Mycotoxins nivalenol and deoxynivalenol differently modulate cytokine mRNA expression in Jurkat T cells.

Deoxynivalenol (DON) and its hydroxylated form nivalenol (NIV) are Fusarium mycotoxins that occur in cereal grains alone or in combination. Several studies have shown that these metabolites affect lymphocyte functions. However, the molecular mechanisms underlying their activities are still partially known. To address this issue, we examined the influence of NIV and DON in modulating IFNc, IL-2 and IL-8 mRNA levels in Jurkat T cells. In PMA/ionomycin stimulated cells, pre-incubated with increasing concentrations of NIV, transcription was induced in the range 0.06–2 lM; higher concentrations of NIV were found non-stimulating (4 lM) or inhibitory (8 lM) for IFNc and IL-2 whereas IL-8 was still induced. DON administration elicited a similar profile for IL-8 and IFNc, whilst IL-2 mRNA was induced in a broader range of concentrations. Combination of NIV and DON at 1:1 and 1:10 ratios essentially restored the cytokine transcriptional pattern observed with NIV alone but the level of transcripts, with the exception of IL-8, peaked at lower concentrations suggesting interactive effects. Moreover both mycotoxins caused inhibition of cell proliferation, mediated by induction of apoptosis, confirming previous results and highlighting the usefulness of Jurkat as a T-cell model to study the effects of mycotoxins on the immune functions in humans

An art course for grade ten based on everyday graphic arts

Thesis (M.A.)--Boston University, 1938. This item was digitized by the Internet Archive

An art course for grade ten based on everyday graphic arts

Thesis (M.A.)--Boston University, 1938. This item was digitized by the Internet Archive

A review of new TDR applications for measuring non-aqueous phase liquids (NAPLs) in soils

The time domain reflectometry (TDR) technique is a geophysical method that allows, in a time-varying electric field, the determination of dielectric permittivity and electrical conductivity of a wide class of porous materials. Measurements of the volumetric water content (θw) in soils is the most frequent application of TDR in Soil Science and Soil Hydrology. In last four decades several studies have sought to explore potential applications of TDR. Such studies (except those conducted on θw estimation) mainly focused on monitoring soil solute transport. In more recent times, innovative TDR approaches have also been implemented to extend current TDR fields of application to the problem of monitoring non-aqueous phase liquids (NAPLs) in variable saturated soils. NAPLs are organic compounds with low solubility in water and are characterised by a high mobility in the vadose zone. Due to their high toxicity, NAPLs constitute a severe geo-environmental problem, thus making detection and observation of such substances in soils an increasingly important issue. The present paper deals with these studies and aims to provide an up-to-date review of the main NAPL-TDR studies. To date, the literature has focused on TDR applications in three main fields: (i) NAPL monitoring in homogeneous, variable saturated soils, (ii) NAPL monitoring in layered variable saturated soils, and (iii) NAPL monitoring during soil decontamination processes. For an exhaustive and complete overview of TDR research in this field, we also recall the basic principles of TDR signal propagation, the functioning of a typical TDR device, and the dielectric mixing models that are widely used to interpret the dielectric response of NAPL-contaminated soils

A decrease of calcitonin serum concentrations less than 50 percent 30 minutes after thyroid surgery suggests incomplete C-cell tumor tissue removal

The prognosis of medullary thyroid carcinoma (MTC) depends on the completeness of the first surgical treatment. To date, it is not possible to predict whether the tumor has been completely removed after surgery. The aim of this study was to evaluate the reliability of an intraoperative calcitonin monitoring as a predictor of the final outcome after surgery in patients with MTC

Co-Management of COVID-19 and heart failure during the COVID-19 pandemic. lessons learned

The COVID pandemic has brought many new challenges worldwide, which has impacted on patients with chronic conditions. There is an increasing evidence base suggesting an interaction between chronic heart failure (HF) and COVID-19, and in turn the prognostic impact of co-existence of the two conditions. Patients with existing HF appear more prone to develop severe complications on contracting COVID-19, but the exact prevalence in patients with mild symptoms of COVID-19 not requiring hospital admission is poorly investigated. In addition, hospitalization rates for acute HF over the pandemic period appear reduced compared to previous periods. Several key issues remain rather unaddressed and, importantly, a specific algorithm focused on diagnostic differentiation between HF and acute respiratory distress syndrome, a severe complication of COVID-19, is still lacking. Furthermore, recent data suggests potential interaction existing between HF treatment and some anti-viral anti-inflammatory drugs prescribed during the infection, raising some doubts about a universal treatment strategy for all patients with COVID-19. With this manuscript, we aim to review the current literature in this field in light of growing understanding of COVID-19 in the setting of the HF population, its associated morbidity and mortality burden, and the impact on healthcare systems. We hope that this may stimulate a discussion to guarantee a better, more tailored delivery of care for patients with HF in the setting of concomitant COVID-19 infection

A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data

Traffic signals play an important role in transportation by enabling traffic flow management, and ensuring safety at intersections. In addition, knowing the traffic signal phase and timing data can allow optimal vehicle routing for time and energy efficiency, eco-driving, and the accurate simulation of signalized road networks. In this paper, we present a machine learning (ML) method for estimating traffic signal timing information from vehicle probe data. To the authors best knowledge, very few works have presented ML techniques for determining traffic signal timing parameters from vehicle probe data. In this work, we develop an Extreme Gradient Boosting (XGBoost) model to estimate signal cycle lengths and a neural network model to determine the corresponding red times per phase from probe data. The green times are then be derived from the cycle length and red times. Our results show an error of less than 0.56 sec for cycle length, and red times predictions within 7.2 sec error on average