Multi-Step Regulation of the TLR4 Pathway by the miR-125a~99b~let-7e Cluster

An appropriate immune response requires a tight balance between pro- and anti-inflammatory mechanisms. IL-10 is induced at late time-points during acute inflammatory conditions triggered by TLR-dependent recognition of infectious agents and is involved in setting this balance, operating as a negative regulator of the TLR-dependent signaling pathway. We identified miR-125a~99b~let-7e as an evolutionary conserved microRNA cluster late-induced in human monocytes exposed to the TLR4 agonist LPS as an effect of this IL-10-dependent regulatory loop. We demonstrated that microRNAs generated by this cluster perform a pervasive regulation of the TLR signaling pathway by direct targeting receptors (TLR4, CD14), signaling molecules (IRAK1), and effector cytokines (TNFα, IL-6, CCL3, CCL7, CXCL8). Modulation of miR-125a~99b~let-7e cluster influenced the production of proinflammatory cytokines in response to LPS and the IL-10-mediated tolerance to LPS, thus identifying this gene as a previously unrecognized major regulatory element of the inflammatory response and endotoxin tolerance

SoK: Contemporary Issues and Challenges to Enable Cyber Situational Awareness for Network Security

Cyber situational awareness is an essential part of cyber defense that allows the cybersecurity operators to cope with the complexity of today's networks and threat landscape. Perceiving and comprehending the situation allow the operator to project upcoming events and make strategic decisions. In this paper, we recapitulate the fundamentals of cyber situational awareness and highlight its unique characteristics in comparison to generic situational awareness known from other fields. Subsequently, we provide an overview of existing research and trends in publishing on the topic, introduce front research groups, and highlight the impact of cyber situational awareness research. Further, we propose an updated taxonomy and enumeration of the components used for achieving cyber situational awareness. The updated taxonomy conforms to the widely-accepted three-level definition of cyber situational awareness and newly includes the projection level. Finally, we identify and discuss contemporary research and operational challenges, such as the need to cope with rising volume, velocity, and variety of cybersecurity data and the need to provide cybersecurity operators with the right data at the right time and increase their value through visualization

Multi-Step Regulation of the TLR4 Pathway by the miR-125a~99b~let-7e Cluster

An appropriate immune response requires a tight balance between pro- and anti-inflammatory mechanisms. IL-10 is induced at late time-points during acute inflammatory conditions triggered by TLR-dependent recognition of infectious agents and is involved in setting this balance, operating as a negative regulator of the TLR-dependent signaling pathway. We identified miR-125a~99b~let-7e as an evolutionary conserved microRNA cluster late-induced in human monocytes exposed to the TLR4 agonist LPS as an effect of this IL-10-dependent regulatory loop. We demonstrated that microRNAs generated by this cluster perform a pervasive regulation of the TLR signaling pathway by direct targeting receptors (TLR4, CD14), signaling molecules (IRAK1), and effector cytokines (TNFα, IL-6, CCL3, CCL7, CXCL8). Modulation of miR-125a~99b~let-7e cluster influenced the production of proinflammatory cytokines in response to LPS and the IL-10-mediated tolerance to LPS, thus identifying this gene as a previously unrecognized major regulatory element of the inflammatory response and endotoxin tolerance

Clinical assessment instruments validated for nursing practice in the Italian context: a systematic review of the literature

Aims. With the aim to identify the instruments validated for Italian nursing practice, a systematic review of the literature was undertaken.Results. A total of 101 instruments emerged. The majority (89; 88.1%) were developed in other countries; the remaining (14; 13.9%) were developed and validated in the Ital-ian context. The instruments were developed to measure patient’s problems (63/101; 62.4%), outcomes (27/101; 26.7%), risks (4/101; 4%) and others issues (7/101; 6.9%). The majority of participants involved in the validation processes were younger adults (49; 48.5%), older adults (40; 39.5%), children (4; 4%), adolescents (3; 3%), and children/adolescents (1; 1%). The instruments were structured primarily in the form of questionnaires (61; 60.4%), as a grid for direct observation (27; 26.7%) or in other forms (12; 11.9%). Among the 101 instruments emerged, there were 1 to 7 validation measures documented with on average 3.2 (95% CI 2.86-3.54) for each instrument.Conclusions. Developing validation studies giving priority to those instruments widely adopted in the clinical nursing practice is recommended.  

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Extracting and summarizing information from large data repositories

Information retrieval from large data repositories has become an important area of computer science. Research in this field is highly encouraged by the ever-increasing rate with which today's society is able to produce digital data. Unfortunately most of such data (e.g. video recordings, plain text documents) are unstructured. Two major issues thus arise in this scenario: i) extracting structured data -- information -- from unstructured data; ii) summarizing information, i.e. reducing large volumes of information to a short summary or abstract comprising only themost essential facts. In this thesis, techniques for extracting and summarizing information from large data repositories are presented. In particular the attention is focused onto two kinds of repositories: video data collections and natural language text document repositories. We show how the same principles can be applied for summarizing information in both domains and present solutions tailored to each domain. The thesis presents a novel video summarization algorithm, the Priority Curve Algorithm, that outperforms previous solutions, and three heuristic algorithms, OptStory+, GenStory and DynStory, for creating succinct stories about entities of interest using the information collected by algorithms that extract structured data from heterogenous data sources. In particular a Text Attribute Extraction (TAE) algorithm for extracting information from natural language text is presented. Experimental results show that our approach to summarization is promising