High temperature stability of Terphenyl based thermal oils

Thermal oils are nowadays widely used as heat transfer fluids or cooling media in industrial and energy production plants. Currently, very few data are available about their thermal stability in function of the operating temperatures, which is a crucial parameter to estimate oil structural changes and their possible effects the maximum fluids lifetime. The present work is concerned with ageing tests on a commercially used thermal oil at temperatures higher than the nominal working ones, including a full post-test characterization. At this aim, a dedicated experimental set-up was designed and constructed to study the degradation kinetics, and to qualitatively and quantitatively analyze the released gases. As a result, the kinetic parameters were estimated, along with the related changes in the oil thermos-physical properties

SNPs in ultraconserved elements and familial breast cancer risk

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Evaluation of a candidate breast cancer associated SNP in ERCC4 as a risk modifier in BRCA1 and BRCA2 mutation carriers. Results from the Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA)

Background: In this study we aimed to evaluate the role of a SNP in intron 1 of the ERCC4 gene (rs744154), previously reported to be associated with a reduced risk of breast cancer in the general population, as a breast cancer risk modifier in BRCA1 and BRCA2 mutation carriers. Methods: We have genotyped rs744154 in 9408 BRCA1 and 5632 BRCA2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and assessed its association with breast cancer risk using a retrospective weighted cohort approach. Results: We found no evidence of association with breast cancer risk for BRCA1 (per-allele HR: 0.98, 95% CI: 0.93–1.04, P=0.5) or BRCA2 (per-allele HR: 0.97, 95% CI: 0.89–1.06, P=0.5) mutation carriers. Conclusion: This SNP is not a significant modifier of breast cancer risk for mutation carriers, though weak associations cannot be ruled out. A Osorio1, R L Milne2, G Pita3, P Peterlongo4,5, T Heikkinen6, J Simard7, G Chenevix-Trench8, A B Spurdle8, J Beesley8, X Chen8, S Healey8, KConFab9, S L Neuhausen10, Y C Ding10, F J Couch11,12, X Wang11, N Lindor13, S Manoukian4, M Barile14, A Viel15, L Tizzoni5,16, C I Szabo17, L Foretova18, M Zikan19, K Claes20, M H Greene21, P Mai21, G Rennert22, F Lejbkowicz22, O Barnett-Griness22, I L Andrulis23,24, H Ozcelik24, N Weerasooriya23, OCGN23, A-M Gerdes25, M Thomassen25, D G Cruger26, M A Caligo27, E Friedman28,29, B Kaufman28,29, Y Laitman28, S Cohen28, T Kontorovich28, R Gershoni-Baruch30, E Dagan31,32, H Jernström33, M S Askmalm34, B Arver35, B Malmer36, SWE-BRCA37, S M Domchek38, K L Nathanson38, J Brunet39, T Ramón y Cajal40, D Yannoukakos41, U Hamann42, HEBON37, F B L Hogervorst43, S Verhoef43, EB Gómez García44,45, J T Wijnen46,47, A van den Ouweland48, EMBRACE37, D F Easton49, S Peock49, M Cook49, C T Oliver49, D Frost49, C Luccarini50, D G Evans51, F Lalloo51, R Eeles52, G Pichert53, J Cook54, S Hodgson55, P J Morrison56, F Douglas57, A K Godwin58, GEMO59,60,61, O M Sinilnikova59,60, L Barjhoux59,60, D Stoppa-Lyonnet61, V Moncoutier61, S Giraud59, C Cassini62,63, L Olivier-Faivre62,63, F Révillion64, J-P Peyrat64, D Muller65, J-P Fricker65, H T Lynch66, E M John67, S Buys68, M Daly69, J L Hopper70, M B Terry71, A Miron72, Y Yassin72, D Goldgar73, Breast Cancer Family Registry37, C F Singer74, D Gschwantler-Kaulich74, G Pfeiler74, A-C Spiess74, Thomas v O Hansen75, O T Johannsson76, T Kirchhoff77, K Offit77, K Kosarin77, M Piedmonte78, G C Rodriguez79, K Wakeley80, J F Boggess81, J Basil82, P E Schwartz83, S V Blank84, A E Toland85, M Montagna86, C Casella87, E N Imyanitov88, A Allavena89, R K Schmutzler90, B Versmold90, C Engel91, A Meindl92, N Ditsch93, N Arnold94, D Niederacher95, H Deißler96, B Fiebig97, R Varon-Mateeva98, D Schaefer99, U G Froster100, T Caldes101, M de la Hoya101, L McGuffog49, A C Antoniou49, H Nevanlinna6, P Radice4,5 and J Benítez1,3 on behalf of CIMB

Lack of SARS-CoV-2 RNA environmental contamination in a tertiary referral hospital for infectious diseases in Northern Italy

none140noNAnoneColaneri M.; Seminari E.; Piralla A.; Zuccaro V.; Di Filippo A.; Baldanti F.; Bruno R.; Mondelli M.U.; Brunetti E.; Di Matteo A.; Maiocchi L.; Pagnucco L.; Mariani B.; Ludovisi S.; Lissandrin R.; Parisi A.; Sacchi P.; Patruno S.F.A.; Michelone G.; Gulminetti R.; Zanaboni D.; Novati S.; Maserati R.; Orsolini P.; Vecchia M.; Sciarra M.; Asperges E.; Sambo M.; Biscarini S.; Lupi M.; Roda S.; Chiara Pieri T.; Gallazzi I.; Sachs M.; Valsecchi P.; Perlini S.; Alfano C.; Bonzano M.; Briganti F.; Crescenzi G.; Giulia Falchi A.; Guarnone R.; Guglielmana B.; Maggi E.; Martino I.; Pettenazza P.; Pioli di Marco S.; Quaglia F.; Sabena A.; Salinaro F.; Speciale F.; Zunino I.; De Lorenzo M.; Secco G.; Dimitry L.; Cappa G.; Maisak I.; Chiodi B.; Sciarrini M.; Barcella B.; Resta F.; Moroni L.; Vezzoni G.; Scattaglia L.; Boscolo E.; Zattera C.; Michele Fidel T.; Vincenzo C.; Vignaroli D.; Bazzini M.; Iotti G.; Mojoli F.; Belliato M.; Perotti L.; Mongodi S.; Tavazzi G.; Marseglia G.; Licari A.; Brambilla I.; Daniela B.; Antonella B.; Patrizia C.; Giulia C.; Giuditta C.; Marta C.; Rossana D.; Milena F.; Bianca M.; Roberta M.; Enza M.; Stefania P.; Maurizio P.; Elena P.; Antonio P.; Francesca R.; Antonella S.; Maurizio Z.; Guy A.; Laura B.; Ermanna C.; Giuliana C.; Luca D.; Gabriella F.; Gabriella G.; Alessia G.; Viviana L.; Claudia L.; Valentina M.; Simona P.; Marta P.; Alice B.; Giacomo C.; Irene C.; Alfonso C.; Di Martino R.; Di Napoli A.; Alessandro F.; Guglielmo F.; Loretta F.; Federica G.; Alessandra M.; Federica N.; Giacomo R.; Beatrice R.; Maria S.I.; Monica T.; Nepita Edoardo V.; Calvi M.; Tizzoni M.; Nicora C.; Triarico A.; Petronella V.; Marena C.; Muzzi A.; Lago P.; Comandatore F.; Bissignandi G.; Gaiarsa S.; Rettani M.; Bandi C.Colaneri, M.; Seminari, E.; Piralla, A.; Zuccaro, V.; Di Filippo, A.; Baldanti, F.; Bruno, R.; Mondelli, M. U.; Brunetti, E.; Di Matteo, A.; Maiocchi, L.; Pagnucco, L.; Mariani, B.; Ludovisi, S.; Lissandrin, R.; Parisi, A.; Sacchi, P.; Patruno, S. F. A.; Michelone, G.; Gulminetti, R.; Zanaboni, D.; Novati, S.; Maserati, R.; Orsolini, P.; Vecchia, M.; Sciarra, M.; Asperges, E.; Sambo, M.; Biscarini, S.; Lupi, M.; Roda, S.; Chiara Pieri, T.; Gallazzi, I.; Sachs, M.; Valsecchi, P.; Perlini, S.; Alfano, C.; Bonzano, M.; Briganti, F.; Crescenzi, G.; Giulia Falchi, A.; Guarnone, R.; Guglielmana, B.; Maggi, E.; Martino, I.; Pettenazza, P.; Pioli di Marco, S.; Quaglia, F.; Sabena, A.; Salinaro, F.; Speciale, F.; Zunino, I.; De Lorenzo, M.; Secco, G.; Dimitry, L.; Cappa, G.; Maisak, I.; Chiodi, B.; Sciarrini, M.; Barcella, B.; Resta, F.; Moroni, L.; Vezzoni, G.; Scattaglia, L.; Boscolo, E.; Zattera, C.; Michele Fidel, T.; Vincenzo, C.; Vignaroli, D.; Bazzini, M.; Iotti, G.; Mojoli, F.; Belliato, M.; Perotti, L.; Mongodi, S.; Tavazzi, G.; Marseglia, G.; Licari, A.; Brambilla, I.; Daniela, B.; Antonella, B.; Patrizia, C.; Giulia, C.; Giuditta, C.; Marta, C.; D'Alterio, Rossana; Milena, F.; Bianca, M.; Roberta, M.; Enza, M.; Stefania, P.; Maurizio, P.; Elena, P.; Antonio, P.; Francesca, R.; Antonella, S.; Maurizio, Z.; Guy, A.; Laura, B.; Ermanna, C.; Giuliana, C.; Luca, D.; Gabriella, F.; Gabriella, G.; Alessia, G.; Viviana, L.; Meisina, Claudia; Valentina, M.; Simona, P.; Marta, P.; Alice, B.; Giacomo, C.; Irene, C.; Alfonso, C.; Di Martino, R.; Di Napoli, A.; Alessandro, F.; Guglielmo, F.; Loretta, F.; Federica, G.; Albertini, Alessandra; Federica, N.; Giacomo, R.; Beatrice, R.; Maria, S. I.; Monica, T.; Nepita Edoardo, V.; Calvi, M.; Tizzoni, M.; Nicora, C.; Triarico, A.; Petronella, V.; Marena, C.; Muzzi, A.; Lago, P.; Comandatore, F.; Bissignandi, G.; Gaiarsa, S.; Rettani, M.; Bandi, C

Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020

We describe clinical characteristics, treatments and outcomes of 44 Caucasian patients with coronavirus disease (COVID-19) at a single hospital in Pavia, Italy, from 21\u201328 February 2020, at the beginning of the outbreak in Europe. Seventeen patients developed severe disease, two died. After a median of 6 days, 14 patients were discharged from hospital. Predictors of lower odds of discharge were age>65 years, antiviral treatment and for severe disease, lactate dehydrogenase >300 mg/dL

Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility

On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. We use a global structured metapopulation model integrating mobility and transportation data worldwide in order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum likelihood analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1M computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R0 = 1.75 (95% CI 1.64 to 1.88) for the basic reproductive number. Correlation analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak inorder to restore the effectiveness of the vaccination programs.Comment: Paper: 29 Pages, 3 Figures and 5 Tables. Supplementary Information: 29 Pages, 5 Figures and 7 Tables. Print version: http://www.biomedcentral.com/1741-7015/7/4

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models