Severe asthma: One disease and multiple definitions

Introduction: There is, so far, no universal definition of severe asthma. This definition usually relies on: number of exacerbations, inhaled therapy, need for oral corticosteroids, and respiratory function. The use of such parameters varies in the different definitions used. Thus, according to the parameters chosen, each patient may result in having severe asthma or not. The aim of this study was to evaluate how the choice of a specific definition of severe asthma can change the allocation of patients. Methods: Data collected from the Severe Asthma Network Italy (SANI) registry were analyzed. All the patients included were then reclassified according to the definitions of U-BIOPRED, NICE, WHO, ATS/ERS, GINA, ENFUMOSA, and TENOR. Results: 540 patients, were extracted from the SANI database. We observed that 462 (86%) met the ATS/ERS criteria as well as the GINA criteria, 259 (48%) the U-Biopred, 222 (41%) the NICE, 125 (23%) the WHO, 313 (58%) the Enfumosa, and 251 (46%) the TENOR criteria. The mean eosinophil value were similar in the ATS/ERS, U-Biopred, and Enfumosa (528, 532 and 516 cells/mcl), higher in WHO and Tenor (567 and 570 cells/mcl) and much higher in the NICE classification (624 cells/mcl). Lung function tests resulted similarly in all groups, with WHO (67%) and ATS/ERS-GINA (73%), respectively, showing the lower and upper mean FEV1 values. Conclusions: The present observations clearly evidence the heterogeneity in the distribution of patients when different definitions of severe asthma are used. However, the recent definition of severe asthma, provided by the GINA document, is similar to that indicated in 2014 by ATS/ERS, allowing mirror reclassification of the patients examined. This lack of homogeneity could complicate the access to biological therapies. The definition provided by the GINA document, which reflects what suggested by ATS/ERS, could partially overcome the problem

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Accuracy of two optical chlorophyll meters in predicting chemical composition and in vitro ruminal organic matter degradability of Brachiaria hybrid, Megathyrsus maximus, and Paspalum atratum

The objective of this study was to determine the accuracy and reliability of 2 optical chlorophyll meters: FieldScout CM 1,000 NDVI and Yara N-Tester, in predicting neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), acid detergent insoluble nitrogen (ADIN) and in vitro ruminal organic matter degradability (IVOMD) of 3 tropical grasses. Optical chlorophyll measurements were taken at 3 stages (4, 8 and 12 weeks) of regrowth in Brachiaria hybrid, and Megathyrsus maximus and at 6 and 12 weeks of regrowth in Paspalum atratum (cv. Ubon). Optical chlorophyll measurements showed the highest correlation (r = 0.57 to 0.85) with NDF concentration. The FieldScout CM 1,000 NDVI was better than the Yara N-Tester in predicting NDF (R2 = 0.70) and ADF (R2 = 0.79) concentrations in Brachiaria hybrid and NDF (R2 = 0.79) in M. maximus. Similarly, FieldScout CM 1,000 NDVI produced better estimates of 24 h IVOMD (IVOMD24h) in Brachiaria hybrid (R2 = 0.81) and IVOMD48h in Brachiaria hybrid (R2 = 0.65) and M. maximus (R2 = 0.75). However, these prediction models had relatively low concordance correlation coefficients, i.e., CCC >0.90, but random errors were the main source of bias. It was, therefore, concluded that both optical chlorophyll meters were poor and unreliable predictors of ADIN and ADL concentrations. Overall, the FieldScout CM 1,000 NDVI shows potential to produce useful estimates of IVOMD24h and ADF in Brachiaria hybrid and IVOMD48h and NDF concentrations in M. maximus

Severe asthma: One disease and multiple definitions

123noopenIntroduction: There is, so far, no universal definition of severe asthma. This definition usually relies on: number of exacerbations, inhaled therapy, need for oral corticosteroids, and respiratory function. The use of such parameters varies in the different definitions used. Thus, according to the parameters chosen, each patient may result in having severe asthma or not. The aim of this study was to evaluate how the choice of a specific definition of severe asthma can change the allocation of patients. Methods: Data collected from the Severe Asthma Network Italy (SANI) registry were analyzed. All the patients included were then reclassified according to the definitions of U-BIOPRED, NICE, WHO, ATS/ERS, GINA, ENFUMOSA, and TENOR. Results: 540 patients, were extracted from the SANI database. We observed that 462 (86%) met the ATS/ERS criteria as well as the GINA criteria, 259 (48%) the U-Biopred, 222 (41%) the NICE, 125 (23%) the WHO, 313 (58%) the Enfumosa, and 251 (46%) the TENOR criteria. The mean eosinophil value were similar in the ATS/ERS, U-Biopred, and Enfumosa (528, 532 and 516 cells/mcl), higher in WHO and Tenor (567 and 570 cells/mcl) and much higher in the NICE classification (624 cells/mcl). Lung function tests resulted similarly in all groups, with WHO (67%) and ATS/ERS-GINA (73%), respectively, showing the lower and upper mean FEV1 values. Conclusions: The present observations clearly evidence the heterogeneity in the distribution of patients when different definitions of severe asthma are used. However, the recent definition of severe asthma, provided by the GINA document, is similar to that indicated in 2014 by ATS/ERS, allowing mirror reclassification of the patients examined. This lack of homogeneity could complicate the access to biological therapies. The definition provided by the GINA document, which reflects what suggested by ATS/ERS, could partially overcome the problem.restrictedopenBagnasco D.; Paggiaro P.; Latorre M.; Folli C.; Testino E.; Bassi A.; Milanese M.; Heffler E.; Manfredi A.; Riccio A.M.; De Ferrari L.; Blasi F.; Canevari R.F.; Canonica G.W.; Passalacqua G.; Guarnieri G.; Patella V.; Maria Pia F.B.; Carpagnano G.E.; Colle A.D.; Scioscia G.; Gerolamo P.; Puggioni F.; Racca F.; Favero E.; Iannacone S.; Savi E.; Montagni M.; Camiciottoli G.; Allegrini C.; Lombardi C.; Spadaro G.; Detoraki C.; Menzella F.; Galeone C.; Ruggiero P.; Yacoub M.R.; Berti A.; Scichilone N.; Durante C.; Costantino M.T.; Roncallo C.; Braschi M.; D'Adda A.; Ridolo E.; Triggiani M.; Parente R.; Maria D.A.; Verrillo M.V.; Rolla G.; Brussino L.; Frazzetto A.V.; Cristina Z.M.; Lilli M.; Crimi N.; Bonavia M.; Corsico A.G.; Grosso A.; Del Giacco S.; Deidda M.; Ricciardi L.; Isola S.; Cicero F.; Amato G.; Vita F.; Spanevello A.; Pignatti P.; Cherubino F.; Visca D.; Massimo Ricciardolo F.L.; Anna Carriero V.M.; Bertolini F.; Santus P.; Barlassina R.; Airoldi A.; Guida G.; Eleonora N.; Aruanno A.; Rizzi A.; Caruso C.; Colantuono S.; Senna G.; Caminati M.; Arcolaci A.; Vianello A.; Bianchi F.C.; Marchi M.R.; Centanni S.; Luraschi S.; Ruggeri S.; Rinaldo R.; Parazzini E.; Calabrese C.; Flora M.; Cosmi L.; Di Pietro L.; Maggi E.; Pini L.; Macchia L.; Di Bona D.; Richeldi L.; Condoluci C.; Fuso L.; Bonini M.; Farsi A.; Carli G.; Montuschi P.; Santini G.; Conte M.E.; Turchet E.; Barbetta C.; Mazza F.; D'Alo S.; Pucci S.; Caiaffa M.F.; Minenna E.; D'Elia L.; Pasculli C.; Viviano V.; Tarsia P.; Rolo J.; Di Proietto M.; Lo Cicero S.Bagnasco, D.; Paggiaro, P.; Latorre, M.; Folli, C.; Testino, E.; Bassi, A.; Milanese, M.; Heffler, E.; Manfredi, A.; Riccio, A. M.; De Ferrari, L.; Blasi, F.; Canevari, R. F.; Canonica, G. W.; Passalacqua, G.; Guarnieri, G.; Patella, V.; Maria Pia, F. B.; Carpagnano, G. E.; Colle, A. D.; Scioscia, G.; Gerolamo, P.; Puggioni, F.; Racca, F.; Favero, E.; Iannacone, S.; Savi, E.; Montagni, M.; Camiciottoli, G.; Allegrini, C.; Lombardi, C.; Spadaro, G.; Detoraki, C.; Menzella, F.; Galeone, C.; Ruggiero, P.; Yacoub, M. R.; Berti, A.; Scichilone, N.; Durante, C.; Costantino, M. T.; Roncallo, C.; Braschi, M.; D'Adda, A.; Ridolo, E.; Triggiani, M.; Parente, R.; Maria, D. A.; Verrillo, M. V.; Rolla, G.; Brussino, L.; Frazzetto, A. V.; Cristina, Z. M.; Lilli, M.; Crimi, N.; Bonavia, M.; Corsico, A. G.; Grosso, A.; Del Giacco, S.; Deidda, M.; Ricciardi, L.; Isola, S.; Cicero, F.; Amato, G.; Vita, F.; Spanevello, A.; Pignatti, P.; Cherubino, F.; Visca, D.; Massimo Ricciardolo, F. L.; Anna Carriero, V. M.; Bertolini, F.; Santus, P.; Barlassina, R.; Airoldi, A.; Guida, G.; Eleonora, N.; Aruanno, A.; Rizzi, A.; Caruso, C.; Colantuono, S.; Senna, G.; Caminati, M.; Arcolaci, A.; Vianello, A.; Bianchi, F. C.; Marchi, M. R.; Centanni, S.; Luraschi, S.; Ruggeri, S.; Rinaldo, R.; Parazzini, E.; Calabrese, C.; Flora, M.; Cosmi, L.; Di Pietro, L.; Maggi, E.; Pini, L.; Macchia, L.; Di Bona, D.; Richeldi, L.; Condoluci, C.; Fuso, L.; Bonini, M.; Farsi, A.; Carli, G.; Montuschi, P.; Santini, G.; Conte, M. E.; Turchet, E.; Barbetta, C.; Mazza, F.; D'Alo, S.; Pucci, S.; Caiaffa, M. F.; Minenna, E.; D'Elia, L.; Pasculli, C.; Viviano, V.; Tarsia, P.; Rolo, J.; Di Proietto, M.; Lo Cicero, S