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Editor-in-chief
Maria Stella Graziani

Deputy Director
Martina Zaninotto

Associate Editors
Ferruccio Ceriotti
Davide Giavarina
Bruna Lo Sasso
Giampaolo Merlini
Martina Montagnana
Andrea Mosca
Paola Pezzati
Rossella Tomaiuolo
Matteo Vidali

EIC Assistant
Francesco Busardò

International Advisory Board Khosrow Adeli Canada
Sergio Bernardini Italy
Marcello Ciaccio Italy
Eleftherios Diamandis Canada
Philippe Gillery France
Kjell Grankvist Sweden
Hans Jacobs The Netherlands
Eric Kilpatrick UK
Magdalena Krintus Poland
Giuseppe Lippi Italy
Mario Plebani Italy
Sverre Sandberg Norway
Ana-Maria Simundic Croatia
Tommaso Trenti Italy
Cas Weykamp The Netherlands
Maria Willrich USA
Paul Yip Canada


Publisher
Biomedia srl
Via L. Temolo 4, 20126 Milano

Responsible Editor
Giuseppe Agosta

Editorial Secretary
Chiara Riva
Biomedia srl
Via L. Temolo 4, 20126 Milano
Tel. 0245498282
email: biochimica.clinica@sibioc.it

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ISSN print: 0393 – 0564
ISSN digital: 0392- 7091



BC: Articoli scritti da T. Fasano

Big Data e Intelligenza Artificiale in Medicina di Laboratorio
Artificial intelligence and big data in laboratory medicine
<p><span style="color:rgb(33, 29, 30); font-family:calibri,sans-serif; font-size:9pt">In the last few years, artificial intelligence (AI) is gaining attention in several medical disciplines, including laboratory medicine (LM). The raised interest on AI has been fueled not only by the huge amounts of information daily generated, but also by the special natural context offered by laboratories, where digitalization have already occupied an important part of the routine workflow of patients&rsquo; data. Motivated by these topics and under the auspices of SIBioC, a conference on AI and big data was organized in May 2022 in Bologna, Italy. This conference covered several topics of AI and big data, including but not limited to the current and future perspectives, comprising ethical challenges and the role of laboratory specialists, including young professionals, the productive integration of AI with information technologies and with other digital infrastructure, such as the LOINC and the block chain. Furthermore, some examples of real application of AI in LM were reported, including diagnosis and monitoring of familiar hypercholesterolemia, management of insulin treatments for diabetes, reference intervals identification and verification by indirect methods, COVID-19 diagnosis and the monitoring of outpatients monoclonal gammopathy treatment by digital healthcare</span></p>
Biochimica Clinica ; 47(1) 074-081
Documenti - Documents
 
Calcolo e valutazione dei valori di riferimento della troponina cardiaca I (cTnI) misurata in un gruppo di volontari sani italiani con metodi immunometrici ad alta sensibilità: uno studio multicentrico
Establishment and evaluation of cardiac troponin I reference values measured in a group of Italian healthy volunteers using high-sensitivity methods: a multi-center study.
<p>Introduction: this study compares the cardiac troponin I (cTnI) values measured with three high-sensitivity (hs) different methods in apparently healthy volunteers enrolled in a multicenter study.<br />Methods: heparinized plasma samples were collected from 1511 volunteers in 8 Italian clinical institutions (mean age 51.5 years, SD 14.2, range 18-86, female to male ratio 0.94). All volunteers denied chronic or acute diseases and had normal values of routine laboratory tests and ECG. The reference laboratory of the study (Laboratorio Fondazione CNR Regione Toscana G. Monasterio, Pisa, Italy) assayed all plasma samples with three hs-methods: Architect hs-cTnI, Access hs-cTnIand ADVIA Centaur XPT hs-cTnI. After the exclusion of outlier values, calculation of 99th percentile (Upper Reference Limit, URL) values was performed using both robust nonparametric and bias corrected and accelerated bootstrap methods.<br />Results: large between-method differences were found. ADVIA Centaur measured higher cTnI values (up to 2-fold) than the two other methods. cTnI values were significantly higher in men than in women, and progressively increased with age over 55 years. Moreover, 99th percentile URL values also depended on the statistical approach used for calculation (robust non-parametric versusbootstrap). All 99th percentile URL values calculated with non-parametric robust method were on average slightly lower than those suggested by manufacturers (mean difference 4.2 ng/L, standard error 1.7, p=0.0273).<br />Conclusion: clinicians should be advised that plasma samples from the same patient should be measured for hs-cTnI in the same laboratory. Specific clinical studies are needed to establish the most appropriate statistical approach to calculate 99th percentile URL values for hs-cTnI methods.</p>
Biochimica Clinica ; 44(2) S032-S047
Contributi Scientifici - Scientific Papers