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Dendritic cells (DCs) are required to initiate and sustain T cell-dependent anti-cancer immunity. However, tumors often evade immune control by crippling normal DC function. The endoplasmic reticulum (ER) stress response factor XBP1 promotes intrinsic tumor growth directly, but whether it also regulates the host anti-tumor immune response is not known. Here we show that constitutive activation of XBP1 in tumor-associated DCs (tDCs) drives ovarian cancer (OvCa) progression by blunting antitumor immunity. XBP1 activation, fueled by lipid peroxidation byproducts, induced a triglyceride biosynthetic program in tDCs leading to abnormal lipid accumulation and subsequent inhibition of tDC capacity to support anti-tumor T cells. Accordingly, DC-specific XBP1 deletion or selective nanoparticle-mediated XBP1 silencing in tDCs restored their immunostimulatory activity in situ and extended survival by evoking protective type 1 anti-tumor responses. Targeting the ER stress response should concomitantly inhibit tumor growth and enhance anti-cancer immunity, thus offering a unique approach to cancer immunotherapy.

to describe the characteristics and effectiveness of various smoking cessation programs offered by Italian treatment services operating within the National Health Service.prospective longitudinal multicentre study involving 41 smoking cessation services in 16 Italian regions.the study population includes patients entering smoking cessation programs between April 2003 and June 2004. The "study population" includes 1226 patients (54.2% males and 45.4% females), mean age 47 years. Patients have a middle/high level of education and a long history of smoking; most are highly dependent on nicotine and report previous attempts to quit smoking.treatment effectiveness in smoking cessation is assessed six months after entering treatment service. Logistic Regression Model was used to determine the predictors of successfiul cessation, independent of treatment typology. The predictors were included as confounding variables in the logistic regression model that was used to evaluate the effectiveness of treatments. Besides the effect of treatment completion on smoking cessation was estimated.predictors of successful smoking cessation are: being male, presence of a partner, strong motivation to quit, previous attempts to give up smoking, mild nicotine dependence, and not suffering from mood disturbances. All treatments are effective in helping people to stop smoking: cessation rate ranges between 25.00% for patients receiving a single session of motivational counselling and 65.3% for those receiving nicotine replacement therapy combined to group therapy. Compared to a single session of motivational counseling, nicotine replacement therapy combined to group therapy is the most effective therapeutic program (OR 5.4; 95%CI 12.5-12.0). Treatment completion is a strong determinant ofsuccess (OR 4.8; 95%CI 3.5-6.4).enrolling people in any type of therapeutic program, in particular nicotine replacement therapy combined with group therapy increases the probability of successfully quitting smoking; moreover, patients that begin a smoking cessation program should be encouraged to complete the therapy

In contrast to other malignancies, circulating immune complexes (CICS) are usually not detected by conventional assays in the sera of ovarian cancer patients. However, a polyethylene glycol (PEG) precipitation assay has been reported to detect putative CICS in ovarian cancer. To determine if CICS were indeed present, we analyzed sera from 12 women with ovarian cancer. All were negative for CICS by the Raji cell assay; 5 (42%) were positive by the PEG assay. However, the PEG precipitate did not possess characteristics of immune complexes. IgG in sera or in the precipitate sedimented in sucrose gradients solely at the same rate as 7S monomeric IgG. In addition, the precipitates were not able to activate the complement system and the four IgG subclasses were present in the same relative concentration as that found in normal serum. The results suggest that it is probably a misnomer to label the material detected in ovarian cancer sera by the PEG precipitation assay as CICS. Instead a non-immune interaction of IgG with other components, possibly membrane fragments, is probably being measured.

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Human cytomegalovirus (hCMV) infection is the leading cause of birth defects in newborns and death in immunosuppressed people. Traditional techniques require time-consuming and costly analyses, and sometimes result in false positive results; thus, a rapid and accurate detection for hCMV infection is necessary. Recently, hcmv-miR-US4-5p was selected as the biomarker for cytomegalovirus diagnosis and follow-up. Herein, we propose a bioassay based on microgels endowed with optical fluorescent oligonucleotide probes for the detection of circulating endogenous hcmv-microRNAs. In particular, a double strand probe, based on the fluorescence recovery after target capture, was conjugated on microgels and the probe density was opportunely optimised. Then, the microgels were directly mixed with the sample. The fluorescence read-out was measured as a function of target concentration at a fixed number of microgels per tube. As a bead-based assay, the performances of optical detection in terms of dynamic working range and limit of detection could be finely tuned by tuning the number of microgels per tube. The limit of detection of the assay could be tuned in the range from 39.1 fM to 156 aM by changing the microgel concentration from 50 μg mL-1 to 0.5 μg mL-1, respectively. The assay results specific for the selected target were stable over a one-year time span and they were not affected by the presence of human serum. Therefore, this bioassay based on microgels might represent a flexible platform that should be able to predict, identify and follow-up several diseases by monitoring freely circulating oligonucleotides in body fluids.

CuO/CeO 2 catalysts with CuO content ranging from 0.5 wt.% to 8 wt.%, prepared by wet impregnation of commercial ceria, have been tested for the preferential oxidation of CO (CO-PROX) under H 2 -rich conditions at 70–210 °C. Catalytic activity increases up to 4 wt.% CuO content, with less concentrated catalysts showing higher intrinsic activity. Catalysts have been characterized by means of XRD, BET analysis and UV spectroscopy. Formation of segregated CuO clusters has been detected for Cu richest CuO/CeO 2 sample. Redox properties have been deeply investigated using TP analysis (H 2 TPR, CO TPR, TPO) of fresh or pre-treated samples. Participation of surface ceria, induced by the strong interaction with copper, to reduction/oxidation reactions in the temperature range explored (up to 430 °C) has been demonstrated. Different copper species and their reactivity towards H 2 and CO have been individuated by comparing TPR of fully oxidized catalysts with those of partially oxidized catalysts. Active species have been identified as copper-ceria sites able to oxidize CO even at room temperature and to be re-oxidized by O 2 at the same temperature. Transient experiments have been carried out at different temperature using a diluted mixture starting from oxidized or reduced catalysts and followed by a H 2 TPR of the used samples. The results of these tests have showed that active centres for CO oxidation contain copper in the +2 oxidation state. At T > 100 °C some reduced copper sites are stabilized which promote H 2 oxidation thus lowering the selectivity of the CO-PROX process.

In high risk, coastal, urban areas, cliff stability monitoring is an essential task for human activities. This paper presents the implementation of an integrated monitoring system at the Coroglio tuff cliff, located in the highly urbanized coastal area of Naples (Italy) on the border of the active volcanic caldera of Campi Flegrei. The system consists of standard geotechnical monitoring instruments (crackmeters and clinometers) coupled with a modern deformation monitoring technique based on Brillouin Optical Time-Domain Analysis (BOTDA) optical fiber network, integrated by a velocimetry sensor and a weather station. Remote sensing equipment like UAV digital photogrammetry (UAV-DP) and terrestrial laser scanning (TLS) have been also used for obtaining detailed multitemporal DTM of the cliff, as well as geostructural analysis and classification of the slope, supported by structural fieldwork. In the first phase of the study, we integrated the results of long-range TLS surveys with structural field mapping. The accurate and rapid detection of structural discontinuities of the rock played an important role for the understanding of the failure mechanism and the kinematic analysis of unstable blocks along the cliff. The preliminary results obtained during the first two year of monitoring activity (2014-2015) are presented. Micro-deformation of rocks measured by geotechnical sensors reveals a general sinusoidal trend, possibly linked to the bulk volume variation of the rocks, as a response to seasonal and daily temperature variations. This research provides a contribute to the understanding of the rates of geomorphic evolution of coastal tuff cliff and its relation with forcing factors (e.g. meteo-marine weathering, human actions, volcano-tectonic activity) in the perspective of early-warning actions and policies.