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468 results on '"Catania M."'

1. Autonomous Robotic Arm Manipulation for Planetary Missions using Causal Machine Learning

Author

McDonnell, C., Arana-Catania, M., and Upadhyay, S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Computer Science - Machine Learning, Computer Science - Robotics
Abstract

Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on differing causal factors. These are parameters, such as mass or friction coefficient, that causally determine the outcomes of its interactions. Through reinforcement learning, the manipulator learns to interact in ways that reveal the underlying causal factors. We show that this method works even without any prior knowledge of the objects, or any previously-collected training data. We carry out the training in planetary exploration conditions, with realistic manipulator models., Comment: 8 pages, ASTRA 2023: 17th Symposium on Advanced Space Technologies in Robotics and Automation, 18-20 October 2023, Leiden, The Netherlands

Published
2024

2. Causal Discovery to Understand Hot Corrosion

Author

Varghese, A., Arana-Catania, M., Mori, S., Encinas-Oropesa, A., and Sumner, J.

Subjects
Condensed Matter - Materials Science
Abstract

Gas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or co-dependencies between these factors. The causal discovery algorithm Fast Causal Inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified the salt deposition flux as the most influential corrosion variable for this limited dataset. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus FCI generated causal links aligned with literature from a randomised corrosion dataset, while also identifying the presence of two different degradation modes in operation., Comment: 12 pages, published in Materials and Corrosion

Published
2024
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3. PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence

Author

Dougrez-Lewis, John, Kochkina, Elena, Arana-Catania, M., Liakata, Maria, and He, Yulan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying and fact-checking claims based on information from Wikipedia, or trustworthy news articles without considering social media context. However works combining the information from social media with external evidence from the wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension of the PHEME benchmark, which contains social media conversations as well as relevant external evidence for each rumour. We demonstrate the effectiveness of incorporating such evidence in improving rumour verification models. Additionally, as part of the evidence collection, we evaluate various ways of query formulation to identify the most effective method., Comment: 10 pages, 1 figure, 5 tables, presented in the Fifth Fact Extraction and VERification Workshop (FEVER). 2022

Published
2022

4. Supporting peace negotiations in the Yemen war through machine learning

Author

Arana-Catania, M., Van Lier, F. A., and Procter, Rob

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Today's conflicts are becoming increasingly complex, fluid and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the paper also emphasises the importance of interdisciplinary and participatory, co-creation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation., Comment: 28 pages, 16 figures, 2 tables. An earlier version of this paper was presented at the Data for Policy Conference, September, 2021. Current version to appear in Data & Policy journal

Published
2022

6. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims

Author

Arana-Catania, M., Kochkina, Elena, Zubiaga, Arkaitz, Liakata, Maria, Procter, Rob, and He, Yulan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of heterogeneous claims on COVID-19 and their respective information sources. The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims. We then propose novel techniques for automated veracity assessment based on Natural Language Inference including graph convolutional networks and attention based approaches. We have carried out experiments on evidence retrieval and veracity assessment on the dataset using the proposed techniques and found them competitive with SOTA methods, and provided a detailed discussion., Comment: 16 pages, 1 figure, 8 tables, presented in NAACL 2022

Published
2022

7. Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

Author

Arana-Catania, M., Procter, Rob, He, Yulan, and Liakata, Maria

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model., Comment: 8 pages, presented in EMNLP 2021 - New Frontiers in Summarization Workshop

Published
2021

8. A mixed-methods ethnographic approach to participatory budgeting in Scotland

Author

Davies, Jonathan, Arana-Catania, M., Procter, Rob, Van Lier, F. A., and He, Yulan

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Participatory budgeting (PB) is already well established in Scotland in the form of community led grant-making yet has recently transformed from a grass-roots activity to a mainstream process or embedded 'policy instrument'. An integral part of this turn is the use of the Consul digital platform as the primary means of citizen participation. Using a mixed method approach, this ongoing research paper explores how each of the 32 local authorities that make up Scotland utilise the Consul platform to engage their citizens in the PB process and how they then make sense of citizens' contributions. In particular, we focus on whether natural language processing (NLP) tools can facilitate both citizen engagement, and the processes by which citizens' contributions are analysed and translated into policies., Comment: 6 pages, presented in GoodIT 2021 Conference

Published
2021
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9. Machine Learning for Mediation in Armed Conflicts

Author

Arana-Catania, M., Van Lier, F. A., and Procter, Rob

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Today's conflicts are becoming increasingly complex, fluid and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace making, or the identification of key conflict issues and their interdependence. International peace efforts appear increasingly ill-equipped to successfully address these challenges. While technology is being increasingly used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study is the first to apply state-of-the-art machine learning technologies to data from an ongoing mediation process. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning tools can effectively support international mediators by managing knowledge and offering additional conflict analysis tools to assess complex information. Apart from illustrating the potential of machine learning tools in conflict mediation, the paper also emphasises the importance of interdisciplinary and participatory research design for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation., Comment: 24 pages, 16 figures, 2 tables, to be presented in Data for Policy conference

Published
2021

10. Citizen Participation and Machine Learning for a Better Democracy

Author

Arana-Catania, M., Van Lier, F. A., Procter, Rob, Tkachenko, Nataliya, He, Yulan, Zubiaga, Arkaitz, and Liakata, Maria

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens' experience of digital citizen participation platforms. Taking as a case study the "Decide Madrid" Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience., Comment: 19 pages, 5 figures, 4 tables, to appear in Digital Government: Research and Practice (DGOV)

Published
2021
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12. Updated Constraints on General Squark Flavor Mixing

Author

Arana-Catania, M., Heinemeyer, S., and Herrero, M. J.

Subjects
High Energy Physics - Phenomenology
Abstract

We explore the phenomenological implications on non-minimal flavor violating (NMFV) processes from squark flavor mixing within the Minimal Supersymmetric Standard Model. We work under the model-independent hypothesis of general flavor mixing in the squark sector, being parametrized by a complete set of dimensionless delta^AB_ij (A,B = L, R; i,j = u, c, t or d, s, b) parameters. The present upper bounds on the most relevant NMFV processes, together with the requirement of compatibility in the choice of the MSSM parameters with the recent LHC and g-2 data, lead to updated constraints on all squark flavor mixing parameters., Comment: 30 pages, 7 figures. arXiv admin note: text overlap with arXiv:1304.2783, arXiv:1109.6232

Published
2014
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13. The flavour of supersymmetry: Phenomenological implications of sfermion mixing

Author

Arana-Catania, M.

Subjects
High Energy Physics - Phenomenology
Abstract

We study the phenomenological implications of sfermion flavour mixing in supersymmetry in the context of Non-Minimal Flavour Violation (NMFV). We study the general flavour mixing hypothesis, parametrizing the squark and slepton mass matrices by a complete set of delta^XY_ij (X,Y=L,R; i,j= t,c,u or b,s,d for squarks/1,2,3 for sleptons). With respect to the squark sector, we study the behaviour of the B-physics observables BR(B -> Xs gamma), BR(Bs -> mu+ mu-) and delta M_B_s and update the constraints to the delta parameters coming from them. We present one-loop corrections to the Higgs boson masses in the MSSM with NMFV in the squark sector, and taking into account the previous constraints we evaluate them, finding sizable corrections, exceeding sometimes tens of GeV for the light Higgs boson. These corrections might be used to set further constraints on the delta parameters from the Higgs boson mass measurement. With respect to the slepton sector, we explore the implications on charged lepton flavour violating (LFV) processes. The present upper bounds on the most relevant LFV processes and the recent LHC and (g-2)_mu data lead to updated constraints on all slepton flavour mixing parameters. We also study the LFV Higgs decays h,H, A -> tau mu considering the relevant types of slepton mixing (LL23, LR23, RL23, RR23) in the context of a heavy SUSY with a scale into the multi-TeV range. These observables present a non-decoupling behaviour with mSUSY, and are shown here to remain constant as mSUSY grows, for large mSUSY> 2 TeV values and for all the mixings considered. We show that all the three channels could be measurable at the LHC even in these heavy SUSY scenarios, being h -> tau mu the most promising one, with up to about hundred of events expected with the current LHC centre-of-mass energy and luminosity. The most promising predictions for the future LHC stage are also included., Comment: PhD. Thesis. UAM (Madrid), December 2013. PhD Advisors: M.J. Herrero and S. Heinemeyer. 230 pages, 71 figures

Published
2013

14. Non-decoupling SUSY in LFV Higgs decays: a window to new physics at the LHC

Author

Arana-Catania, M., Arganda, E., and Herrero, M. J.

Subjects
High Energy Physics - Phenomenology
Abstract

The recent discovery of a SM-like Higgs boson at the LHC, with a mass around 125-126 GeV, together with the absence of results in the direct searches for supersymmetry, is pushing the SUSY scale ($m_\text{SUSY}$) into the multi-TeV range. This discouraging situation from a low-energy SUSY point of view has its counterpart in indirect SUSY observables which present a non-decoupling behavior with $m_\text{SUSY}$. This is the case of the one-loop lepton flavor violating Higgs decay rates induced by SUSY, which are shown here to remain constant as $m_\text{SUSY}$ grows, for large $m_\text{SUSY} >$ 2 TeV values and for all classes of intergenerational mixing in the slepton sector, $LL$, $LR$, $RL$ and $RR$. In this work we focus on the LFV decays of the three neutral MSSM Higgs bosons $h$, $H$, $A \to \tau \mu$, considering the four types of slepton mixing ($\delta_{23}^{LL}$, $\delta_{23}^{LR}$, $\delta_{23}^{RL}$, $\delta_{23}^{RR}$), and show that all the three channels could be measurable at the LHC, being $h \to \tau \mu$ the most promising one, with up to about hundred of events expected with the current LHC center-of-mass energy and luminosity. The most promising predictions for the future LHC stage are also included., Comment: 24 pages, 10 figures. v5: includes erratum 4 pages, 5 figures

Published
2013
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15. New Constraints on General Slepton Flavor Mixing

Author

Arana-Catania, M., Heinemeyer, S., and Herrero, M. J.

Subjects
High Energy Physics - Phenomenology
Abstract

We explore the phenomenological implications on charged lepton flavor violating (LFV) processes from slepton flavor mixing within the Minimal Supersymmetric Standard Model. We work under the model-independent hypothesis of general flavor mixing in the slepton sector, being parametrized by a complete set of dimensionless delta^AB_ij (A,B = L,R; i,j = 1, 2, 3) parameters. The present upper bounds on the most relevant LFV processes, together with the requirement of compatibility in the choice of the MSSM parameters with the recent LHC and (g-2) data, lead to updated constraints on all slepton flavor mixing parameters. A comparative discussion of the most effective LFV processes to constrain the various generation mixings is included., Comment: 42 pages, 19 figures. Minor changes, version to appear in PRD

Published
2013
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16. The Higgs sector of the NMFV MSSM at the ILC

Author

Arana-Catania, M., Heinemeyer, S., Herrero, M. J., and Penaranda, S.

Subjects
High Energy Physics - Phenomenology
Abstract

We calculate the one-loop corrections to the Higgs boson masses within the context of the MSSM with Non-Minimal Flavor Violation in the squark sector. We take into account all the relevant restrictions from BR(B -> X_s gamma), BR(B_s -> mu^+ mu^-) and \Delta M_{B_s}. We find sizable corrections to the lightest Higgs boson mass that are considerably larger than the expected ILC precision for acceptable values of the mixing parameters deltas. We find delta^{LR}_{ct} and delta^{RL}_{ct} specially relevant, mainly at low tan beta., Comment: LaTex, 7 pages, 3 figures. The 2011 International Workshop on Future Linear Colliders (LCWS11), Granada, Spain

Published
2012

17. Higgs Boson masses and B-Physics Constraints in Non-Minimal Flavor Violating SUSY scenarios

Author

Arana-Catania, M., Heinemeyer, S., Herrero, M. J., and Penaranda, S.

Subjects
High Energy Physics - Phenomenology
Abstract

We present one-loop corrections to the Higgs boson masses in the MSSM with Non-Minimal Flavor Violation. The flavor violation is generated from the hypothesis of general flavor mixing in the squark mass matrices, and these are parameterized by a complete set of delta^XY_ij (X, Y = L,R; i; j = t, c, u or b, s, d). We calculate the corrections to the Higgs masses in terms of these delta^XY_ij taking into account all relevant restrictions from B-physics data. This includes constraints from BR(B -> Xs gamma), BR(Bs -> mu+ mu-) and delta M_B_s . After taking into account these constraints we find sizable corrections to the Higgs boson masses, in the case of the lightest MSSM Higgs boson mass exceeding tens of GeV. These corrections are found mainly for the low tan beta case. In the case of a Higgs boson mass measurement these corrections might be used to set further constraints on delta^XY_ij., Comment: 58 pages, 15 figures, Minor modifications, version to appear in JHEP

Published
2011
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19. Global consortium for the classification of fungi and fungus-like taxa

Author

Hyde, K. D., Abdel-Wahab, M. A., Abdollahzadeh, J., Abeywickrama, P. D., Absalan, S., Afshari, N., Ainsworth, A. M., Akulov, O. Y., Aleoshin, V. V., Al-Sadi, A. M., Alvarado, P., Alves, A., Alves-Silva, G., Amalfi, M., Amira, Y., Amuhenage, T. B., Anderson, J. L., Antonín, V., Aouali, S., Aptroot, A., Apurillo, C. C. S., Araújo, J. P.M., Ariyawansa, H. A., Armand, A., Arumugam, E., Asghari, R., Assis, D. M.A., Atienza, V., Avasthi, S., Azevedo, E., Bahkali, A. H., Bakhshi, M., Banihashemi, Z., Bao, D. F., Baral, H. O., Barata, M., Barbosa, F. R., Barbosa, R. N., Barreto, R. W., Baschien, C., Belamesiatseva, D. B., Reuel, M. Bennett, Bera, I., Bezerra, J. D. P., Bezerra, J. L., Bhat, D. J., Bhunjun, C. S., Bianchinotti, M. V., Błaszkowski, J., Blondelle, A., Boekhout, T., Bonito, G., Boonmee, S., Boonyuen, N., Bregant, C., Buchanan, P., Bundhun, D., Burgaud, G., Burgess, T., Buyck, B., Cabarroi-Hernández, M., Cáceres, M. E. S., Caeiro, M. F., Cai, L., Cai, M. F., Calabon, M. S., Calaça, F. J. S., Callalli, M., Camara, M. P. S., Cano-Lira, J. F., Cantillo, T., Cao, B., Carlavilla, J. R., Carvalho, A., Castañeda-Ruiz, R. F., Castlebury, L., Castro-Jauregui, O., Catania, M. D., Cavalcanti, L. H., Cazabonne, J., Cedeño-Sanchez, M. L., Chaharmiri-Dokhaharani, S., Chaiwan, N., Chakraborty, N., Chaverri, P., Cheewangkoon, R., Chen, C., Chen, C. Y., Chen, K. H., Chen, J., Chen, Q., Chen, W. H., Chen, Y. P., Chethana, K. W. T., Coleine, C., Condé, T. O., Corazon-Guivin, M. A., Cortés-Pérez, A., Costa-Rezende, D. H., Courtecuisse, R., Crouch, J. A., Crous, P. W., Cui, B. K., Cui, Y. Y., da Silva, D. K. A., da Silva, G. A., da Silva, I. R., da Silva, R. M. F., da Silva Santos, A. C., Dai, D. Q., Dai, Y. C., Damm, U., Darmostuk, V., Zoha, Daroodi, Das, K., Davoodian, N., Davydov, E. A., Dayarathne, M. C., Decock, C., de Groot, M. D., De Kesel, A., de la Cruz, T. E. E., De Lange, R., Delgado, G., Denchev, C. M., Denchev, T. T., de Oliveira, N. T., de Silva, N. I., de Souza, F. A., Dentinger, B., Devadatha, B., Dianese, J. C., Dima, B., Diniz, A. G., Dissanayake, A. J., Dissanayake, L. S., Doğan, H. H., Doilom, M., Dolatabadi, S., Dong, W., Dong, Z. Y., Dos Santos, L. A., Drechsler-Santos, E. R., Du, T. Y., Dubey, M. K., Dutta, A. K., Egidi, E., Elliott, T. F., Elshahed, M. S., Erdoğdu, M., Ertz, D., Etayo, J., Evans, H. C., Fan, X. L., Fan, Y. G., Fedosova, A. G., Fell, J., Fernandes, I., Firmino, A. L., Fiuza, P. O., Flakus, A., de Souza, C. A.Fragoso, Frisvad, J. C., Fryar, S. C., Gabaldón, T., Gajanayake, A. J., Galindo, L. J., Gannibal, P. B., García, D., García-Sandoval, S. R., Garrido-Benavent, I., Garzoli, L., Gautam, A. K., Ge, Z. W., Gené, D. J., Gentekaki, E., Ghobad-Nejhad, M., Giachini, A. J., Gibertoni, T. B., Góes-Neto, A., Gomdola, D., de Farias, A. R. Gomes, Gorjón, S. P., Goto, B. T., Granados-Montero, M. M., Griffith, G. W., Groenewald, J. Z., Groenewald, M., Grossart, H. P., Gueidan, C., Gunarathne, A., Gunaseelan, S., Gusmão, L. F.P., Gutierrez, A. C., Guzmán-Dávalos, L., Haelewaters, D., Halling, R., Han, Y. F., Hapuarachchi, K. K., Harder, C. B., Harrington, T. C., Hattori, T., He, M. Q., He, S., He, S. H., Healy, R., Herández-Restrepo, M., Heredia, G., Hodge, K. T., Holgado-Rojas, M., Hongsanan, S., Horak, E., Hosoya, T., Houbraken, J., Huang, S. K., Huanraluek, N., Hur, J. S., Hurdeal, V. G., Hustad, V. P., Iotti, M., Iturriaga, T., Jafar, E., Janik, P., Jany, J. L., Jayalal, R. G.U., Jayasiri, S. C., Jayawardena, R. S., Jeewon, R., Jerônimo, G. H., Jesus, A. L., Jin, J., Johnston, P. R., Jones, E. B.G., Joshi, Y., Justo, A., Kaishian, P., Kakishima, M., Kaliyaperumal, M., Kang, G. P., Kang, J. C., Karakehian, J. M., Karimi, O., Karpov, S. A., Karunarathna, S. C., Kaufmann, M., Kemler, M., Kezo, K., Khyaju, S., Kirchmair, M., Kirk, P. M., Kitaura, M. J., Klawonn, I., Kolarik, M., Kong, A., Kuhar, F., Kukwa, M., Kumar, S., Kušan, I., Lado, C., Larsson, K. H., Latha, K. P.D., Lee, H. B., Leonardi, M., Leontyev, D. L., Lestari, A. S., Li, C. J.Y., Li, D. W., Li, H. Y., Li, L., Li, Q. R., Li, W. L., Li, Y., Li, Y. C., Liao, C. F., Liimatainen, K., Lim, Y. W., Lin, C. G., Linaldeddu, B. T., Linde, C. C., Linn, M. M., Liu, F., Liu, J. K., Liu, N. G., Liu, S., Liu, X. F., Liu, X. Z., Liu, Z. B., Lu, L., Lu, Y. Z., Luangharn, T., Luangsa-ard, J. J., Lumbsch, H. T., Lumyong, S., Luo, L., Luo, M., Luo, Z. L., Ma, J., Machado, A. R., Madagammana, A. D., Madrid, H., Magurno, F., Magyar, D., Mahadevan, N., Maharachchikumbura, S. S.N., Maimaiti, Y., Malosso, E., Manamgoda, D. S., Manawasinghe, I. S., Mapook, A., Marasinghe, D. S., Mardones, M., Marin-Felix, Y., Márquez, R., Masigol, H., Matočec, N., May, T. W., McKenzie, E. H.C., Meiras-Ottoni, A., Melo, R. F.R., Mendes-Alvarenga, R. L., Mendieta, S., Meng, Q. F., Menkis, A., Menolli, N., Mešić, A., Calvo, J. G.Meza, Mikhailov, K. V., Miller, S. L., Moncada, B., Moncalvo, J. M., Monteiro, J. S., Monteiro, M., Mora-Montes, H. M., Moreau, P. A., Mueller, G. M., Mukhopadyay, S., Murugadoss, R., Nagy, L. G., Najafiniya, M., Nanayakkara, C. M., Nascimento, C. C., Nei, Y., Neves, M. A., Neuhauser, S., Niego, A. G.T., Nilsson, R. H., Niskanen, T., Niveiro, N., Noorabadi, M. T., Noordeloos, M. E., Norphanphoun, C., Otaño, N. B.Nuñez, O’Donnell, R. P., Oehl, F., Olariaga, I., Orlando, O. P., Pang, K. L., Papp, V., Pawłowska, J., Peintner, U., Pem, D., Pereira, O. L., Perera, R. H., Perez-Moreno, J., Perez-Ortega, S., Péter, G., Phillips, A. J.L., Phonemany, M., Phukhamsakda, C., Phutthacharoen, K., Piepenbring, M., Pires-Zottarelli, C. L.A., Poinar, G., Pošta, A., Prieto, M., Promputtha, I., Quandt, C. A., Radek, R., Rahnama, K., Raj, K. N.A., Rajeshkumar, K. C., Rämä, T., Rambold, G., Ramírez-Cruz, V., Rasconi, S., Rathnayaka, A. R., Raza, M., Ren, G. C., Robledo, G. L., Rodriguez-Flakus, P., Ronikier, A., Rossi, W., Ryberg, M., Ryvarden, L. R., Salvador-Montoya, C. A., Samant, B., Samarakoon, B. C., Samarakoon, M. C., Sánchez-Castro, I., Sánchez-García, M., Sandoval-Denis, M., Santamaria, B., Santiago, A. L.C.M.A., Sarma, V. V., Savchenko, A., Savchenko, K., Saxena, R. K., Scholler, M., Schoutteten, N., Seifollahi, E., Selbmann, L., Selcuk, F., Senanayake, I. C., Shabashova, T. G., Shen, H. W., Shen, Y. M., Silva-Filho, A. G.S., Simmons, D. R., Singh, R., Sir, E. B., Song, C. G., Souza-Motta, C. M., Sruthi, O. P., Stadler, M., Stchigel, A. M., Stemler, J., Stephenson, S. L., Strassert, J. F.H., Su, H. L., Su, L., Suetrong, S., Sulistyo, B., Sun, Y. R., Svantesson, S., Sysouphanthong, P., Takamatsu, S., Tan, T. H., Tanaka, K., Tang, A. M.C., Tang, X., Tanney, J. B., Tavakol, N. M., Taylor, J. E., Taylor, P. W.J., Tedersoo, L., Tennakoon, D. S., Thamodini, G. K., Thines, M., Thiyagaraja, V., Thongklang, N., Tiago, P. V., Tian, Q., Tian, W. H., Tibell, L., Tibell, S., Tibpromma, S., Tkalčec, Z., Tomšovský, M., Toome-Heller, M., Torruella, G., Tsurykau, A., Udayanga, D., Ulukapi, M., Untereiner, W. A., Uzunov, B. A., Valle, L. G., Van Caenegem, W., Van den Wyngaert, S., Van Vooren, N., Velez, P., Verma, R. K., Vieira, L. C., Vieira, W. A.S., Vizzini, A., Walker, A., Walker, A. K., Wanasinghe, D. N., Wang, C. G., Wang, K., Wang, S. X., Wang, X. Y., Wang, Y., Wannasawang, N., Wartchow, F., Wei, D. P., Wei, X. L., White, J. F., Wijayawardene, N. N., Wijesinghe, S. N., Wijesundara, D. S.A., Wisitrassameewong, K., Worthy, F. R., Wu, F., Wu, G., Wu, H. X., Wu, N., Wu, W. P., Wurzbacher, C., Xiao, Y. P., Xiong, Y. R., Xu, B., Xu, L. J., Xu, R., Xu, T. M., Yakovchenko, L., Yan, J. Y., Yang, H. D., Yang, J., Yang, Z. L., Yang, Y. H., Yapa, N., Yasanthika, E., Youssef, N. H., Yu, F. M., Yu, Q., Yu, X. D., Yu, Y. X., Yu, Z. F., Yuan, H. S., Yuan, Y., Yurkov, A., Zafari, D., Zamora, J. C., Zare, R., Zeng, M., Zeng, N. K., Zeng, X. Y., Zhang, F., Zhang, H., Zhang, J. F., Zhang, J. Y., Zhang, Q. Y., Zhang, S. N., Zhang, W., Zhang, Y., Zhao, C. L., Zhao, H., Zhao, Q., Zhao, R. L., Zhou, L. W., Zhou, M., Zhurbenko, M. P., Zin, H. H., Zucconi, L., Hyde, K. D., Abdel-Wahab, M. A., Abdollahzadeh, J., Abeywickrama, P. D., Absalan, S., Afshari, N., Ainsworth, A. M., Akulov, O. Y., Aleoshin, V. V., Al-Sadi, A. M., Alvarado, P., Alves, A., Alves-Silva, G., Amalfi, M., Amira, Y., Amuhenage, T. B., Anderson, J. L., Antonín, V., Aouali, S., Aptroot, A., Apurillo, C. C. S., Araújo, J. P.M., Ariyawansa, H. A., Armand, A., Arumugam, E., Asghari, R., Assis, D. M.A., Atienza, V., Avasthi, S., Azevedo, E., Bahkali, A. H., Bakhshi, M., Banihashemi, Z., Bao, D. F., Baral, H. O., Barata, M., Barbosa, F. R., Barbosa, R. N., Barreto, R. W., Baschien, C., Belamesiatseva, D. B., Reuel, M. Bennett, Bera, I., Bezerra, J. D. P., Bezerra, J. L., Bhat, D. J., Bhunjun, C. S., Bianchinotti, M. V., Błaszkowski, J., Blondelle, A., Boekhout, T., Bonito, G., Boonmee, S., Boonyuen, N., Bregant, C., Buchanan, P., Bundhun, D., Burgaud, G., Burgess, T., Buyck, B., Cabarroi-Hernández, M., Cáceres, M. E. S., Caeiro, M. F., Cai, L., Cai, M. F., Calabon, M. S., Calaça, F. J. S., Callalli, M., Camara, M. P. S., Cano-Lira, J. F., Cantillo, T., Cao, B., Carlavilla, J. R., Carvalho, A., Castañeda-Ruiz, R. F., Castlebury, L., Castro-Jauregui, O., Catania, M. D., Cavalcanti, L. H., Cazabonne, J., Cedeño-Sanchez, M. L., Chaharmiri-Dokhaharani, S., Chaiwan, N., Chakraborty, N., Chaverri, P., Cheewangkoon, R., Chen, C., Chen, C. Y., Chen, K. H., Chen, J., Chen, Q., Chen, W. H., Chen, Y. P., Chethana, K. W. T., Coleine, C., Condé, T. O., Corazon-Guivin, M. A., Cortés-Pérez, A., Costa-Rezende, D. H., Courtecuisse, R., Crouch, J. A., Crous, P. W., Cui, B. K., Cui, Y. Y., da Silva, D. K. A., da Silva, G. A., da Silva, I. R., da Silva, R. M. F., da Silva Santos, A. C., Dai, D. Q., Dai, Y. C., Damm, U., Darmostuk, V., Zoha, Daroodi, Das, K., Davoodian, N., Davydov, E. A., Dayarathne, M. C., Decock, C., de Groot, M. D., De Kesel, A., de la Cruz, T. E. E., De Lange, R., Delgado, G., Denchev, C. M., Denchev, T. T., de Oliveira, N. T., de Silva, N. I., de Souza, F. A., Dentinger, B., Devadatha, B., Dianese, J. C., Dima, B., Diniz, A. G., Dissanayake, A. J., Dissanayake, L. S., Doğan, H. H., Doilom, M., Dolatabadi, S., Dong, W., Dong, Z. Y., Dos Santos, L. A., Drechsler-Santos, E. R., Du, T. Y., Dubey, M. K., Dutta, A. K., Egidi, E., Elliott, T. F., Elshahed, M. S., Erdoğdu, M., Ertz, D., Etayo, J., Evans, H. C., Fan, X. L., Fan, Y. G., Fedosova, A. G., Fell, J., Fernandes, I., Firmino, A. L., Fiuza, P. O., Flakus, A., de Souza, C. A.Fragoso, Frisvad, J. C., Fryar, S. C., Gabaldón, T., Gajanayake, A. J., Galindo, L. J., Gannibal, P. B., García, D., García-Sandoval, S. R., Garrido-Benavent, I., Garzoli, L., Gautam, A. K., Ge, Z. W., Gené, D. J., Gentekaki, E., Ghobad-Nejhad, M., Giachini, A. J., Gibertoni, T. B., Góes-Neto, A., Gomdola, D., de Farias, A. R. Gomes, Gorjón, S. P., Goto, B. T., Granados-Montero, M. M., Griffith, G. W., Groenewald, J. Z., Groenewald, M., Grossart, H. P., Gueidan, C., Gunarathne, A., Gunaseelan, S., Gusmão, L. F.P., Gutierrez, A. C., Guzmán-Dávalos, L., Haelewaters, D., Halling, R., Han, Y. F., Hapuarachchi, K. K., Harder, C. B., Harrington, T. C., Hattori, T., He, M. Q., He, S., He, S. H., Healy, R., Herández-Restrepo, M., Heredia, G., Hodge, K. T., Holgado-Rojas, M., Hongsanan, S., Horak, E., Hosoya, T., Houbraken, J., Huang, S. K., Huanraluek, N., Hur, J. S., Hurdeal, V. G., Hustad, V. P., Iotti, M., Iturriaga, T., Jafar, E., Janik, P., Jany, J. L., Jayalal, R. G.U., Jayasiri, S. C., Jayawardena, R. S., Jeewon, R., Jerônimo, G. H., Jesus, A. L., Jin, J., Johnston, P. R., Jones, E. B.G., Joshi, Y., Justo, A., Kaishian, P., Kakishima, M., Kaliyaperumal, M., Kang, G. P., Kang, J. C., Karakehian, J. M., Karimi, O., Karpov, S. A., Karunarathna, S. C., Kaufmann, M., Kemler, M., Kezo, K., Khyaju, S., Kirchmair, M., Kirk, P. M., Kitaura, M. J., Klawonn, I., Kolarik, M., Kong, A., Kuhar, F., Kukwa, M., Kumar, S., Kušan, I., Lado, C., Larsson, K. H., Latha, K. P.D., Lee, H. B., Leonardi, M., Leontyev, D. L., Lestari, A. S., Li, C. J.Y., Li, D. W., Li, H. Y., Li, L., Li, Q. R., Li, W. L., Li, Y., Li, Y. C., Liao, C. F., Liimatainen, K., Lim, Y. W., Lin, C. G., Linaldeddu, B. T., Linde, C. C., Linn, M. M., Liu, F., Liu, J. K., Liu, N. G., Liu, S., Liu, X. F., Liu, X. Z., Liu, Z. B., Lu, L., Lu, Y. Z., Luangharn, T., Luangsa-ard, J. J., Lumbsch, H. T., Lumyong, S., Luo, L., Luo, M., Luo, Z. L., Ma, J., Machado, A. R., Madagammana, A. D., Madrid, H., Magurno, F., Magyar, D., Mahadevan, N., Maharachchikumbura, S. S.N., Maimaiti, Y., Malosso, E., Manamgoda, D. S., Manawasinghe, I. S., Mapook, A., Marasinghe, D. S., Mardones, M., Marin-Felix, Y., Márquez, R., Masigol, H., Matočec, N., May, T. W., McKenzie, E. H.C., Meiras-Ottoni, A., Melo, R. F.R., Mendes-Alvarenga, R. L., Mendieta, S., Meng, Q. F., Menkis, A., Menolli, N., Mešić, A., Calvo, J. G.Meza, Mikhailov, K. V., Miller, S. L., Moncada, B., Moncalvo, J. M., Monteiro, J. S., Monteiro, M., Mora-Montes, H. M., Moreau, P. A., Mueller, G. M., Mukhopadyay, S., Murugadoss, R., Nagy, L. G., Najafiniya, M., Nanayakkara, C. M., Nascimento, C. C., Nei, Y., Neves, M. A., Neuhauser, S., Niego, A. G.T., Nilsson, R. H., Niskanen, T., Niveiro, N., Noorabadi, M. T., Noordeloos, M. E., Norphanphoun, C., Otaño, N. B.Nuñez, O’Donnell, R. P., Oehl, F., Olariaga, I., Orlando, O. P., Pang, K. L., Papp, V., Pawłowska, J., Peintner, U., Pem, D., Pereira, O. L., Perera, R. H., Perez-Moreno, J., Perez-Ortega, S., Péter, G., Phillips, A. J.L., Phonemany, M., Phukhamsakda, C., Phutthacharoen, K., Piepenbring, M., Pires-Zottarelli, C. L.A., Poinar, G., Pošta, A., Prieto, M., Promputtha, I., Quandt, C. A., Radek, R., Rahnama, K., Raj, K. N.A., Rajeshkumar, K. C., Rämä, T., Rambold, G., Ramírez-Cruz, V., Rasconi, S., Rathnayaka, A. R., Raza, M., Ren, G. C., Robledo, G. L., Rodriguez-Flakus, P., Ronikier, A., Rossi, W., Ryberg, M., Ryvarden, L. R., Salvador-Montoya, C. A., Samant, B., Samarakoon, B. C., Samarakoon, M. C., Sánchez-Castro, I., Sánchez-García, M., Sandoval-Denis, M., Santamaria, B., Santiago, A. L.C.M.A., Sarma, V. V., Savchenko, A., Savchenko, K., Saxena, R. K., Scholler, M., Schoutteten, N., Seifollahi, E., Selbmann, L., Selcuk, F., Senanayake, I. C., Shabashova, T. G., Shen, H. W., Shen, Y. M., Silva-Filho, A. G.S., Simmons, D. R., Singh, R., Sir, E. B., Song, C. G., Souza-Motta, C. M., Sruthi, O. P., Stadler, M., Stchigel, A. M., Stemler, J., Stephenson, S. L., Strassert, J. F.H., Su, H. L., Su, L., Suetrong, S., Sulistyo, B., Sun, Y. R., Svantesson, S., Sysouphanthong, P., Takamatsu, S., Tan, T. H., Tanaka, K., Tang, A. M.C., Tang, X., Tanney, J. B., Tavakol, N. M., Taylor, J. E., Taylor, P. W.J., Tedersoo, L., Tennakoon, D. S., Thamodini, G. K., Thines, M., Thiyagaraja, V., Thongklang, N., Tiago, P. V., Tian, Q., Tian, W. H., Tibell, L., Tibell, S., Tibpromma, S., Tkalčec, Z., Tomšovský, M., Toome-Heller, M., Torruella, G., Tsurykau, A., Udayanga, D., Ulukapi, M., Untereiner, W. A., Uzunov, B. A., Valle, L. G., Van Caenegem, W., Van den Wyngaert, S., Van Vooren, N., Velez, P., Verma, R. K., Vieira, L. C., Vieira, W. A.S., Vizzini, A., Walker, A., Walker, A. K., Wanasinghe, D. N., Wang, C. G., Wang, K., Wang, S. X., Wang, X. Y., Wang, Y., Wannasawang, N., Wartchow, F., Wei, D. P., Wei, X. L., White, J. F., Wijayawardene, N. N., Wijesinghe, S. N., Wijesundara, D. S.A., Wisitrassameewong, K., Worthy, F. R., Wu, F., Wu, G., Wu, H. X., Wu, N., Wu, W. P., Wurzbacher, C., Xiao, Y. P., Xiong, Y. R., Xu, B., Xu, L. J., Xu, R., Xu, T. M., Yakovchenko, L., Yan, J. Y., Yang, H. D., Yang, J., Yang, Z. L., Yang, Y. H., Yapa, N., Yasanthika, E., Youssef, N. H., Yu, F. M., Yu, Q., Yu, X. D., Yu, Y. X., Yu, Z. F., Yuan, H. S., Yuan, Y., Yurkov, A., Zafari, D., Zamora, J. C., Zare, R., Zeng, M., Zeng, N. K., Zeng, X. Y., Zhang, F., Zhang, H., Zhang, J. F., Zhang, J. Y., Zhang, Q. Y., Zhang, S. N., Zhang, W., Zhang, Y., Zhao, C. L., Zhao, H., Zhao, Q., Zhao, R. L., Zhou, L. W., Zhou, M., Zhurbenko, M. P., Zin, H. H., and Zucconi, L.

Abstract

The Global Consortium for the Classification of Fungi and fungus-like taxa is an international initiative of more than 550 mycologists to develop an electronic structure for the classification of these organisms. The members of the Consortium originate from 55 countries/regions worldwide, from a wide range of disciplines, and include senior, mid-career and early-career mycologists and plant pathologists. The Consortium will publish a biannual update of the Outline of Fungi and fungus-like taxa, to act as an international scheme for other scientists. Notes on all newly published taxa at or above the level of species will be prepared and published online on the Outline of Fungi website (https://www.outlineoffungi.org/), and these will be finally published in the biannual edition of the Outline of Fungi and fungus-like taxa. Comments on recent important taxonomic opinions on controversial topics will be included in the biannual outline. For example, 'to promote a more stable taxonomy in Fusarium given the divergences over its generic delimitation', or 'are there too many genera in the Boletales?' and even more importantly, 'what should be done with the tremendously diverse 'dark fungal taxa?' There are undeniable differences in mycologists' perceptions and opinions regarding species classification as well as the establishment of new species. Given the pluralistic nature of fungal taxonomy and its implications for species concepts and the nature of species, this consortium aims to provide a platform to better refine and stabilise fungal classification, taking into consideration views from different parties. In the future, a confidential voting system will be set up to gauge the opinions of all mycologists in the Consortium on important topics. The results of such surveys will be presented to the International Commission on the Taxonomy of Fungi (ICTF) and the Nomenclature Committee

Published
2023

21. Invasive Fungal Infections in Hospitalized Patients with COVID-19: A Non-Intensive Care Single-Centre Experience during the First Pandemic Waves

Author

Cattaneo L., Buonomo A. R., Iacovazzo C., Giaccone A., Scotto R., Viceconte G., Mercinelli S., Vargas M., Roscetto E., Cacciatore F., Salvatore P., Catania M. R., Villari R., Cittadini A., Gentile I., Cattaneo, L., Buonomo, A. R., Iacovazzo, C., Giaccone, A., Scotto, R., Viceconte, G., Mercinelli, S., Vargas, M., Roscetto, E., Cacciatore, F., Salvatore, P., Catania, M. R., Villari, R., Cittadini, A., and Gentile, I.

Subjects
invasive aspergillosi, pneumocystosis, Microbiology (medical), IFIs, COVID-19, invasive aspergillosis, IFI, Plant Science, Ecology, Evolution, Behavior and Systematics
Abstract

Invasive fungal infections (IFIs) represent a severe complication of COVID-19, yet they are under-estimated. We conducted a retrospective analysis including all the COVID-19 patients admitted to the Infectious Diseases Unit of the Federico II University Hospital of Naples until the 1 July 2021. Among 409 patients, we reported seven cases of IFIs by Candida spp., seven of Pneumocystis jirovecii pneumonia, three of invasive pulmonary aspergillosis, and one of Trichosporon asahii. None of the cases presented underlying predisposing conditions, excluding one oncohematological patient treated with rituximab. Ten cases showed lymphopenia with high rates of CD4+ < 200/µL. All cases received high-dose steroid therapy (mean duration 33 days, mean cumulative dosage 1015 mg of prednisone equivalent), and seven cases had severe COVID-19 disease (OSCI ≥ 5) prior to IFI diagnosis. The cases showed a higher overall duration of hospitalization (63 vs 24 days) and higher mortality rate (23% vs. 7%) compared with the COVID-19 patients who did not developed IFIs. Cases showed a higher prevalence of high-dose steroid therapy and lymphopenia with CD4+ < 200/µL, primarily due to SARS-CoV-2 infection and not related to underlying comorbidities. IFIs strongly impact the overall length of hospitalization and mortality. Therefore, clinicians should maintain a high degree of suspicion of IFIs, especially in severe COVID-19 patients.

Published
2023
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25. The changing epidemiology of carbapenemase-producing Klebsiella pneumoniae in Italy: Toward polyclonal evolution with emergence of high-risk lineages

Author

Di Pilato V., Errico G., Monaco M., Giani T., Del Grosso M., Antonelli A., David S., Lindh E., Camilli R., Aanensen D. M., Rossolini G. M., Pantosti A., Manso E., Pedna M. F., Mungiguerra M., Mosca A., Vailati F., Aschbacher R., Imbriani A., Sartore P., Giraldi C., Piana F., Pecile P., de Nittis R., Pini B., Mirri P., Bianchi E., Restelli A., Morelli D., Catania M. R., Barbaro A., Bernaschi P, Parisi G, Gualdi P, Dusi PA, Bona R, D'Andrea M M, Cavallo R, Lanzafame P, Sartor A, Grandesso S, Milano F, Di Pilato, V., Errico, G., Monaco, M., Giani, T., Del Grosso, M., Antonelli, A., David, S., Lindh, E., Camilli, R., Aanensen, D. M., Rossolini, G. M., Pantosti, A., Manso, E., Pedna, M. F., Mungiguerra, M., Mosca, A., Vailati, F., Aschbacher, R., Imbriani, A., Sartore, P., Giraldi, C., Piana, F., Pecile, P., de Nittis, R., Pini, B., Mirri, P., Bianchi, E., Restelli, A., Morelli, D., Catania, M. R., Barbaro, A., Bernaschi, P, Parisi, G, Gualdi, P, Dusi, Pa, Bona, R, D'Andrea, M M, Cavallo, R, Lanzafame, P, Sartor, A, Grandesso, S, and Milano, F

Subjects
Microbiology (medical), Imipenem, Klebsiella pneumoniae, Population, Microbial Sensitivity Tests, Meropenem, beta-Lactamases, Settore MED/07, Antibiotic resistance, Bacterial Proteins, Genotype, medicine, Humans, Pharmacology (medical), education, Pharmacology, Genetics, education.field_of_study, biology, Settore BIO/19, biology.organism_classification, Anti-Bacterial Agents, Klebsiella Infections, Resistome, Infectious Diseases, Italy, Multilocus sequence typing, Multilocus Sequence Typing, medicine.drug
Abstract

BackgroundPrevious studies showed that the epidemic of carbapenem-resistant Klebsiella pneumoniae (CR-KP) observed in Italy since 2010 was sustained mostly by strains of clonal group (CG) 258 producing KPC-type carbapenemases. In the framework of the National Antibiotic-Resistance Surveillance (AR-ISS), a countrywide survey was conducted in 2016 to explore the evolution of the phenotypic and genotypic characteristics of CR-KP isolates.MethodsFrom March to July 2016, hospital laboratories participating in AR-ISS were requested to provide consecutive, non-duplicated CR-KP (meropenem and/or imipenem MIC >1 mg/L) from invasive infections. Antibiotic susceptibility was determined according to EUCAST recommendations. A WGS approach was adopted to characterize the isolates by investigating phylogeny, resistome and virulome.ResultsTwenty-four laboratories provided 157 CR-KP isolates, of which 156 were confirmed as K. pneumoniae sensu stricto by WGS and found to carry at least one carbapenemase-encoding gene, corresponding in most cases (96.1%) to blaKPC. MLST- and SNP-based phylogeny revealed that 87.8% of the isolates clustered in four major lineages: CG258 (47.4%), with ST512 as the most common clone, CG307 (19.9%), ST101 (15.4%) and ST395 (5.1%). A close association was identified between lineages and antibiotic resistance phenotypes and genotypes, virulence traits and capsular types. Colistin resistance, mainly associated with mgrB mutations, was common in all major lineages except ST395.ConclusionsThis WGS-based survey showed that, although CG258 remained the most common CR-KP lineage in Italy, a polyclonal population has emerged with the spread of the new high-risk lineages CG307, ST101 and ST395, while KPC remained the most common carbapenemase.

Published
2021

31. The fine line between persuasion and digital addiction

Author

Cemiloglu, D. (Deniz), Naiseh, M. (Mohammad), Catania, M. (Maris), Oinas-Kukkonen, H. (Harri), Ali, R. (Raian), Cemiloglu, D. (Deniz), Naiseh, M. (Mohammad), Catania, M. (Maris), Oinas-Kukkonen, H. (Harri), and Ali, R. (Raian)

Abstract

Digital addiction is becoming a prevalent societal concern and persuasive design techniques used in digital platforms might be accountable also for the development and maintenance of such problematic behavior. This paper theoretically analyses the relationship between persuasive system design principles and digital addiction in light of theories on behavioral and substance-based addictions. The findings suggest that some of the persuasive design principles, in specific contexts, may trigger and expedite digital addiction. The purpose of this paper is to open a discussion around the potential effects of persuasive technology on digital addiction and cater to this risk in the design processes and the persuasive design itself.

Published
2021

34. Development of a low-cost seismic station based on MEMS technology

Author

D'Alessandro, A, Greco, L, Scudero, S, Siino, M, VITALE, Giovanni, D'Anna, R, Di Gangi, F, Nicolosi, D, Passafiume, G, Speciale, S, Catania, M, Cosenza, P, Martin, LF, Patane, D, Martorana, R, Rao, S, Stramondo, S, D'Alessandro, A, Greco, L, Scudero, S, Siino, M, Vitale, G, D'Anna, R, Di Gangi, F, Nicolosi, D, Passafiume, G, Speciale, S, Catania, M, Cosenza, P, Martin, LF, Patane, D, Martorana, R, Rao, S, and Stramondo, S

Subjects
MEMS, Seimic station
Abstract

L’impatto di un forte terremoto su una comunità urbana può essere ridotto con una azione tempestiva e ben pianificata da parte degli organi di Protezione Civile preposti. In tale prospettiva è di fondamentale importanza che gli organi preposti alla sorveglianza sismica del territorio nazionale si dotino di strumenti di osservazione puntuale in grado di fornire nell’immediato terremoto informazioni utilissime per la gestione ottimale delle emergenze. Ciò può essere fatto oggi mediante la realizzazione reti sismiche urbane, con funzionamento in tempo reale (OSU Osservatori Sismici Urbani), in grado di fornire in maniera tempestiva ai centri per la gestione delle emergenze, la distribuzione dell'intensità dello scuotimento a seguito di un terremoto. Le mappe di scuotimento potrebbero consentire agli organi preposti, un’ottimale gestione delle priorità e la ripartizione delle risorse in modo da ottenere una significativa riduzione del numero di vittime a seguito di un forte terremoto. Le verifiche del grado di danneggiamento e di agibilità degli edifici potrebbero così essere effettuate secondo una logica prioritaria sulla base dei massimi scuotimenti misurati dalla rete sismica urbana. La realizzazione di OSU per le finalità su preposte è l’obiettivo principale del progetto MEMS finanziato dal MIUR nell’ambito del programma SIR (Scientific Independence of young Researchers). La prima sfida affrontata nell’ambito del progetto MEMS è stata la progettazione e sviluppo di una stazione accelerometrica idonea agli scopi del progetto. Il presente lavoro ha lo scopo di descrivere nel dettaglio, la stazione accelerometrica basata su tecnologia MEMS (Micro ElectroMechanical Systems), implementata nell’abito dell’omonimo progetto. Ciò che stato realizzato è una stazione accelerometrica dalle ottime performance, estremamente versatile e a bassissimo costo. The impact of a strong earthqauke onto an urban community can be reduced taking timely actions managed by the authority in charge for the civil protection. In this view, it is of fundamental importance that the authority in charge for the seismic surveillance is provided with the appropriate monitoring tools able to give the most useful information for the best emergency management in the immediate post-event. Today this goal can be reached realizing urban-scale, real-time seismic monitoring networks (Osservatori Sismici Urbani, OSU, in Italian). The real-time OSU networks are able to promptly provide information to the emergency authority about the ground shaking at each node after an earthquake. Maps showing the spatial distribution of the ground shaking could help in the optimization of the priorities and a good management of the rescue resources in order to reduce the number of victims. Also the assessment of the damage of the buildings could be carried out according to the logic of priority given by the shakings measured by the OSU. The realisation of OSUs is the main objective of the MEMS project. This project is funded by the MIUR under the SIR program (Scientific Independence of young Researchers). The first challenge of the MEMS project was the design and the development of a devoted accelerometric stations. This work illustrates in detail the accelerometric station based on MEMS technology (Micro Electro-Mechanical Systems). The final product is a low-cost accelerometric station with high performance and great versatility.

Published
2019

35. Association between changes in body composition and neoadjuvant treatment for pancreatic cancer

Author

Sandini, M, Patino, M, Ferrone, C, Alvarez-Perez, C, Honselmann, K, Paiella, S, Catania, M, Riva, L, Tedesco, G, Casolino, R, Auriemma, A, Salandini, M, Carrara, G, Cristel, G, Damascelli, A, Ippolito, D, D'Onofrio, M, Lillemoe, K, Bassi, C, Braga, M, Gianotti, L, Sahani, D, Fernandez-Del Castillo, C, Sandini M., Patino M., Ferrone C. R., Alvarez-Perez C. A., Honselmann K. C., Paiella S., Catania M., Riva L., Tedesco G., Casolino R., Auriemma A., Salandini M. C., Carrara G., Cristel G., Damascelli A., Ippolito D., D'Onofrio M., Lillemoe K. D., Bassi C., Braga M., Gianotti L., Sahani D., Fernandez-Del Castillo C., Sandini, M, Patino, M, Ferrone, C, Alvarez-Perez, C, Honselmann, K, Paiella, S, Catania, M, Riva, L, Tedesco, G, Casolino, R, Auriemma, A, Salandini, M, Carrara, G, Cristel, G, Damascelli, A, Ippolito, D, D'Onofrio, M, Lillemoe, K, Bassi, C, Braga, M, Gianotti, L, Sahani, D, Fernandez-Del Castillo, C, Sandini M., Patino M., Ferrone C. R., Alvarez-Perez C. A., Honselmann K. C., Paiella S., Catania M., Riva L., Tedesco G., Casolino R., Auriemma A., Salandini M. C., Carrara G., Cristel G., Damascelli A., Ippolito D., D'Onofrio M., Lillemoe K. D., Bassi C., Braga M., Gianotti L., Sahani D., and Fernandez-Del Castillo C.

Abstract

IMPORTANCE Sarcopenia and sarcopenic obesity have been associated with poor outcomes in unresectable pancreatic cancer (PC). Neoadjuvant treatment (NT) is used increasingly to improve resectability; however, its effects on fat and muscle body composition have not been characterized. OBJECTIVES To evaluate whether NT affects muscle mass and adipose tissue in patients with borderline resectable PC (BRPC) and locally advanced PC (LAPC) and determine whether there were potential differences between patients who ultimately underwent resection and those who did not. DESIGN, SETTING, AND PARTICIPANTS In this retrospective cohort study conducted at 4 academic medical centers, 193 patients with BRPC and LAPC undergoing surgical exploration after NT who had available computed tomographic scans (both at diagnosis and preoperatively) and confirmed pancreatic ductal adenocarcinoma were evaluated. The study was conducted from January 2013 to December 2015. Data analysis was performed from September 2016 to May 2017. Measurement of body compartments was evaluated with volume assessment software before and after NT. A radiologist blinded to the patient outcome assessed the areas of skeletal muscle, total adipose tissue, and visceral adipose tissue through a standardized protocol. EXPOSURES Receipt of NT. MAIN OUTCOMES AND MEASURES Achievement of pancreatic resection at surgical exploration after the receipt of NT. RESULTS Of the 193 patients with complete radiologic imaging available after NT, 96 (49.7%) were women; mean (SD) age at diagnosis was 64 (11) years. Most patients received combined therapy with fluorouracil, irinotecan, oxaliplatin, leucovorin, and folic acid (124 [64.2%]) and 86 (44.6%) received chemoradiotherapy as well. The median interval between pre-NT and post-NT imaging was 6 months (interquartile range [IQR], 4-7 months). All body compartments significantly changed. The adipose compound decreased (median total adipose tissue area from 284.0 cm2; IQR, 171.0-414.0

Published
2018

38. Diagnostic Accuracy of Prion Disease Biomarkers in Iatrogenic Creutzfeldt-Jakob Disease

Author

Llorens, F, Villar-Pique, A, Hermann, P, Schmitz, M, Calero, O, Stehmann, C, Sarros, S, Moda, F, Ferrer, I, Poleggi, A, Pocchiari, M, Catania, M, Klotz, S, O'Regan, C, Brett, F, Heffernan, J, Ladogana, A, Collins, SJ, Calero, M, Kovacs, GG, Zerr, I, Llorens, F, Villar-Pique, A, Hermann, P, Schmitz, M, Calero, O, Stehmann, C, Sarros, S, Moda, F, Ferrer, I, Poleggi, A, Pocchiari, M, Catania, M, Klotz, S, O'Regan, C, Brett, F, Heffernan, J, Ladogana, A, Collins, SJ, Calero, M, Kovacs, GG, and Zerr, I

Abstract

Human prion diseases are classified into sporadic, genetic, and acquired forms. Within this last group, iatrogenic Creutzfeldt-Jakob disease (iCJD) is caused by human-to-human transmission through surgical and medical procedures. After reaching an incidence peak in the 1990s, it is believed that the iCJD historical period is probably coming to an end, thanks to lessons learnt from past infection sources that promoted new prion prevention and decontamination protocols. At this point, we sought to characterise the biomarker profile of iCJD and compare it to that of sporadic CJD (sCJD) for determining the value of available diagnostic tools in promptly recognising iCJD cases. To that end, we collected 23 iCJD samples from seven national CJD surveillance centres and analysed the electroencephalogram and neuroimaging data together with a panel of seven CSF biomarkers: 14-3-3, total tau, phosphorylated/total tau ratio, alpha-synuclein, neurofilament light, YKL-40, and real-time quaking induced conversion of prion protein. Using the cut-off values established for sCJD, we found the sensitivities of these biomarkers for iCJD to be similar to those described for sCJD. Given the limited relevant information on this issue to date, the present study validates the use of current sCJD biomarkers for the diagnosis of future iCJD cases.

Published
2020

39. Discovering the Italian phenotype of cerebral amyloid angiopathy (CAA): the SENECA project

Author

Bersano, A, Scelzo, E, Pantoni, L, Morotti, A, Erbetta, A, Chiapparini, L, Vitali, P, Giaccone, G, Caroppo, P, Catania, M, Obici, L, Di Fede, G, Gatti, L, Tinelli, F, Di Francesco, J, Piazza, F, Ferrarese, C, Gasparini, M, Adobbati, L, Bianchi-Marzoli, S, Tremolada, G, Sacco, S, Mancuso, M, Zedde, M, Godani, M, Lanfranconi, S, Pareyson, D, Di Girolamo, M, Motto, C, Charidimou, A, Boulouis, G, Parati, E, Bersano, Anna, Scelzo, Emma, Pantoni, Leonardo, Morotti, Andrea, Erbetta, Alessandra, Chiapparini, Luisa, Vitali, Paolo, Giaccone, Giorgio, Caroppo, Paola, Catania, Marcella, Obici, Laura, Di Fede, Giuseppe, Gatti, Laura, Tinelli, Francesca, Di Francesco, Jacopo C., Piazza, Fabrizio, Ferrarese, Carlo, Gasparini, Massimo, Adobbati, Laura, Bianchi-Marzoli, Stefania, Tremolada, Gemma, Sacco, Simona, Mancuso, Michelangelo, Zedde, Maria Luisa, Godani, Massimiliano, Lanfranconi, Silvia, Pareyson, Davide, Di Girolamo, Marco, Motto, Cristina, Charidimou, Andreas, Boulouis, Gregoire, Parati, Eugenio A., Bersano, A, Scelzo, E, Pantoni, L, Morotti, A, Erbetta, A, Chiapparini, L, Vitali, P, Giaccone, G, Caroppo, P, Catania, M, Obici, L, Di Fede, G, Gatti, L, Tinelli, F, Di Francesco, J, Piazza, F, Ferrarese, C, Gasparini, M, Adobbati, L, Bianchi-Marzoli, S, Tremolada, G, Sacco, S, Mancuso, M, Zedde, M, Godani, M, Lanfranconi, S, Pareyson, D, Di Girolamo, M, Motto, C, Charidimou, A, Boulouis, G, Parati, E, Bersano, Anna, Scelzo, Emma, Pantoni, Leonardo, Morotti, Andrea, Erbetta, Alessandra, Chiapparini, Luisa, Vitali, Paolo, Giaccone, Giorgio, Caroppo, Paola, Catania, Marcella, Obici, Laura, Di Fede, Giuseppe, Gatti, Laura, Tinelli, Francesca, Di Francesco, Jacopo C., Piazza, Fabrizio, Ferrarese, Carlo, Gasparini, Massimo, Adobbati, Laura, Bianchi-Marzoli, Stefania, Tremolada, Gemma, Sacco, Simona, Mancuso, Michelangelo, Zedde, Maria Luisa, Godani, Massimiliano, Lanfranconi, Silvia, Pareyson, Davide, Di Girolamo, Marco, Motto, Cristina, Charidimou, Andreas, Boulouis, Gregoire, and Parati, Eugenio A.

Abstract

Cerebral amyloid angiopathy (CAA) is one of the major types of cerebral small vessel disease, and a leading cause of spontaneous intracerebral hemorrhage and cognitive decline in elderly patients. Although increasingly detected, a number of aspects including the pathophysiology, the clinical and neuroradiological phenotype, and the disease course are still under investigation. The incomplete knowledge of the disease limits the implementation of evidence-based guidelines on patient’s clinical management and the development of treatments able to prevent or reduce disease progression. The SENECA (SEarchiNg biomarkErs of Cerebral Angiopathy) project is the first Italian multicenter cohort study aimed at better defining the disease natural history and identifying clinical and neuroradiological markers of disease progression. By a multidisciplinary approach and the collection of a large and well-phenotyped series and biorepository of CAA patients, the study is ultimately expected to improve the diagnosis and the knowledge of CAA pathophysiological mechanisms.

Published
2020

40. Strategies for preventing group B streptococcal infections in newborns: A nation-wide survey of Italian policies

Author

Tzialla, C, berardi, A, farina, C, clerici, P, borghesi, A, viora, E, scollo, P, stronati, M, Task Force for group B streptococcal infections for the Italian Society of Neonatology including Stival, G, barbaglia, Ma, guala, A, giunta, E, parola, L, grossignani, Mr, perri, P, tubaldi, L, alletto, G, daidone, S, flacco, V, dani, C, sterpa, A, rapisardi, G, elicio, Mr, faldella, G, capretti, Mg, messner, H, bandiera, M, achille, C, azzali, A, montrasio, G, mariani, S, galvagno, G, giacosa, E, de Angelis, F, spandrio, M, serra, A, garofalo, F, perona, A, porcelli, F, ferrero, F, De Franco, S, paollilo, P, picone, S, besana, R, varisco, T, farina, M, memo, L, nicolini, G, lietti, D, Di Chiara, G, rottoli, A, Bonabitacola, T, Cortis, E, Neri, E, Martinelli, S, Ilardi, L, Rondanini, Gf, Calzi, P, Gatta, A, Quntadamo, Pa, Ivaldi, M, Terenzani, L, Di Lascio, N, Travaglio, Md, Vetrano, G, Furcolo, G, Vitacco, V, Intini, C, Frigerio, M, Stroppiana, P, Policicchio, G, Mesirca, P, Gianino, P, Audenio, E, Paludetto, R, Raimondi, F, Pugliese, A, Valentino, L, Nosari, N, Marchesano, G, Chirico, G, Bellù, R, Menchini, M, Poletti, A, E T, Vacchiano, Pinto, L, E D, Perri, Coppola, R, Perini, R, Vetrella, A, De Luca, G, Lista, G, Cavigioli, F, Bettinelli, A, Massironi, E, Franco, C, Bernardo, L, Poli, S, Palladini, M, Tota, V, Spadavecchia, F, Zuccotti, Gv, Pogliani, L, Bracaglia, G, Mancini, Al, Zocco, F, Iozzia, G, Auriemma, A, Teani, M, Mangilli, G, Tempra, Am, Di Terlizi, L, Bottino, R, Salvi, C, Fortunato, V, Musaico, R, Gargantini, G, Carrera, G, Magaldi, R, Taurino, L, D'Onofrio, Am, Buffone, E, Tempera, A, Agosti, M, Garzia, P, Mosca, F, Pugni, L, Tagliabue, P, Colombo, C, Demi, M, Picco, G, Carlucci, A, Zorzi, G, Padula, D, Cardone, Ml, Buonocore, G, Muraca, Mc, Boldrini, A, Ciantelli, M, Lanari, M, Serra, L, Felici, L, Banderalli, G, Brambilla, C, Dall'Agnola, A, Viviani, E, Zonca, Mc, Licardi, G, Chiara, A, Ancora, G, Papa, I, Gancia, P, Pomero, G, Deloglu, A, Villani, P, Mussini, P, Canidio, E, Migliavacca, D, Di Fabio, S, Cipollone, I, Biasucci, G, Rubbi, P, Piepoli, M, Guastaferro, N, Infriccioli, F, Bertino, E, Perathoner, C, Parmigiani, S, Suriano, G, Ianniello, C, Biasini, A, Azzalli, M, Timpani, G, Barresi, S, Caoci, G, Ciccotti, R, De Curtis, M, Natale, F, Finocchi, M, Haass, C, Milillo, F, Lucieri, S, Guercio, E, Canepa, Sa, Scozia, G, Antonucci, R, Limongelli, O, Macciò, S, Mongelli, F, Colonna, F, Dragovic, D, Calipa, Mt, Cohen, A, Moresco, L, Italian Society of Obstetricians and Gynecologists including La Spina, R, Ruggeri, R, Luehwink, A, Brattoli, M, Fedi, A, Lacchi, L, Ettore, G, Pappalardo, E, Conoscenti, G, Zeni, B, Spellecchia, D, Favretti, L, Spagna, L, Zaglio, S, Bresciani, D, Bandini, A, Mancini, R, Mustoni, P, Dodero, D, Grimaldi, M, Di Mario, M, Migliorini, P, Kemeny, A, Anastasio, Ps, Riccardi, T, Maggino, T, Cerri, G, Puggina, P, Marconi, Am, Morgia, S, Bellia, G, D'Anna, Mr, Catania, M, Bacchi Modena, A, Franchi, L, Calonaci, N, Schettini, S, Paradiso, R, Saccucci, P, Ioppi, M, Zorzi, M, Stellin, G, Patacchiola, F, Carrata, L, Bassini, D, San Marco, L, Todros, T, Tibadi, C, Liborio, M, Italian Association of Clinical Microbiologists including Laricchia, R, Tauro, L, Ferrara, F, Nuara, C, Ghiraldi, E, Molinari, F, Comessatti, A, Rocchetti, A, Di Matteo, L, Miconi, V, Calvi, P, Pernigotti, A, Fabozzi, F, Micca, G, Monticone, G, Sarti, M, Da Rin, G, Zoppelletto, M, Modolo, E, Landini, Mp, Furlini, G, Galluppi, E, Pagani, E, Aschbacher, R, Innocenti, P, Bresolin, N, Raggi, Me, Bonfanti, C, De Francesco, M, Santer, P, Griessmaier, A, De Francesco, D, Pirali, A, Prasciolu, C, Usai, F, Cuzzone, G, Scutellà, M, Tramacere, P, Fossati, D, Piaserico, G, Bordignon, G, Sciacca, A, Di Vincenzo, F, Imbriani, A, Melotti, D, Catanoso, G, Rivetti, I, Neri, G, Bruno, R, Bacelle, L, Sartore, P, Giana, G, Sala, E, Giraldi, C, Cavalcanti, P, Perugini, M, Perugini, A, Ginardi, C, Maritano, D, Ferrini, A, Bonettini, A, Avanzini, A, Gasperoni, S, Pieretti, B, Montanari, E, Carillo, C, Rossi, Mr, Laureti, A, Baldoni, Ml, Serra, D, Melioli, G, Bandettini, R, Oneto, F, Colla, R, Storchi Incerti, S, Lanzini, F, Pauri, P, Tili, E, Leone, Ra, Verdastro, G, Megha, M, Luzzaro, F, Conti, A, Busulini, L, Mirri, P, Diodati, R, Vettori, C, Pittalis, S, Anesi, A, Fiore, A, Goglia, L, Vitullo, E, Sinno, A, Platzgummer, S, Spitaler, C, Trabucchi, Mc, Besozzi, M, Cesana, E, Inghilleri, G, Grosso, S, D'Angelo, R, Fogato, E, Lavarda, F, Ortisi, G, Clementi, M, Cichero, P, Rumpianesi, F, Venturelli, C, Mortillaro, F, Daffara, S, Catania, Mr, Iula, D, Andreoni, S, Politi, A, Agostinelli, C, Paparella, C, Capozzi, D, Notaris, P, Bistoni, F, Mencacci, A, Valentini, M, Filippetti, A, Confalonieri, M, Novarese, O, Bonini, F, Salamone, D, Camporese, A, De Rosa, R, Casprini, P, Degl'Innocenti, R, Giordano, R, Allù, Mt, Zanella, D, Malandrino, M, Tronci, M, Valmarin, M, Leonetti, G, Falco, S, Meledandri, M, Ballardini, M, Spanò, A, Cava, Mc, Mascellino, Mt, Schinella, M, Gualdi, P, Casari, E, Scattolo, N, Motta, C, Perfetti, C, Bassano, M, Cera, G, Iafisco, P, Mura, I, Palmieri, A, Migliardi, M, Ferlini, M, Grandi, G, Giardini, F, Albano, F, Latino, M, Ferrero, Mp, Bellizia, L, Russolo, M, Russolo, S, Pesenti, A, Fasano, Ma, Previato, S, Radillo, O, Busetti, M, Ferrari, P, Siderini, V, Puzzolante, L, Scarparo, C, Arzese, A, Cappuccia, N, Lodolo, L, Delledonne, L, Gramoni, A, Maiolo, V, Gheller, A, Spadaro, S, Balzaretti, M, Tzialla, C., Berardi, A., Farina, C., Clerici, P., Borghesi, A., Viora, E., Scollo, P., Stronati, M., Stival, G., Barbaglia, M. A., Guala, A., Giunta, E., Parola, L., Grossignani, M. R., Perri, P., Tubaldi, L., Alletto, G., Daidone, S., Flacco, V., Dani, C., Sterpa, A., Rapisardi, G., Elicio, M. R., Faldella, G., Capretti, M. G., Messner, H., Bandiera, M., Achille, C., Azzali, A., Montrasio, G., Mariani, S., Galvagno, G., Giacosa, E., de Angelis, F., Spandrio, M., Serra, A., Garofalo, F., Perona, A., Porcelli, F., Ferrero, F., De Franco, S., Paollilo, P., Picone, S., Besana, R., Varisco, T., Farina, M., Memo, L., Nicolini, G., Lietti, D., Di Chiara, G., Rottoli, A., Bonabitacola, T., Cortis, E., Neri, E., Martinelli, S., Ilardi, L., Rondanini, G. F., Calzi, P., Gatta, A., Quntadamo, P. A., Ivaldi, M., Terenzani, L., Di Lascio, N., Travaglio, M. D., Vetrano, G., Furcolo, G., Vitacco, V., Intini, C., Frigerio, M., Stroppiana, P., Policicchio, G., Mesirca, P., Gianino, P., Audenio, E., Paludetto, R., Raimondi, F., Pugliese, A., Valentino, L., Nosari, N., Marchesano, G., Chirico, G., Bell(`u), R., Menchini, M., Poletti, A., Vacchiano, T., Pinto, L., Perri, D., Coppola, R., Perini, R., Vetrella, A., De Luca, G., Lista, G., Cavigioli, F., Bettinelli, A., Massironi, E., Franco, C., Bernardo, L., Poli, S., Palladini, M., Tota, V., Spadavecchia, F., Zuccotti, G. V., Pogliani, L., Bracaglia, G., Mancini, A. L., Zocco, F., Iozzia, G., Auriemma, A., Teani, M., Mangilli, G., Tempra, A. M., Di Terlizi, L., Bottino, R., Salvi, C., Fortunato, V., Musaico, R., Gargantini, G., Carrera, G., Magaldi, R., Taurino, L., D?onofrio, A. M., Buffone, E., Tempera, A., Agosti, M., Garzia, P., Mosca, F., Pugni, L., Tagliabue, P., Colombo, C., Demi, M., Picco, G., Carlucci, A., Zorzi, G., Padula, D., Cardone, M. L., Buonocore, G., Muraca, M. C., Boldrini, A., Ciantelli, M., Lanari, M., Serra, L., Felici, L., Banderalli, G., Brambilla, C., Dall?agnola, A., Viviani, E., Zonca, M. C., Licardi, G., Chiara, A., Ancora, G., Papa, I., Gancia, P., Pomero, G., Deloglu, A., Villani, P., Mussini, P., Canidio, E., Migliavacca, D., Di Fabio, S., Cipollone, I., Biasucci, G., Rubbi, P., Piepoli, M., Guastaferro, N., Infriccioli, F., Bertino, E., Perathoner, C., Parmigiani, S., Suriano, G., Ianniello, C., Biasini, A., Azzalli, M., Timpani, G., Barresi, S., Caoci, G., Ciccotti, R., De Curtis, M., Natale, F., Finocchi, M., Haass, C., Milillo, F., Lucieri, S., Guercio, E., Canepa, S. A., Scozia, G., Antonucci, R., Limongelli, O., Macci(`o), S., Mongelli, F., Colonna, F., Dragovic, D., Calipa, M. T., Cohen, A., Moresco, L., La Spina, R., Ruggeri, R., Luehwink, A., Brattoli, M., Fedi, A., Lacchi, L., Ettore, G., Pappalardo, E., Conoscenti, G., Zeni, B., Spellecchia, D., Favretti, L., Spagna, L., Zaglio, S., Bresciani, D., Bandini, A., Mancini, R., Mustoni, P., Dodero, D., Grimaldi, M., Di Mario, M., Migliorini, P., Kemeny, A., Anastasio, P. S., Riccardi, T., Maggino, T., Cerri, G., Puggina, P., Marconi, A. M., Morgia, S., Bellia, G., D?anna, M. R., Catania, M., Bacchi Modena, A., Franchi, L., Calonaci, N., Schettini, S., Paradiso, R., Saccucci, P., Ioppi, M., Zorzi, M., Stellin, G., Patacchiola, F., Carrata, L., Bassini, D., San Marco, L., Todros, T., Tibadi, C., Liborio, M., Laricchia, R., Tauro, L., Ferrara, F., Nuara, C., Ghiraldi, E., Molinari, F., Comessatti, A., Rocchetti, A., Di Matteo, L., Miconi, V., Calvi, P., Pernigotti, A., Fabozzi, F., Micca, G., Monticone, G., Sarti, M., Da Rin, G., Zoppelletto, M., Modolo, E., Landini, M. P., Furlini, G., Galluppi, E., Pagani, E., Aschbacher, R., Innocenti, P., Bresolin, N., Raggi, M. E., Bonfanti, C., De Francesco, M., Santer, P., Griessmaier, A., De Francesco, D., Pirali, A., Prasciolu, C., Usai, F., Cuzzone, G., Scutell(`a), M., Tramacere, P., Fossati, D., Piaserico, G., Bordignon, G., Sciacca, A., Di Vincenzo, F., Imbriani, A., Melotti, D., Catanoso, G., Rivetti, I., Neri, G., Bruno, R., Bacelle, L., Sartore, P., Giana, G., Sala, E., Giraldi, C., Cavalcanti, P., Perugini, M., Perugini, A., Ginardi, C., Maritano, D., Ferrini, A., Bonettini, A., Avanzini, A., Gasperoni, S., Pieretti, B., Montanari, E., Carillo, C., Rossi, M. R., Laureti, A., Baldoni, M. L., Serra, D., Melioli, G., Bandettini, R., Oneto, F., Colla, R., Storchi Incerti, S., Lanzini, F., Pauri, P., Tili, E., Leone, R. 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Subjects
Male, Pediatrics, Group B, 0302 clinical medicine, Neonate, Pregnancy, Surveys and Questionnaires, Prevalence, Mass Screening, Blood culture, 030212 general & internal medicine, Antibiotic prophylaxis, Survey, GBS, Group B streptococcus, Infection, Newborn infant, Adult, Antibiotic Prophylaxis, Female, Health Surveys, Humans, Infant, Newborn, Italy, Neonatal Screening, Pregnancy Complications, Infectious, Prenatal Care, Primary Prevention, Risk Assessment, Streptococcal Infections, Streptococcus agalactiae, reproductive and urinary physiology, Group B streptococcu, medicine.diagnostic_test, lcsh:RJ1-570, Infectious, Perinatology and Child Health, Pediatrics, Perinatology and Child Health, medicine.medical_specialty, Antibiotic sensitivity, Group B Streptococcal Infection, Prenatal care, 03 medical and health sciences, 030225 pediatrics, medicine, Intensive care medicine, Mass screening, business.industry, Public health, Infant, lcsh:Pediatrics, Newborn, Pregnancy Complications, business
Abstract

Background There are no Italian data regarding the strategies for preventing neonatal group B streptococcal (GBS) infection. We conducted a national survey in order to explore obstetrical, neonatal and microbiological practices for the GBS prevention. Methods Three distinct questionnaires were sent to obstetricians, neonatologists and microbiologists. Questionnaires included data on prenatal GBS screening, maternal risk factors, intrapartum antibiotic prophylaxis, microbiological information concerning specimen processing and GBS antimicrobial susceptibility. Results All respondent obstetrical units used the culture-based screening approach to identify women who should receive intrapartum antibiotic prophylaxis, and more than half of the microbiological laboratories (58%) reported using specimen processing consistent with CDC guidelines. Most neonatal units (89 out of 107, 82%) reported using protocols for preventing GBS early-onset sepsis consistent with CDC guidelines. Conclusions The screening-based strategy is largely prevalent in Italy, and most protocols for preventing GBS early-onset sepsis are consistent with CDC guidelines. However, we found discrepancies in practices among centers that may reflect the lack of Italian guidelines issued by public health organizations.

Published
2017

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