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Lai A, Bergna A, Toppo S, Morganti M, Menzo S, Ghisetti V, Bruzzone B, Codeluppi M, Fiore V, Rullo EV, Antonelli G, Sarmati L, Brindicci G, Callegaro A, Sagnelli C, Francisci D, Vicenti I, Miola A, Tonon G, Cirillo D, Menozzi I, Caucci S, Cerutti F, Orsi A, Schiavo R, Babudieri S, Nunnari G, Mastroianni CM, Andreoni M, Monno L, Guarneri D, Coppola N, Crisanti A, Galli M, Zehender G. Phylogeography and genomic epidemiology of SARS-CoV-2 in Italy and Europe with newly characterized Italian genomes between February-June 2020. Sci Rep 2022; 12:5736. [PMID: 35388091 PMCID: PMC8986836 DOI: 10.1038/s41598-022-09738-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 03/25/2022] [Indexed: 12/29/2022] Open
Abstract
The aims of this study were to characterize new SARS-CoV-2 genomes sampled all over Italy and to reconstruct the origin and the evolutionary dynamics in Italy and Europe between February and June 2020. The cluster analysis showed only small clusters including < 80 Italian isolates, while most of the Italian strains were intermixed in the whole tree. Pure Italian clusters were observed mainly after the lockdown and distancing measures were adopted. Lineage B and B.1 spread between late January and early February 2020, from China to Veneto and Lombardy, respectively. Lineage B.1.1 (20B) most probably evolved within Italy and spread from central to south Italian regions, and to European countries. The lineage B.1.1.1 (20D) developed most probably in other European countries entering Italy only in the second half of March and remained localized in Piedmont until June 2020. In conclusion, within the limitations of phylogeographical reconstruction, the estimated ancestral scenario suggests an important role of China and Italy in the widespread diffusion of the D614G variant in Europe in the early phase of the pandemic and more dispersed exchanges involving several European countries from the second half of March 2020.
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Affiliation(s)
- Alessia Lai
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy.,Pediatric Clinical Research Center Fondazione Romeo ed Enrica Invernizzi, University of Milan, Milan, Italy
| | - Annalisa Bergna
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
| | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padua, Italy.,CRIBI Biotech Center, University of Padova, Padua, Italy
| | - Marina Morganti
- Risk Analyses and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Parma, Italy
| | - Stefano Menzo
- Department of Biomedical Sciences and Public Health, Virology Unit, Polytechnic University of Marche, Ancona, Italy
| | - Valeria Ghisetti
- Laboratory of Microbiology and Virology, Amedeo di Savoia, ASL Città di Torino, Torino, Italy
| | | | - Mauro Codeluppi
- UOC of Infectious Diseases, Department of Oncology and Hematology, Guglielmo da Saliceto Hospital, AUSL Piacenza, Piacenza, Italy
| | - Vito Fiore
- Infectious and Tropical Disease Clinic, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Emmanuele Venanzi Rullo
- Unit of Infectious Diseases, Department of Experimental and Clinical Medicine, University of Messina, Messina, Italy
| | - Guido Antonelli
- Department of Molecular Medicine, University Hospital Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | | | - Annapaola Callegaro
- Microbiology and Virology Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Caterina Sagnelli
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Daniela Francisci
- Department of Medicine and Surgery, Clinic of Infectious Diseases, "Santa Maria della Misericordia" Hospital, University of Perugia, Perugia, Italy
| | - Ilaria Vicenti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Arianna Miola
- Intesa San Paolo Innovation Center-AI LAB, Turin, Italy
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS Ospedale San Raffaele, Milan, Italy.,Division of Experimental Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Daniela Cirillo
- Division of Immunology, Transplantation and Infectious Disease, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ilaria Menozzi
- Risk Analyses and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Parma, Italy
| | - Sara Caucci
- Department of Biomedical Sciences and Public Health, Virology Unit, Polytechnic University of Marche, Ancona, Italy
| | - Francesco Cerutti
- Laboratory of Microbiology and Virology, Amedeo di Savoia, ASL Città di Torino, Torino, Italy
| | - Andrea Orsi
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Roberta Schiavo
- UOC of Microbiology, Department of Clinical Pathology, Guglielmo da Saliceto Hospital, AUSL Piacenza, Piacenza, Italy
| | - Sergio Babudieri
- Infectious and Tropical Disease Clinic, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Giuseppe Nunnari
- Unit of Infectious Diseases, Department of Experimental and Clinical Medicine, University of Messina, Messina, Italy
| | - Claudio M Mastroianni
- Department of Public Health and Infectious Diseases, University Hospital Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | - Laura Monno
- Infectious Diseases Unit, University of Bari, Bari, Italy
| | - Davide Guarneri
- Microbiology and Virology Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Nicola Coppola
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Andrea Crisanti
- Microbiology and Virology Diagnostic Unit, Padua University Hospital, Padua, Italy.,Department of Life Science, Imperial College London, South Kensington Campus Imperial College Road, London, SW7 AZ, UK
| | - Massimo Galli
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
| | - Gianguglielmo Zehender
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy. .,Pediatric Clinical Research Center Fondazione Romeo ed Enrica Invernizzi, University of Milan, Milan, Italy. .,CRC-Coordinated Research Center "EpiSoMI", University of Milan, Milan, Italy.
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Berloco C, De Francisci Morales G, Frassineti D, Greco G, Kumarasinghe H, Lamieri M, Massaro E, Miola A, Yang S. Predicting corporate credit risk: Network contagion via trade credit. PLoS One 2021; 16:e0250115. [PMID: 33914764 PMCID: PMC8084139 DOI: 10.1371/journal.pone.0250115] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/30/2021] [Indexed: 11/18/2022] Open
Abstract
Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.
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Affiliation(s)
- Claudia Berloco
- Intesa Sanpaolo, Torino, Italy
- Università degli Studi di Torino, Torino, Italy
- * E-mail: (CB); (GDFM); (ML)
| | | | | | | | | | - Marco Lamieri
- Intesa Sanpaolo, Torino, Italy
- * E-mail: (CB); (GDFM); (ML)
| | | | | | - Shuyi Yang
- Intesa Sanpaolo, Torino, Italy
- Università degli Studi di Torino, Torino, Italy
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Abstract
We evaluated growth hormone binding protein (GHBP) activity in a group of obese children (12 boys and 12 girls, age 3.1-14.7 years, BMI 21.1-33.3, 11 prepubertal and 13 early pubertal) and in 26 age-matched normal weight children (14 boys and 12 girls, age 2.1-16.0 years, BMI 14.2-21.4, 18 prepubertal and 8 early pubertal). All children were of normal stature. GHBP activity was significantly higher in the obese (39.1 +/- 1.1%) than in the control children (28.3 +/- 1.0%, p < 0.0001). Mean serum GHBP was not different between boys and girls or between prepubertal and pubertal subjects. A positive correlation was found between BMI and GHBP levels only in the normal weight children (r = 0.425, p < 0.05). Baseline insulin concentrations in the obese children were 97.6 +/- 7.9 pmol/l (normal values, 45.0 +/- 18.6 pmol/l), and the mean insulin AUC following OGTT in the obese was 811.3 +/- 160.7 pmol/l (normal values, 373.1 +/- 150.1 pmol/l). Serum GHBP activity in the obese was not correlated with baseline serum insulin concentrations or with the insulin AUC following OGTT. In conclusion, we found that obese children have elevated GHBP activity, and speculate that this phenomenon may serve to compensate for their reduced GH secretion and accelerated GH clearance.
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Affiliation(s)
- S Seminara
- Dipartimento di Pediatria, Azienda Ospedale/Università Meyer, Firenze, Milano, Italy
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