Excellent diagnostic characteristics for ultrafast gene profiling of DEFA1-IL1B-LTF in detection of prosthetic joint infections

The timely and exact diagnosis of prosthetic joint infection (PJI) is crucial for surgical decision-making. Intraoperatively, delivery of the result within an hour is required. Alpha-defensin lateral immunoassay of joint fluid (JF) is precise for the intraoperative exclusion of PJI; however, for patients with a limited amount of JF and/or in cases where the JF is bloody, this test is unhelpful. Important information is hidden in periprosthetic tissues that may much better reflect the current status of implant pathology. We therefore investigated the utility of the gene expression patterns of 12 candidate genes (TLR1, -2, -4, -6, and 10, DEFA1, LTF, IL1B, BPI, CRP, IFNG, and DEFB4A) previously associated with infection for detection of PJI in periprosthetic tissues of patients with total joint arthroplasty (TJA) (n = 76) reoperated for PJI (n = 38) or aseptic failure (n = 38), using the ultrafast quantitative reverse transcription-PCR (RT-PCR) Xxpress system (BJS Biotechnologies Ltd.). Advanced data-mining algorithms were applied for data analysis. For PJI, we detected elevated mRNA expression levels of DEFA1 (P < 0.0001), IL1B (P < 0.0001), LTF (P < 0.0001), TLR1 (P = 0.02), and BPI (P = 0.01) in comparison to those in tissues from aseptic cases. A feature selection algorithm revealed that the DEFA1-IL1B-LTF pattern was the most appropriate for detection/exclusion of PJI, achieving 94.5% sensitivity and 95.7% specificity, with likelihood ratios (LRs) for positive and negative results of 16.3 and 0.06, respectively. Taken together, the results show that DEFA1-IL1B-LTF gene expression detection by use of ultrafast qRT-PCR linked to an electronic calculator allows detection of patients with a high probability of PJI within 45 min after sampling. Further testing on a larger cohort of patients is needed.Web of Science5592697268

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process

A mathematical model of quorum sensing regulated EPS production in biofilm communities

<p>Abstract</p> <p>Background</p> <p>Biofilms are microbial communities encased in a layer of extracellular polymeric substances (EPS). The EPS matrix provides several functional purposes for the biofilm, such as protecting bacteria from environmental stresses, and providing mechanical stability. Quorum sensing is a cell-cell communication mechanism used by several bacterial taxa to coordinate gene expression and behaviour in groups, based on population densities.</p> <p>Model</p> <p>We mathematically model quorum sensing and EPS production in a growing biofilm under various environmental conditions, to study how a developing biofilm impacts quorum sensing, and conversely, how a biofilm is affected by quorum sensing-regulated EPS production. We investigate circumstances when using quorum-sensing regulated EPS production is a beneficial strategy for biofilm cells.</p> <p>Results</p> <p>We find that biofilms that use quorum sensing to induce increased EPS production do not obtain the high cell populations of low-EPS producers, but can rapidly increase their volume to parallel high-EPS producers. Quorum sensing-induced EPS production allows a biofilm to switch behaviours, from a colonization mode (with an optimized growth rate), to a protection mode.</p> <p>Conclusions</p> <p>A biofilm will benefit from using quorum sensing-induced EPS production if bacteria cells have the objective of acquiring a thick, protective layer of EPS, or if they wish to clog their environment with biomass as a means of securing nutrient supply and outcompeting other colonies in the channel, of their own or a different species.</p

Pattern recognition for bivariate process mean shifts using feature-based artificial neural network

In multivariate quality control, the artificial neural networks (ANN)-based pattern recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charting. However, these schemes revealed disadvantages in term of reference bivariate patterns in identifying the joint effect and excess false alarms in identifying stable process condition. In this study, feature-based ANN scheme was investigated for recognizing bivariate correlated patterns. Feature-based input representation was utilized into an ANN training and testing towards strengthening discrimination capability between bivariate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate patterns, the proposed scheme promotes a smaller network size and better monitoring capability as compared to the raw data-based ANN scheme