Determination of volatile organic compounds in exhaled breath of heart failure patients by needle trap micro-extraction coupled with gas chromatography-tandem mass spectrometry

The analytical performances of needle trap micro-extraction (NTME) coupled with gas chromatography tandem mass spectrometry were evaluated by analyzing a mixture of twenty-two representative breath VOCs belonging to different chemical classes (i.e. hydrocarbons, ketones, aldehydes, aromatics and sulfurs). NTME is an emerging technique that guarantees detection limits in pptv range by pre-concentrating low volumes of sample, and it is particularly suitable for breath analysis. For most VOCs, detection limits between 20 and 500 pptv were obtained by pre-concentrating 25 mL of a humidified standard gas mixture at a flow rate of 15 mL/min. For all compounds, inter- and intra-day precisions were always below 15%, confirming the reliability of the method. The procedure was successfully applied to the analysis of exhaled breath samples collected from forty heart failure patients during their stay in the University Hospital of Pisa. The majority of patients (about 80%) showed a significant decrease of breath acetone levels (a factor of 3 or higher) at discharge compared to admission (acute phase) in correspondence to the improved clinical conditions during hospitalization, thus making this compound eligible as a biomarker of heart failure exacerbation

Predicting Heart Failure Patient Events by Exploiting Saliva and Breath Biomarkers Information

The aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management

La valutazione dei dirigenti: strumenti e ambiti applicativi

Il mio elaborato tratta l'evoluzione della figura del dirigente e i metodi di valutazione usati dentro della pubblica amministrazione. Dalla prima legge sulla valutazione del dirigente, nel , ad oggi ci sono stati molti cambiamenti. Il punto di svolta si è avuto con il decreto legislativo 150/2009 la così'detta Riforma Brunetta che cercava di avvicinare la sfera pubblica a quella privata nelle parti in cui questa riesce meglio; per cui le pubbliche amministrazioni hanno iniziato ad operare in una logica sempre più manageriale ponendo attenzione alle modalità di utilizzo delle risorse allo scopo di migliorare la qualità dei servizi erogati e di soddisfare le esigenze della collettività in modo efficiente ed efficace. Pertanto è necessario lavorare sui sistemi di valutazione della dirigenza per ottenere un effetto leva trasversale alle pubbliche amministrazioni con effetti in termini di efficacia ed efficienza. Come ambito applicativo ho studiato me avviene la valutazione della dirigenza nell'Istituto Nazionale di Previdenza Sociale, INPS

Estimation of Heart Failure Patients Medication Adherence through the Utilization of Saliva and Breath Biomarkers and Data Mining Techniques

International audienceThe aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied. In the second stage, the output of the model, accompanied with data from saliva and breath biomarkers, is given as input to a classification model for determining if the patient is adherent, in terms of medication. The method is evaluated on a dataset of 29 patients and the achieved accuracy is 96%

Predicting Heart Failure patient events by exploiting saliva and breath biomarkers information

International audienceThe aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management

LncRNAs as novel indicators of patients' prognosis in stage i epithelial ovarian cancer: A retrospective and multicentric study

24noPurpose: Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs and is characterized by good prognosis with fewer than 20% of patients relapsing. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. We have demonstrated that in stage I EOC miR-200c-3p can predict patients' outcome. In the present study, we analyzed the expression of long non-coding RNAs (lncRNA) to enable potential definition of a non-coding transcriptional signature with prognostic relevance for stage I EOC. Experimental Design: 202 snap-frozen stage I EOC tumor biopsies, 47 of which relapsed, were gathered together from three independent tumor tissue collections and subdivided into a training set (n = 73) and a validation set (n = 129). Median follow up was 9 years. LncRNAs' expression profiles were correlated in univariate and multivariate analysis with overall survival (OS) and progression-free survival (PFS). Results: The expression of lnc-SERTAD2-3, lnc-SOX4-1, lnc- HRCT1-1, and PVT1 was associated in univariate and multivariate analyses with relapse and poor outcome in both training and validation sets (P < 0.001). Using the expression profiles of PVT1, lnc-SERTAD2-3, and miR-200c-3p simultaneously, it was possible to stratify patients into high and low risk. The OS for high- and low-risk individuals are 36 and 123 months, respectively (OR, 15.55; 95% confidence interval, 3.81-63.36). Conclusions: We have identified a non-coding transcriptional signature predictor of survival and biomarker of relapse for stage I EOC. Clin Cancer Res; 23(9); 2356-66. ©2016 AACR.nonenoneMartini, Paolo; Paracchini, Lara; Caratti, Giulia; Mello-Grand, Maurizia; Fruscio, Robert; Beltrame, Luca; Calura, Enrica; Sales, Gabriele; Ravaggi, Antonella; Bignotti, Eliana; Odicino, Franco.; Sartori, Enrico; Perego, Patrizia; Katsaros, Dionyssios; Craparotta, Ilaria; Chiorino, Giovanna; Cagnin, Stefano; Mannarino, Laura; Ceppi, Lorenzo; Mangioni, Costantino; Ghimenti, Chiara; D'Incalci, Maurizio*; Marchini, Sergio; Romualdi, ChiaraMartini, Paolo; Paracchini, Lara; Caratti, Giulia; Mello-Grand, Maurizia; Fruscio, Robert; Beltrame, Luca; Calura, Enrica; Sales, Gabriele; Ravaggi, Antonella; Bignotti, Eliana; Odicino, Franco.; Sartori, Enrico; Perego, Patrizia; Katsaros, Dionyssios; Craparotta, Ilaria; Chiorino, Giovanna; Cagnin, Stefano; Mannarino, Laura; Ceppi, Lorenzo; Mangioni, Costantino; Ghimenti, Chiara; D'Incalci, Maurizio; Marchini, Sergio; Romualdi, Chiar

iASPP/p63 autoregulatory feedback loop is required for the homeostasis of stratified epithelia

iASPP, an inhibitory member of the ASPP (apoptosis stimulating protein of p53) family, is an evolutionarily conserved inhibitor of p53 which is frequently upregulated in human cancers. However, little is known about the role of iASPP under physiological conditions. Here, we report that iASPP is a critical regulator of epithelial development. We demonstrate a novel autoregulatory feedback loop which controls crucial physiological activities by linking iASPP to p63, via two previously unreported microRNAs, miR-574-3p and miR-720. By investigating its function in stratified epithelia, we show that iASPP participates in the p63-mediated epithelial integrity program by regulating the expression of genes essential for cell adhesion. Silencing of iASPP in keratinocytes by RNA interference promotes and accelerates a differentiation pathway, which also affects and slowdown cellular proliferation. Taken together, these data reveal iASPP as a key regulator of epithelial homeostasis