general and can be extended to other class of fullerene graphs

Preserving p-conjugation in covalently functionalized carbon nanotubes for optoelectronic applications

Covalent functionalization tailors carbon nanotubes for a wide range of applications in varying environments. Its strength and stability of attachment come at the price of degrading the carbon nanotubes sp 2 network and destroying the tubes electronic and optoelectronic features. Here we present a non-destructive, covalent, gram-scale functionalization of single-walled carbon nanotubes by a new 2+1] cycloaddition. The reaction rebuilds the extended p-network, thereby retaining the outstanding quantum optoelectronic properties of carbon nanotubes, including bright light emission at high degree of functionalization (1 group per 25 carbon atoms). The conjugation method described here opens the way for advanced tailoring nanotubes as demonstrated for light-triggered reversible doping through photochromic molecular switches and nanoplasmonic gold-nanotube hybrids with enhanced infrared light emission

Investigation of the Relationship between Serum Leptin levels and Nausea and Vomiting of Pregnancy

Background: Worldwide, half of women suffer from nausea and vomiting in early pregnancy which generally continues to the 20th week of pregnancy. Although pathogeneses of nausea and vomiting of pregnancy as well as hyperemesis gravid arum are still unknown, some believe that nausea and vomiting of pregnancy is likely related to maternal serum leptin level. Objectives: This study aimed to examine the relationship between leptin and pregnancy nausea and vomiting. Methods: In this case-control study, 45 pregnant women at first and second trimesters were selected through convenient sampling. Mothers’ blood samples were taken in the 6th, 12th, 15th, and 20th weeks of pregnancy. The participants were devised into healthy, without nausea, (24) and with nausea and vomiting groups (21). The relationship among the variables was analyzed using independent t-test, Pearson correlation, regression tests, and Lambda statistic (P value <0.05). Results: The mean age of the participants was 27.47±5.55 years, and Body Mass Index (BMI) was found to be 5.458±26.57. There was no significant difference between groups in this regard. Based on results, changes in maternal serum leptin had significant correlation with nausea and vomiting of pregnancy (p<0.04), meaning that the mean of leptin changes in patients with nausea and vomiting was significantly lower. Moreover, serum leptin at first and second trimesters of pregnancy did not have significant correlation with nausea and vomiting (p=0.5 and 0.3, respectively). Conclusion: With regard to leptin peak level at second trimester of pregnancy, leptin changes at first and second trimesters can be a good index to predict the nausea and vomiting of pregnancy. Thus, further domestic studies are required in this respect

Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence

IntroductionDual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence.MethodsWe collected a sample of positive and negative DECTs, reviewed twice—once with and once without the DL tool—with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed.ResultsWe included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL.ConclusionsThe implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model

Isoreticular two-dimensional magnetic coordination polymers prepared through pre-synthetic ligand functionalization

Chemical functionalization is a powerful approach to tailor the physical and chemical properties of two-dimensional materials, increase their processability and stability, tune their functionalities and, even, create new 2D materials. This is typically achieved through post-synthetic functionalization by anchoring molecules on the surface of an exfoliated 2D crystal, but it inevitably alters the long-range structural order of the material. Here we present a pre-synthetic approach that allows the isolation of crystalline, robust, and magnetic functionalized monolayers of coordination polymers. A series of five isoreticular layered magnetic coordination polymers based on Fe(II) centres and different benzimidazole derivatives (bearing a Cl, H, CH3, Br or NH2 side group) were first prepared. On mechanical exfoliation, 2D materials are obtained that retain their long-range structural order and exhibit good mechanical and magnetic properties. This combination, together with the possibility to functionalize their surface at will, makes them good candidates to explore magnetism in the 2D limit and to fabricate mechanical resonators for selective gas sensing