A new model for the characterization of infection risk in gunshot injuries:Technology, principal consideration and clinical implementation

<p>Abstract</p> <p>Introduction</p> <p>The extent of wound contamination in gunshot injuries is still a topic of controversial debate. The purpose of the present study is to develop a model that illustrates the contamination of wounds with exogenous particles along the bullet path.</p> <p>Material and methods</p> <p>To simulate bacteria, radio-opaque barium titanate (3-6 μm in diameter) was atomized in a dust chamber. Full metal jacket or soft point bullets caliber .222 (n = 12, v₀= 1096 m/s) were fired through the chamber into a gelatin block directly behind it. After that, the gelatin block underwent multi-slice CT in order to analyze the permanent and temporary wound cavity.</p> <p>Results</p> <p>The permanent cavity caused by both types of projectiles showed deposits of barium titanate distributed over the entire bullet path. Full metal jacket bullets left only few traces of barium titanate in the temporary cavity. In contrast, the soft point bullets disintegrated completely, and barium titanate covered the entire wound cavity.</p> <p>Discussion</p> <p>Deep penetration of potential exogenous bacteria can be simulated easily and reproducibly with barium titanate particles shot into a gelatin block. Additionally, this procedure permits conclusions to be drawn about the distribution of possible contaminants and thus can yield essential findings in terms of necessary therapeutic procedures.</p

Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks

A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

MicroRNA expression as risk biomarker of breast cancer metastasis : a pilot retrospective case-cohort study

Background: MicroRNAs (miRNAs) are small, non-coding RNA molecules involved in post-transcriptional gene regulation and have recently been shown to play a role in cancer metastasis. In solid tumors, especially breast cancer, alterations in miRNA expression contribute to cancer pathogenesis, including metastasis. Considering the emerging role of miRNAs in metastasis, the identification of predictive markers is necessary to further the understanding of stage-specific breast cancer development. This is a retrospective analysis that aimed to identify molecular biomarkers related to distant breast cancer metastasis development. Methods: A retrospective case cohort study was performed in 64 breast cancer patients treated during the period from 1998-2001. The case group (n = 29) consisted of patients with a poor prognosis who presented with breast cancer recurrence or metastasis during follow up. The control group (n = 35) consisted of patients with a good prognosis who did not develop breast cancer recurrence or metastasis. These patient groups were stratified according to TNM clinical stage (CS) I, II and III, and the main clinical features of the patients were homogeneous. MicroRNA profiling was performed and biomarkers related to metastatic were identified independent of clinical stage. Finally, a hazard risk analysis of these biomarkers was performed to evaluate their relation to metastatic potential. Results: MiRNA expression profiling identified several miRNAs that were both specific and shared across all clinical stages (p <= 0.05). Among these, we identified miRNAs previously associated with cell motility (let-7 family) and distant metastasis (hsa-miR-21). In addition, hsa-miR-494 and hsa-miR-21 were deregulated in metastatic cases of CSI and CSII. Furthermore, metastatic miRNAs shared across all clinical stages did not present high sensitivity and specificity when compared to specific-CS miRNAs. Between them, hsa-miR-183 was the most significative of CSII, which miRNAs combination for CSII (hsa-miR-494, hsa-miR-183 and hsa-miR-21) was significant and were a more effective risk marker compared to the single miRNAs. Conclusions: Women with metastatic breast cancer, especially CSII, presented up-regulated levels of miR-183, miR-494 and miR-21, which were associated with a poor prognosis. These miRNAs therefore represent new risk biomarkers of breast cancer metastasis and may be useful for future targeted therapies.We thank the Researcher Support Center of Barretos Cancer Hospital, especially the statistician Zanardo C. for assisting in the statistical analysis.This study received financial support from Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (Fapesp, Proc: 10/ 16796-0, Sao Paulo, Brazil)

Pretransplant Prediction of Posttransplant Survival for Liver Recipients with Benign End-Stage Liver Diseases: A Nonlinear Model

Background: The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients ’ survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a generalillness severity model (the sequential organ failure assessment score, or SOFA score). Methodology/Principal Findings: With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na +; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, wit

A Re-Examination of Global Suppression of RNA Interference by HIV-1

The nature of the interaction between replicating HIV-1 and the cellular RNAi pathway has been controversial, but it is clear that it can be complex and multifaceted. It has been proposed that the interaction is bi-directional, whereby cellular silencing pathways can restrict HIV-1 replication, and in turn, HIV-1 can suppress silencing pathways. Overall suppression of RNAi has been suggested to occur via direct binding and inhibition of Dicer by the HIV-1 Tat protein or through sequestration of TRBP, a Dicer co-factor, by the structured TAR element of HIV-1 transcripts. The role of Tat as an inhibitor of Dicer has been questioned and our results support and extend the conclusion that Tat does not inhibit RNAi that is mediated by either exogenous or endogenous miRNAs. Similarly, we find no suppression of silencing pathways in cells with replicating virus, suggesting that viral products such as the TAR RNA elements also do not reduce the efficacy of cellular RNA silencing. However, knockdown of Dicer does allow increased viral replication and this occurs at a post-transcriptional level. These results support the idea that although individual miRNAs can act to restrict HIV-1 replication, the virus does not counter these effects through a global suppression of RNAi synthesis or processing

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service

The interbank mobile payment service (IMPS) is a very recent technology in India that serves the very critical purpose of a mobile wallet. To account for the adoption and use of IMPS by the Indian consumers, this study seeks to compare three competing sets of attributes borrowed from three recognized pieces of work in the area of innovations adoption. This study aims to examine which of the three sets of attributes better predicts the adoption of IMPS in an Indian context. The research model is empirically tested and validated against the data gathered from 323 respondents from different cities in India. The findings are analysed using the SPSS analysis tool, which are then discussed to derive the key conclusions from this study. The research implications are stated, limitations listed and suggestions for future research on this technology are then finally made

De Novo and Bi-allelic Pathogenic Variants in NARS1 Cause Neurodevelopmental Delay Due to Toxic Gain-of-Function and Partial Loss-of-Function Effects

Aminoacyl-tRNA synthetases (ARSs) are ubiquitous, ancient enzymes that charge amino acids to cognate tRNA molecules, the essential first step of protein translation. Here, we describe 32 individuals from 21 families, presenting with microcephaly, neurodevelopmental delay, seizures, peripheral neuropathy, and ataxia, with de novo heterozygous and bi-allelic mutations in asparaginyl-tRNA synthetase (NARS1). We demonstrate a reduction in NARS1 mRNA expression as well as in NARS1 enzyme levels and activity in both individual fibroblasts and induced neural progenitor cells (iNPCs). Molecular modeling of the recessive c.1633C>T (p.Arg545Cys) variant shows weaker spatial positioning and tRNA selectivity. We conclude that de novo and bi-allelic mutations in NARS1 are a significant cause of neurodevelopmental disease, where the mechanism for de novo variants could be toxic gain-of-function and for recessive variants, partial loss-of-function