Treatment Outcomes of Treatment-Naïve Hepatitis C Patients co-infected with HIV: a systematic review and meta-analysis of observational cohorts

Co-infection with Hepatitis C (HCV) and HIV is common and HIV accelerates hepatic disease progression due to HCV. However, access to HCV treatment is limited and success rates are generally poor

Road traffic casualties in Great Britain at daylight savings time transitions: a causal regression discontinuity design analysis

Objectives: To determine whether daylight savings time (DST) transitions have an effect on road traffic casualties in Great Britain using causal regression discontinuity design analysis. We undertake aggregate and disaggregate spatial and temporal analyses to test the commonly referenced sleep and light hypotheses. Design: The study takes the form of a natural experiment in which the DST transitions are interventions to be evaluated. Two outcomes are tested: (i) the total number of casualties of all severities (ii) the number of fatalities. Data: Data are obtained from the UK Department for Transport STATS19 database. Over a period of 14 years between 2005 and 2018, 311,766 total casualties and 5,429 fatalities occurred 3 weeks either side of the Spring DST transition and 367,291 total casualties and 6,650 fatalities occurred 3 weeks either side of the Autumn DST transition. Primary outcome measure: A regression discontinuity design method (RDD) is applied. The presence of a causal effect is determined via the degree of statistical significance and magnitude of the average treatment effect. Results: All significant average treatment effects are negative (54 significant models out of 287 estimated), indicating that there are fewer casualties following the transitions. Overall, bootstrapped summary statistics indicate a reduction of 0.75 in the number of fatalities (95% CI: -1.61, -0.04) and a reduction of 4.73 in the number of total casualties (95% CI: -6.08, -3.27) on average per year at both the Spring and Autumn DST transitions combined. Conclusions: The results indicate minor reductions in the number of fatalities following the DST transitions, and thus our analysis does not support the most recent UK parliamentary estimate that there would be 30 fewer fatalities in Great Britain if DST were to be abolished. Furthermore, the results do not provide conclusive support for either the sleep or light hypotheses

Elevated ACKR2 expression is a common feature of inflammatory arthropathies

Objectives. Chemokines are essential contributors to leucocyte accumulation at sites of inflammatory pathology. Interfering with chemokine or chemokine receptor function therefore represents a plausible therapeutic option. However, our currently limited understanding of chemokine orchestration of inflammatory responses means that such therapies have not yet been fully developed. We have a particular interest in the family of atypical chemokine receptors that fine-tune, or resolve, chemokine-driven responses. In particular we are interested in atypical chemokine receptor 2 (ACKR2), which is a scavenging receptor for inflammatory CC-chemokines and that therefore helps to resolve in vivo inflammatory responses. The objective of the current study was to examine ACKR2 expression in common arthropathies. Methods. ACKR2 expression was measured by a combination of qPCR and immuno-histochemistry. In addition, circulating cytokine and chemokine levels in patient plasma were assessed using multiplexing approaches. Results. Expression of ACKR2 was elevated on peripheral blood cells as well as on leucocytes and stromal cells in synovial tissue. Expression on peripheral blood leucocytes correlated with, and could be regulated by, circulating cytokines with particularly strong associations being seen with IL-6 and hepatocyte growth factor. In addition, expression within the synovium was coincident with aggregates of lymphocytes, potentially atopic follicles and sites of high inflammatory chemokine expression. Similarly increased levels of ACKR2 have been reported in psoriasis and SSc. Conclusion. Our data clearly show increased ACKR2 in a variety of arthropathies and taking into account our, and others’, previous data we now propose that elevated ACKR2 expression is a common feature of inflammatory pathologies

Sex distribution of offspring-parents obesity: Angel's hypothesis revisited

This study, which is based on two cross sectional surveys' data, aims to establish any effect of parental obesity sex distribution of offspring and to replicate the results that led to the hypothesis that obesity may be associated with sex-linked recessive lethal gene. A representative sample of 4,064 couples living in Renfrew/Paisley, Scotland was surveyed 1972-1976. A total of 2,338 offspring from 1,477 of the couples screened in 1972-1976, living in Paisley, were surveyed in 1996. In this study, males represented 47.7% among the total offspring of the couples screened in 1972-1976. In the first survey there was a higher male proportion of offspring (53%, p < 0.05) from parents who were both obese, yet this was not significant after adjustment for age of parents. Also, there were no other significant differences in sex distribution of offspring according to body mass index, age, or social class of parents. The conditions of the original 1949 study of Angel (1949) (which proposed a sex-linked lethal recessive gene) were simulated by selecting couples with at least one obese daughter. In this subset, (n = 409), obesity in fathers and mothers was associated with 26% of offspring being male compared with 19% of offspring from a non-obese father and obese mother. Finally we conclude that families with an obese father have a higher proportion of male offspring. These results do not support the long-established hypotheses of a sex-linked recessive lethal gene in the etiology of obesity

Change in Nutritional Status Modulates the Abundance of Critical Pre-initiation Intermediate Complexes During Translation Initiation \u3cem\u3ein Vivo\u3c/em\u3e

In eukaryotic translation initiation, eIF2∙GTP–Met-tRNAiMet ternary complex (TC) interacts with eIF3–eIF1–eIF5 complex to form the multifactor complex (MFC), while eIF2∙GDP associates with eIF2B for guanine nucleotide exchange. Gcn2p phosphorylates eIF2 to inhibit eIF2B. Here we evaluate the abundance of eIFs and their pre-initiation intermediate complexes in gcn2 deletion mutant grown under different conditions. We show that ribosomes are three times as abundant as eIF1, eIF2 and eIF5, while eIF3 is half as abundant as the latter three and hence, the limiting component in MFC formation. By quantitative immunoprecipitation, we estimate that ∼ 15% of the cellular eIF2 is found in TC during rapid growth in a complex rich medium. Most of the TC is found in MFC, and important, ∼ 40% of the total eIF2 is associated with eIF5 but lacks tRNAiMet. When the gcn2Δ mutant grows less rapidly in a defined complete medium, TC abundance increases threefold without altering the abundance of each individual factor. Interestingly, the TC increase is suppressed by eIF5 overexpression and Gcn2p expression. Thus, eIF2B-catalyzed TC formation appears to be fine-tuned by eIF2 phosphorylation and the novel eIF2/eIF5 complex lacking tRNAiMet

Validation and sensitivity analysis of InfoCrop simulation model for growth and yield of Indian mustard varieties at Allahabad

Field experiment was carried out at SHUATS, Allahabad, to study validation and sensitivity analysis of InfoCrop model with the data sets generated respectively during Rabi season of 2016-17. The main plot treatments and sub-plot treatment consisted three dates of sowing and cultivars (D1-25th October, D2-5th November and D3-15th November) and (V1- Parasmani, V2- Varuna and V3- SRM 777) using split plot design. The results revealed that simulation of growth and yield parameters were compared with observed data and results concluded that the model overestimates all the parameters within the acceptable range

Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods