Explore the vast range of titles available.

At present, anti-virulence drugs are being considered as potential therapeutic alternatives and/or adjuvants to currently failing antibiotics. These drugs do not kill bacteria but inhibit virulence factors essential for establishing infection and pathogenesis through targeting non-essential metabolic pathways reducing the selective pressure to develop resistance. We investigated the effect of naturally isolated plant compounds on the repression of the quorum sensing (QS) system which is linked to virulence/pathogenicity in Pseudomonas aeruginosa . Our results show that trans -cinnamaldehyde (CA) and salicylic acid (SA) significantly inhibit expression of QS regulatory and virulence genes in P. aeruginosa PAO1 at sub-inhibitory levels without any bactericidal effect. CA effectively downregulated both the las and rhl QS systems with lasI and lasR levels inhibited by 13- and 7-fold respectively compared to 3- and 2-fold reductions with SA treatment, during the stationary growth phase. The QS inhibitors (QSI) also reduced the production of extracellular virulence factors with CA reducing protease, elastase and pyocyanin by 65%, 22% and 32%, respectively. The QSIs significantly reduced biofilm formation and concomitantly with repressed rhamnolipid gene expression, only trace amount of extracellular rhamnolipids were detected. The QSIs did not completely inhibit virulence factor expression and production but their administration significantly lowered the virulence phenotypes at both the transcriptional and extracellular levels. This study shows the significant inhibitory effect of natural plant-derived compounds on the repression of QS systems in P. aeruginosa .

Energy is vital parameter for communication in Internet of Things (IoT) applications via Wireless Sensor Networks (WSN). Genetic algorithms with dynamic clustering approach are supposed to be very effective technique in conserving energy during the process of network planning and designing for IoT. Dynamic clustering recognizes the cluster head (CH) with higher energy for the data transmission in the network. In this paper, various applications, like smart transportation, smart grid, and smart cities, are discussed to establish that implementation of dynamic clustering computing-based IoT can support real-world applications in an efficient way. In the proposed approach, the dynamic clustering-based methodology and frame relay nodes (RN) are improved to elect the most preferred sensor node (SN) amidst the nodes in cluster. For this purpose, a Genetic Analysis approach is used. The simulations demonstrate that the proposed technique overcomes the dynamic clustering relay node (DCRN) clustering algorithm in terms of slot utilization, throughput and standard deviation in data transmission.

Terpenes are the largest class of natural products with extensive structural diversity and are widely used as pharmaceuticals, herbicides, flavourings, fragrances, and biofuels. While they have mostly been isolated from plants and fungi, the availability and analysis of bacterial genome sequence data indicates that bacteria also possess many putative terpene synthase genes. In this study, we further explore this potential for terpene synthase activity in bacteria. Twenty two potential class I terpene synthase genes (TSs) were selected to represent the full sequence diversity of bacterial synthase candidates and recombinantly expressed in E. coli. Terpene synthase activity was detected for 15 of these enzymes, and included mono-, sesqui- and diterpene synthase activities. A number of confirmed sesquiterpene synthases also exhibited promiscuous monoterpene synthase activity, suggesting that bacteria are potentially a richer source of monoterpene synthase activity then previously assumed. Several terpenoid products not previously detected in bacteria were identified, including aromandendrene, acora-3,7(14)-diene and longiborneol. Overall, we have identified promiscuous terpene synthases in bacteria and demonstrated that terpene synthases with substrate promiscuity are widely distributed in nature, forming a rich resource for engineering terpene biosynthetic pathways for biotechnology.

One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

Histopathological examination is considered as gold standard procedure for arriving at a final diagnosis of various lesions of the human body. However, it is limited by a number of alterations of normal morphologic and cytological features that occur as a result of presence of artifacts. These artifacts may occur during surgical removal, fixation, tissue processing, embedding and microtomy and staining and mounting procedures. They can even lead to complete uselessness of the tissue. It is therefore essential to identify the commonly occurring artifacts during histopathological interpretations of tissue sections. This article reviews the common artifacts encountered during slide examination alongside the remedial measures which can be undertaken to differentiate between an artifact and tissue constituent.

Background Logistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs. not sick). However, when applying logistic regression to complex survey data, which includes complex sampling designs, specific methodological issues are often overlooked. Methods The systematic review extensively searched the PubMed and ScienceDirect databases from January 2015 to December 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, focusing primarily on the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). 810 articles met the inclusion criteria and were included in the analysis. When discussing logistic regression, the review considered multiple methodological problems such as the model adequacy assessment, handling dependence of observations, utilization of complex survey design, dealing with missing values, outliers, and more. Results Among the selected articles, the DHS database was used the most (96%), with MICS accounting for only 3%, and both DHS and MICS accounting for 1%. Of these, it was found that only 19.7% of the studies employed multilevel mixed-effects logistic regression to account for data dependencies. Model validation techniques were not reported in 94.8% of the studies with limited uses of the bootstrap, jackknife, and other resampling methods. Moreover, sample weights, PSUs, and strata variables were used together in 40.4% of the articles, and 41.7% of the studies did not use any of these variables, which could have produced biased results. Goodness-of-fit assessments were not mentioned in 75.3% of the articles, and the Hosmer–Lemeshow and likelihood ratio test were the most common among those reported. Furthermore, 95.8% of studies did not mention outliers, and only 41.0% of studies corrected for missing information, while only 2.7% applied imputation techniques. Conclusions This systematic review highlights important gaps in the use of logistic regression with complex survey data, such as overlooking data dependencies, survey design, and proper validation techniques, along with neglecting outliers, missing data, and goodness-of-fit assessments, all of which point to the need for clearer methodological standards and more thorough reporting to improve the reliability of results. Future research should focus on consistently following these standards to ensure stronger and more dependable findings.

In Bangladesh, live bird market environments are frequently contaminated with avian influenza viruses. Shop-level biosecurity practices might increase risk for environmental contamination. We sought to determine which shop-level biosecurity practices were associated with environmental contamination. We surveyed 800 poultry shops to describe biosecurity practices and collect environmental samples. Samples from 205 (26%) shops were positive for influenza A viral RNA, 108 (14%) for H9, and 60 (8%) for H5. Shops that slaughtered poultry, kept poultry overnight, remained open without rest days, had uneven muddy floors, held poultry on the floor, and housed sick and healthy poultry together were more frequently positive for influenza A viruses. Reported monthly cleaning seemed protective, but disinfection practices were not otherwise associated with influenza A virus detection. Slaughtering, keeping poultry overnight, weekly rest days, infrastructure, and disinfection practices could be targets for interventions to reduce environmental contamination.

Pseudomonas aeruginosa uses quorum sensing (QS) to modulate the expression of several virulence factors that enable it to establish severe infections. The QS system in P. aeruginosa is complex, intricate and is dominated by two main N -acyl-homoserine lactone circuits, LasRI and RhlRI. These two QS systems work in a hierarchical fashion with LasRI at the top, directly regulating RhlRI. Together these QS circuits regulate several virulence associated genes, metabolites, and enzymes in P. aeruginosa . Paradoxically, LasR mutants are frequently isolated from chronic P. aeruginosa infections, typically among cystic fibrosis (CF) patients. This suggests P. aeruginosa can undergo significant evolutionary pathoadaptation to persist in long term chronic infections. In contrast, mutations in the RhlRI system are less common. Here, we have isolated a clinical strain of P. aeruginosa from a CF patient that has deleted the transcriptional regulator RhlR entirely. Whole genome sequencing shows the rhlR locus is deleted in PA80 alongside a few non-synonymous mutations in virulence factors including protease lasA and rhamnolipid rhlA, rhlB, rhlC. Importantly we did not observe any mutations in the LasRI QS system. PA80 does not appear to have an accumulation of mutations typically associated with several hallmark pathoadaptive genes (i.e., mexT, mucA, algR, rpoN, exsS, ampR ). Whole genome comparisons show that P. aeruginosa strain PA80 is closely related to the hypervirulent Liverpool epidemic strain (LES) LESB58. PA80 also contains several genomic islands (GI’s) encoding virulence and/or resistance determinants homologous to LESB58. To further understand the effect of these mutations in PA80 QS regulatory and virulence associated genes, we compared transcriptional expression of genes and phenotypic effects with isogenic mutants in the genetic reference strain PAO1. In PAO1, we show that deletion of rhlR has a much more significant impact on the expression of a wide range of virulence associated factors rather than deletion of lasR . In PA80, no QS regulatory genes were expressed, which we attribute to the inactivation of the RhlRI QS system by deletion of rhlR and mutation of rhlI. This study demonstrates that inactivation of the LasRI system does not impact RhlRI regulated virulence factors. PA80 has bypassed the common pathoadaptive mutations observed in LasR by targeting the RhlRI system. This suggests that RhlRI is a significant target for the long-term persistence of P. aeruginosa in chronic CF patients. This raises important questions in targeting QS systems for therapeutic interventions.

Omicron, the most recent SARS-CoV-2 variant of concern (VOC), harbours multiple mutations in the spike protein that were not observed in previous VOCs. Initial studies suggest Omicron to substantially reduce the neutralizing capability of antibodies induced from vaccines and previous infection. However, its effect on T cell responses remains to be determined. Here, we assess the effect of Omicron mutations on known T cell epitopes and report data suggesting T cell responses to remain broadly robust against this new variant.