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2. The weekend effect on the provision of Emergency Surgery before and during the COVID-19 pandemic: case-control analysis of a retrospective multicentre database

Author

Tebala G. D., Milani M. S., Cirocchi R., Bignell M., Bond-Smith G., Lewis C., Agnoletti V., Catarci M., Di Saverio S., Luridiana G., Catena F., Scatizzi M., Marini P., Lo Dico R., Stracqualursi A., Russo G., D'Errico S., Cianci P., Restini E., Scialandrone G., Guercioni G., Martinez G., Pezzolla A., Altomare D. F., Picciariello A., Trigiante G., Dibra R., Papagni V., Righetti C., Polastri R., Andreuccetti J., Pignata G., D'Alessio R., Arici E., Canfora I., Cillara N., Deserra A., Sechi R., Bianco F., Gili S., Cappiello A., Incollingo P., Biloslavo A., Bellio G., Germani P., De Manzini N., Buiatti M., Paladino F. P., Sasia D., Borghi F., Testa V., Giraudo G., Allisiardi F., Giuffrida M. C., Gerosa M., Fogliati A., Maggioni D., Fabbri N., Feo C. V., Bianchini E., Panzini I., Lizzi V., Tricarico F. G., Di Gioia G., Melino R., Tartaglia N., Ambrosi A., Pavone G., Pacilli M., Vovola F., Belli F., Barberis A., Azzinnaro A., Coratti A., Benigni R., Berti S., Saracco M., Gennai A., Dova L., Farfaglia R., Pata G., Arizzi V., Pandolfo G., Frontali A., Danelli P., Ferrario L., Guerci C., Mariani N. M., Pisani Ceretti A., Nicastro V., Opocher E., Gozzo D., Casoni Pattacini G., Castriconi M., Amendola A., Gaudiello M., Palomba G., Petracca G. L., Perrone G., Giuffrida M., Moretto G., Impellizzeri H., Casaril A., Filosa M., Caizzone A., Agrusti S., Cattaneo G. M., Capelli P., Muratore A., Calabro M., Pipitore Federico N., Cuzzola B., Danna R., Murgese A., Coccolini F., Pieroni E., Chiarugi M., Tartaglia D., Giannessi S., Somigli R., Trafeli M., Fedi M., De Vincenti R., Guariniello A., Grande M., Bagaglini G., Pirozzi B., Guida A. M., Ingallinella S., Don C. P., Siragusa L., Capone O., Cerbo D., Santoro E., Pende V., Fassari A., Mingoli A., Brachini G., Cirillo B., Zambon M., Cicerchia P., Meneghini S., Sapienza P., Puzzovio A., La Torre F., Fransvea P., Di Grezia M., Sganga G., Armellino M. F., Ioia G., Rampone B., Della Corte M., Fleres F., Clarizia G., Bordoni P., Spolini A., Franzini M., Grechi A., Suppo M., Bono D., Scaglione D., Cotsoglou C., Paleini S., Chierici A. P., Uccelli M., Olmi S., Cesana G., Tenreiro N., Marcal A., Martins D., Leal C., Vieira B., Ugarte-Sierra B., Vincene-Rodriguez I., Duran-Ballesteros M., Sanz-Larrainzar A., Ibanez-Aguirre F. J., Yanez-Benites C., Talal I., Blas J. L., Garau R., Clark-Stuart S., Wallace A., Di Carlo A., Wisnia E., Ehsan K., Beck-Sanders K., Godson E., Campbell P., Ahmad R., Ali R., Aswani S. S., Barza A., Carrillo C., Dawani A., Dey A., Elserafy A., Gaspar D., Lazzareschi L., Patel M., Shabana A., Shams M., Shams O., Slack Z., Tebala, Giovanni D, Milani, Marika S, Cirocchi, Roberto, Bignell, Mark, Bond-Smith, Gile, Lewis, Christopher, Agnoletti, Vanni, Catarci, Marco, Di Saverio, Salomone, Luridiana, Gianluigi, Catena, Fausto, Scatizzi, Marco, Marini, Pierluigi, de Manzini, Nicolo', Tebala G.D., Milani M.S., Cirocchi R., Bignell M., Bond-Smith G., Lewis C., Agnoletti V., Catarci M., Di Saverio S., Luridiana G., Catena F., Scatizzi M., Marini P., Lo Dico R., Stracqualursi A., Russo G., D'Errico S., Cianci P., Restini E., Scialandrone G., Guercioni G., Martinez G., Pezzolla A., Altomare D.F., Picciariello A., Trigiante G., Dibra R., Papagni V., Righetti C., Polastri R., Andreuccetti J., Pignata G., D'Alessio R., Arici E., Canfora I., Cillara N., Deserra A., Sechi R., Bianco F., Gili S., Cappiello A., Incollingo P., Biloslavo A., Bellio G., Germani P., De Manzini N., Buiatti M., Paladino F.P., Sasia D., Borghi F., Testa V., Giraudo G., Allisiardi F., Giuffrida M.C., Gerosa M., Fogliati A., Maggioni D., Fabbri N., Feo C.V., Bianchini E., Panzini I., Lizzi V., Tricarico F.G., Di Gioia G., Melino R., Tartaglia N., Ambrosi A., Pavone G., Pacilli M., Vovola F., Belli F., Barberis A., Azzinnaro A., Coratti A., Benigni R., Berti S., Saracco M., Gennai A., Dova L., Farfaglia R., Pata G., Arizzi V., Pandolfo G., Frontali A., Danelli P., Ferrario L., Guerci C., Mariani N.M., Pisani Ceretti A., Nicastro V., Opocher E., Gozzo D., Casoni Pattacini G., Castriconi M., Amendola A., Gaudiello M., Palomba G., Petracca G.L., Perrone G., Giuffrida M., Moretto G., Impellizzeri H., Casaril A., Filosa M., Caizzone A., Agrusti S., Cattaneo G.M., Capelli P., Muratore A., Calabro M., Pipitore Federico N., Cuzzola B., Danna R., Murgese A., Coccolini F., Pieroni E., Chiarugi M., Tartaglia D., Giannessi S., Somigli R., Trafeli M., Fedi M., De Vincenti R., Guariniello A., Grande M., Bagaglini G., Pirozzi B., Guida A.M., Ingallinella S., Don C.P., Siragusa L., Capone O., Cerbo D., Santoro E., Pende V., Fassari A., Mingoli A., Brachini G., Cirillo B., Zambon M., Cicerchia P., Meneghini S., Sapienza P., Puzzovio A., La Torre F., Fransvea P., Di Grezia M., Sganga G., Armellino M.F., Ioia G., Rampone B., Della Corte M., Fleres F., Clarizia G., Bordoni P., Spolini A., Franzini M., Grechi A., Suppo M., Bono D., Scaglione D., Cotsoglou C., Paleini S., Chierici A.P., Uccelli M., Olmi S., Cesana G., Tenreiro N., Marcal A., Martins D., Leal C., Vieira B., Ugarte-Sierra B., Vincene-Rodriguez I., Duran-Ballesteros M., Sanz-Larrainzar A., Ibanez-Aguirre F.J., Yanez-Benites C., Talal I., Blas J.L., Garau R., Clark-Stuart S., Wallace A., Di Carlo A., Wisnia E., Ehsan K., Beck-Sanders K., Godson E., Campbell P., Ahmad R., Ali R., Aswani S.S., Barza A., Carrillo C., Dawani A., Dey A., Elserafy A., Gaspar D., Lazzareschi L., Patel M., Shabana A., Shams M., Shams O., and Slack Z.

Subjects
Hot gallbladder, education, Weekend effect, COVID-19, Settore MED/18 - Chirurgia Generale, Retrospective Studie, Case-Control Studies, Emergency surgery, Hospital Mortality, Humans, Retrospective Studies, Pandemics, Emergency Medicine, Surgery, Case-Control Studie, Human
Abstract

Introduction The concept of “weekend effect”, that is, substandard healthcare during weekends, has never been fully demonstrated, and the different outcomes of emergency surgical patients admitted during weekends may be due to different conditions at admission and/or different therapeutic approaches. Aim of this international audit was to identify any change of pattern of emergency surgical admissions and treatments during weekends. Furthermore, we aimed at investigating the impact of the COVID-19 pandemic on the alleged “weekend effect”. Methods The database of the CovidICE-International Study was interrogated, and 6263 patients were selected for analysis. Non-trauma, 18+ yo patients admitted to 45 emergency surgery units in Europe in the months of March–April 2019 and March–April 2020 were included. Demographic and clinical data were anonymised by the referring centre and centrally collected and analysed with a statistical package. This study was endorsed by the Association of Italian Hospital Surgeons (ACOI) and the World Society of Emergency Surgery (WSES). Results Three-quarters of patients have been admitted during workdays and only 25.7% during weekends. There was no difference in the distribution of gender, age, ASA class and diagnosis during weekends with respect to workdays. The first wave of the COVID pandemic caused a one-third reduction of emergency surgical admission both during workdays and weekends but did not change the relation between workdays and weekends. The treatment was more often surgical for patients admitted during weekends, with no difference between 2019 and 2020, and procedures were more often performed by open surgery. However, patients admitted during weekends had a threefold increased risk of laparoscopy-to-laparotomy conversion (1% vs. 3.4%). Hospital stay was longer in patients admitted during weekends, but those patients had a lower risk of readmission. There was no difference of the rate of rescue surgery between weekends and workdays. Subgroup analysis revealed that interventional procedures for hot gallbladder were less frequently performed on patients admitted during weekends. Conclusions Our analysis revealed that demographic and clinical profiles of patients admitted during weekends do not differ significantly from workdays, but the therapeutic strategy may be different probably due to lack of availability of services and skillsets during weekends. The first wave of the COVID-19 pandemic did not impact on this difference.

Published
2022

4. Minore (Sottrazione internazionale)

Author

Almureden, E, Amagliani, R, Astone, F, Auletta, T, Azzarri, F, Balestra, L, Baratta, R, Bargelli, E, Bariatti, S, Bartoli, R, Battelli, E, Benigni, R, Bertolino, M, Bianca, M, Bugetti, M, Calogero, M, Campiglio, C, Carratta, A, Cipriani, N, Damascelli, D, del Tufo, M, Di Blase, A, Dilandro, A, Dimartino, A, Dirosa, G, Favilli, C, Garlati, L, Gigliotti, F, Gorgoni, M, Iannicelli, A, Lenzi, R, Lipari, N, Marella, MR, Martino, M, Merli, A, Morozzo, P, Paladini, M, Palazzo, M, Paradiso, M, Patti, S, Poliseno, B, Renda, A, Rimini, C, Salanitro, U, Sesta, M, Spadafora, A, Spena, A, Tuo, C, Viarengo, I, Macario, F, Honorati, C, Almureden, E, Amagliani, R, Astone, F, Auletta, T, Azzarri, F, Balestra, L, Baratta, R, Bargelli, E, Bariatti, S, Bartoli, R, Battelli, E, Benigni, R, Bertolino, M, Bianca, M, Bugetti, M, Calogero, M, Campiglio, C, Carratta, A, Cipriani, N, Damascelli, D, del Tufo, M, Di Blase, A, Dilandro, A, Dimartino, A, Dirosa, G, Favilli, C, Garlati, L, Gigliotti, F, Gorgoni, M, Iannicelli, A, Lenzi, R, Lipari, N, Marella, MR, Martino, M, Merli, A, Morozzo, P, Paladini, M, Palazzo, M, Paradiso, M, Patti, S, Poliseno, B, Renda, A, Rimini, C, Salanitro, U, Sesta, M, Spadafora, A, Spena, A, Tuo, C, Viarengo, I, Macario, F, and Honorati, C

Published
2022

10. Data Quality Assessment for In Silico Methods: A Survey of Approaches and Needs

Author

Nendza, M., primary, Aldenberg, T., additional, Benfenati, E., additional, Benigni, R., additional, Cronin, M.T.D., additional, Escher, S., additional, Fernandez, A., additional, Gabbert, S., additional, Giralt, F., additional, Hewitt, M., additional, Hrovat, M., additional, Jeram, S., additional, Kroese, D., additional, Madden, J. C., additional, Mangelsdorf, I., additional, Rallo, R., additional, Roncaglioni, A., additional, Rorije, E., additional, Segner, H., additional, Simon-Hettich, B., additional, and Vermeire, T., additional

Published
2010
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16. Risk assessment of N‐nitrosamines in food

Author

EFSA Panel on Contaminants in the Food Chain (EFSA CONTAM Panel), Dieter Schrenk, Margherita Bignami, Laurent Bodin, James Kevin Chipman, Jesús delMazo, Christer Hogstrand, Laurentius (Ron) Hoogenboom, Jean‐Charles Leblanc, Carlo Stefano Nebbia, Elsa Nielsen, Evangelia Ntzani, Annette Petersen, Salomon Sand, Tanja Schwerdtle, Christiane Vleminckx, Heather Wallace, Benigni Romualdo, Cristina Fortes, Stephen Hecht, Marco Iammarino, Olaf Mosbach‐Schulz, Francesca Riolo, Anna Christodoulidou, and Bettina Grasl‐Kraupp

Subjects
N‐nitrosamines (N‐NAs), cancer, genotoxicity, occurrence, exposure, food, Nutrition. Foods and food supply, TX341-641, Chemical technology, TP1-1185
Abstract

Abstract EFSA was asked for a scientific opinion on the risks to public health related to the presence of N‐nitrosamines (N‐NAs) in food. The risk assessment was confined to those 10 carcinogenic N‐NAs occurring in food (TCNAs), i.e. NDMA, NMEA, NDEA, NDPA, NDBA, NMA, NSAR, NMOR, NPIP and NPYR. N‐NAs are genotoxic and induce liver tumours in rodents. The in vivo data available to derive potency factors are limited, and therefore, equal potency of TCNAs was assumed. The lower confidence limit of the benchmark dose at 10% (BMDL10) was 10 μg/kg body weight (bw) per day, derived from the incidence of rat liver tumours (benign and malignant) induced by NDEA and used in a margin of exposure (MOE) approach. Analytical results on the occurrence of N‐NAs were extracted from the EFSA occurrence database (n = 2,817) and the literature (n = 4,003). Occurrence data were available for five food categories across TCNAs. Dietary exposure was assessed for two scenarios, excluding (scenario 1) and including (scenario 2) cooked unprocessed meat and fish. TCNAs exposure ranged from 0 to 208.9 ng/kg bw per day across surveys, age groups and scenarios. ‘Meat and meat products’ is the main food category contributing to TCNA exposure. MOEs ranged from 3,337 to 48 at the P95 exposure excluding some infant surveys with P95 exposure equal to zero. Two major uncertainties were (i) the high number of left censored data and (ii) the lack of data on important food categories. The CONTAM Panel concluded that the MOE for TCNAs at the P95 exposure is highly likely (98–100% certain) to be less than 10,000 for all age groups, which raises a health concern.

Published
2023
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17. Chemical safety assessment using read-across: assessing the use of novel testing methods to strengthen the evidence base for decision making

Author

Berggren, E., Amcoff, P., Benigni, R., Blackburn, K., Carney, E., Cronin, M., Deluyker, H., Gautier, F., Judson, R.S., Kass, G.E.N., Keller, D., Knight, D., Lilienblum, W., Mahony, C., Rusyn, I., Schultz, T., Schwarz, M., Schüürmann, Gerrit, White, A., Burton, J., Lostia, A.M., Munn, S., Worth, A., Berggren, E., Amcoff, P., Benigni, R., Blackburn, K., Carney, E., Cronin, M., Deluyker, H., Gautier, F., Judson, R.S., Kass, G.E.N., Keller, D., Knight, D., Lilienblum, W., Mahony, C., Rusyn, I., Schultz, T., Schwarz, M., Schüürmann, Gerrit, White, A., Burton, J., Lostia, A.M., Munn, S., and Worth, A.

Abstract

Background: Safety assessment for repeated dose toxicity is one of the largest challenges in the process to replace animal testing. This is also one of the proof of concept ambitions of SEURAT-1, the largest ever European Union research initiative on alternative testing, co-funded by the European Commission and Cosmetics Europe. This review is based on the discussion and outcome of a workshop organized on initiative of the SEURAT-1 consortium joined by a group of international experts with complementary knowledge to further develop traditional read-across and include new approach data.Objectives: The aim of the suggested strategy for chemical read-across is to show how a traditional read-across based on structural similarities between source and target substance can be strengthened with additional evidence from new approach data—for example, information from in vitro molecular screening, “-omics” assays and computational models—to reach regulatory acceptance. Methods: We identified four read-across scenarios that cover typical human health assessment situations. For each such decision context, we suggested several chemical groups as examples to prove when read-across between group members is possible, considering both chemical and biological similarities.Conclusions: We agreed to carry out the complete read-across exercise for at least one chemical category per read-across scenario in the context of SEURAT-1, and the results of this exercise will be completed and presented by the end of the research initiative in December 2015.

Published
2015

21. QSAR Modeling: Where Have You Been? Where Are You Going To?

Author

Cherkasov, A, Muratov, E, Fourches, D, Varnek, A, Baskin, I, Cronin, M, Dearden, J, Gramatica, P, Martin, Y, Todeschini, R, Consonni, V, Kuz’Min, V, Cramer, R, Benigni, R, Yang, C, Rathman, J, Terfloth, L, Gasteiger, J, Richard, A, Tropsha, A, Muratov, EN, Baskin, II, Martin, YC, Kuz’min, VE, Tropsha, A., TODESCHINI, ROBERTO, CONSONNI, VIVIANA, Cherkasov, A, Muratov, E, Fourches, D, Varnek, A, Baskin, I, Cronin, M, Dearden, J, Gramatica, P, Martin, Y, Todeschini, R, Consonni, V, Kuz’Min, V, Cramer, R, Benigni, R, Yang, C, Rathman, J, Terfloth, L, Gasteiger, J, Richard, A, Tropsha, A, Muratov, EN, Baskin, II, Martin, YC, Kuz’min, VE, Tropsha, A., TODESCHINI, ROBERTO, and CONSONNI, VIVIANA

Abstract

Quantitative structure−activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Published
2014

23. AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

Author

Richardt Am and Benigni R

Subjects
Computer science, Endpoint Determination, General problem, Bioengineering, Context (language use), Machine learning, computer.software_genre, Rodent carcinogenicity, Xenobiotics, Mice, Structure-Activity Relationship, Neoplasms, Drug Discovery, Animals, Mechanism (biology), business.industry, General Medicine, Status report, Rats, Cell Transformation, Neoplastic, Models, Chemical, Molecular Medicine, Artificial intelligence, Data mining, business, computer, Forecasting
Abstract

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

Published
2002

24. A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH

Author

Nendza, M., Gabbert, S., Kühne, Ralph, Lombardo, A., Roncaglioni, A., Benfenati, E., Benigni, R., Bossa, C., Strempel, S., Scheringer, M., Fernández, A., Rallo, R., Giralt, F., Dimitrov, S., Mekenyan, O., Bringezu, F., Schüürmann, Gerrit, Nendza, M., Gabbert, S., Kühne, Ralph, Lombardo, A., Roncaglioni, A., Benfenati, E., Benigni, R., Bossa, C., Strempel, S., Scheringer, M., Fernández, A., Rallo, R., Giralt, F., Dimitrov, S., Mekenyan, O., Bringezu, F., and Schüürmann, Gerrit

Abstract

This paper presents an inventory of in silico screening tools to identify substance properties of concern under the European chemicals’ legislation REACH. The objective is to support the selection and implementation of appropriate tools as building blocks within integrated testing strategies (ITS). The relevant concerns addressed are persistence, bioaccumulation potential, acute and long-term aquatic toxicity, PBT/vPvB properties ((very) persistent, (very) bioaccumulative, toxic), CMR (carcinogenicity, mutagenicity, reproductive toxicity), endocrine disruption and skin sensitisation. The inventory offers a comparative evaluation of methods with respect to the underlying algorithms (how does the method work?) and the applicability domains (when does the method work?) as well as their limitations (when does the method not work?). The inventory explicitly addresses the reliability of predictions of different in silico models for diverse chemicals by applicability domain considerations. The confidence in predictions can be greatly improved by consensus modelling that allows for taking conflicting results into account. The inventory is complemented by a brief discussion of socio-economic tools for assessing the potential efficiency gains of using in silico methods compared to traditional in vivo testing of chemical hazards.

Published
2013

33. Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives

Author

BENFENATI, E., primary, BENIGNI, R., additional, DEMARINI, D. M., additional, HELMA, C., additional, KIRKLAND, D., additional, MARTIN, T. M., additional, MAZZATORTA, P., additional, OUÉDRAOGO-ARRAS, G., additional, RICHARD, A. M., additional, SCHILTER, B., additional, SCHOONEN, W. G. E. J., additional, SNYDER, R. D., additional, and YANG, C., additional

Published
2009
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35. Understanding Genetic Toxicity Through Data Mining: The Process of Building Knowledge by Integrating Multiple Genetic Toxicity Databases

Author

Yang, C., primary, Hasselgren, C. H., additional, Boyer, S., additional, Arvidson, K., additional, Aveston, S., additional, Dierkes, P., additional, Benigni, R., additional, Benz, R. D., additional, Contrera, J., additional, Kruhlak, N. L., additional, Matthews, E. J., additional, Han, X., additional, Jaworska, J., additional, Kemper, R. A., additional, Rathman, J. F., additional, and Richard, A. M., additional

Published
2008
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42. The detection and evaluation of aneugenic chemicals

Author

Parry, James M, primary, Parry, E.M, additional, Boumer, R, additional, Doherty, A, additional, Ellard, S, additional, O'Donovan, J, additional, Hoebee, B, additional, de Stoppelaar, J.M, additional, Mohn, G.R, additional, Önfelt, A, additional, Renglin, A, additional, Schultz, N, additional, Soderpalm-Bemdes, C, additional, Jensen, K.G, additional, Kirsch-Volders, M, additional, Elhajouji, A, additional, Van Hummelen, P, additional, Degrassi, F, additional, Antoccia, A, additional, Cimini, D, additional, Izzo, M, additional, Tanzarella, C, additional, Adler, I.-D, additional, Kliesch, U, additional, Schriever-Schwemmer, G, additional, Gasser, P, additional, Crebelli, R, additional, Carere, A, additional, Andreoli, C, additional, Benigni, R, additional, Leopardi, P, additional, Marcon, F, additional, Zinjo, Z, additional, Natarajan, A.T, additional, Boei, J.J.W.A, additional, Kappas, A, additional, Voutsinas, G, additional, Zarani, F.E, additional, Patrinelli, A, additional, Pachierotti, F, additional, Tiveron, C, additional, and Hess, P, additional

Published
1996
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