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Romano D, Novielli P, Diacono D, Cilli R, Pantaleo E, Amoroso N, Bellantuono L, Monaco A, Bellotti R, Tangaro S. Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy. J Pers Med 2024; 14:430. [PMID: 38673057 PMCID: PMC11051343 DOI: 10.3390/jpm14040430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors.
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Affiliation(s)
- Donato Romano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Pierfrancesco Novielli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Roberto Cilli
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento di Biomedicina Traslazionale e Neuroscienze, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
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Fania A, Monaco A, Amoroso N, Bellantuono L, Cazzolla Gatti R, Firza N, Lacalamita A, Pantaleo E, Tangaro S, Velichevskaya A, Bellotti R. Machine learning and XAI approaches highlight the strong connection between O 3 and N O 2 pollutants and Alzheimer's disease. Sci Rep 2024; 14:5385. [PMID: 38443419 DOI: 10.1038/s41598-024-55439-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/23/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly O 3 and N O 2 ) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer's disease and to confirm the urgent need to further investigate the relationship between the environment and the disease.
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Affiliation(s)
- Alessandro Fania
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy.
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
| | - Roberto Cazzolla Gatti
- Department of Biological Sciences, Geological and Environmental (BiGeA), Alma Mater Studiorum - University of Bologna, 40126, Bologna, Italy
| | - Najada Firza
- Dipartimento di Economia e Finanza, Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
- Catholic University Our Lady of Good Counsel, 1031, Tirana, Albania
| | - Antonio Lacalamita
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Ester Pantaleo
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126, Bari, Italy
| | | | - Roberto Bellotti
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
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Scattarella F, Diacono D, Monaco A, Amoroso N, Bellantuono L, Massaro G, Pepe FV, Tangaro S, Bellotti R, D'Angelo M. Deep learning approach for denoising low-SNR correlation plenoptic images. Sci Rep 2023; 13:19645. [PMID: 37950034 PMCID: PMC10638444 DOI: 10.1038/s41598-023-46765-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023] Open
Abstract
Correlation Plenoptic Imaging (CPI) is a novel volumetric imaging technique that uses two sensors and the spatio-temporal correlations of light to detect both the spatial distribution and the direction of light. This novel approach to plenoptic imaging enables refocusing and 3D imaging with significant enhancement of both resolution and depth of field. However, CPI is generally slower than conventional approaches due to the need to acquire sufficient statistics for measuring correlations with an acceptable signal-to-noise ratio (SNR). We address this issue by implementing a Deep Learning application to improve image quality with undersampled frame statistics. We employ a set of experimental images reconstructed by a standard CPI architecture, at three different sampling ratios, and use it to feed a CNN model pre-trained through the transfer learning paradigm U-Net architecture with VGG-19 net for the encoding part. We find that our model reaches a Structural Similarity (SSIM) index value close to 1 both for the test sample (SSIM = [Formula: see text]) and in 5-fold cross validation (SSIM = [Formula: see text]); the results are also shown to outperform classic denoising methods, in particular for images with lower SNR. The proposed work represents the first application of Artificial Intelligence in the field of CPI and demonstrates its high potential: speeding-up the acquisition by a factor 20 over the fastest CPI so far demonstrated, enabling recording potentially 200 volumetric images per second. The presented results open the way to scanning-free real-time volumetric imaging at video rate, which is expected to achieve a substantial influence in various applications scenarios, from monitoring neuronal activity to machine vision and security.
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Affiliation(s)
- Francesco Scattarella
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy.
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
| | - Gianlorenzo Massaro
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Francesco V Pepe
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Milena D'Angelo
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
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Lacalamita A, Serino G, Pantaleo E, Monaco A, Amoroso N, Bellantuono L, Piccinno E, Scalavino V, Dituri F, Tangaro S, Bellotti R, Giannelli G. Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma. Int J Mol Sci 2023; 24:15286. [PMID: 37894965 PMCID: PMC10607580 DOI: 10.3390/ijms242015286] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/05/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and the number of cases is constantly increasing. Early and accurate HCC diagnosis is crucial to improving the effectiveness of treatment. The aim of the study is to develop a supervised learning framework based on hierarchical community detection and artificial intelligence in order to classify patients and controls using publicly available microarray data. With our methodology, we identified 20 gene communities that discriminated between healthy and cancerous samples, with an accuracy exceeding 90%. We validated the performance of these communities on an independent dataset, and with two of them, we reached an accuracy exceeding 80%. Then, we focused on two communities, selected because they were enriched with relevant biological functions, and on these we applied an explainable artificial intelligence (XAI) approach to analyze the contribution of each gene to the classification task. In conclusion, the proposed framework provides an effective methodological and quantitative tool helping to find gene communities, which may uncover pivotal mechanisms responsible for HCC and thus discover new biomarkers.
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Affiliation(s)
- Antonio Lacalamita
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy; (A.L.); (E.P.); (R.B.)
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
| | - Grazia Serino
- National Institute of Gastroenterology S. De Bellis, IRCCS Research Hospital, Via Turi 27, 70013 Castellana Grotte, BA, Italy; (G.S.); (E.P.); (V.S.); (F.D.); (G.G.)
| | - Ester Pantaleo
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy; (A.L.); (E.P.); (R.B.)
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy; (A.L.); (E.P.); (R.B.)
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
| | - Nicola Amoroso
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy
| | - Loredana Bellantuono
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy
| | - Emanuele Piccinno
- National Institute of Gastroenterology S. De Bellis, IRCCS Research Hospital, Via Turi 27, 70013 Castellana Grotte, BA, Italy; (G.S.); (E.P.); (V.S.); (F.D.); (G.G.)
| | - Viviana Scalavino
- National Institute of Gastroenterology S. De Bellis, IRCCS Research Hospital, Via Turi 27, 70013 Castellana Grotte, BA, Italy; (G.S.); (E.P.); (V.S.); (F.D.); (G.G.)
| | - Francesco Dituri
- National Institute of Gastroenterology S. De Bellis, IRCCS Research Hospital, Via Turi 27, 70013 Castellana Grotte, BA, Italy; (G.S.); (E.P.); (V.S.); (F.D.); (G.G.)
| | - Sabina Tangaro
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Via G. Amendola 165/a, 70126 Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy; (A.L.); (E.P.); (R.B.)
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, 70125 Bari, Italy; (N.A.); (L.B.); (S.T.)
| | - Gianluigi Giannelli
- National Institute of Gastroenterology S. De Bellis, IRCCS Research Hospital, Via Turi 27, 70013 Castellana Grotte, BA, Italy; (G.S.); (E.P.); (V.S.); (F.D.); (G.G.)
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Bellantuono L, Tommasi R, Pantaleo E, Verri M, Amoroso N, Crucitti P, Di Gioacchino M, Longo F, Monaco A, Naciu AM, Palermo A, Taffon C, Tangaro S, Crescenzi A, Sodo A, Bellotti R. An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis. Sci Rep 2023; 13:16590. [PMID: 37789191 PMCID: PMC10547772 DOI: 10.1038/s41598-023-43856-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/29/2023] [Indexed: 10/05/2023] Open
Abstract
Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Raffaele Tommasi
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Martina Verri
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
- Dipartimento di Scienze, Università degli Studi Roma Tre, 00146, Roma, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Pierfilippo Crucitti
- Unit of Thoracic Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | | | - Filippo Longo
- Unit of Thoracic Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Anda Mihaela Naciu
- Unit of Metabolic Bone and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Andrea Palermo
- Unit of Metabolic Bone and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Chiara Taffon
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Anna Crescenzi
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Armida Sodo
- Dipartimento di Scienze, Università degli Studi Roma Tre, 00146, Roma, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
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6
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Fania A, Monaco A, Amoroso N, Bellantuono L, Cazzolla Gatti R, Firza N, Lacalamita A, Pantaleo E, Tangaro S, Velichevskaya A, Bellotti R. A Dementia mortality rates dataset in Italy (2012-2019). Sci Data 2023; 10:564. [PMID: 37626087 PMCID: PMC10457292 DOI: 10.1038/s41597-023-02461-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Dementia is on the rise in the world population and has been defined by the World Health Organization as a global public health priority. In Italy, according to demographic projections, in 2051 there will be 280 elderly people for every 100 young people, with an increase in all age-related chronic diseases, including dementia. Currently the total number of patients with dementia is estimated to be over 1 million (mainly with Alzheimer's disease (AD) and Parkinson's disease (PD)). In-depth studies of the etiology and physiology of dementia are complicated due to the complexity of these diseases and their long duration. In this work we present a dataset on mortality rates (in the form of Standardized Mortality Ratios, SMR) for AD e PD in Italy at provincial level over a period of 8 years (2012-2019). Access to long-term, spatially detailed and ready-to-use data could favor both health monitoring and the research of new treatments and new drugs as well as innovative methodologies for early diagnosis of dementia.
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Affiliation(s)
- Alessandro Fania
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, Via G. Amendola 173, Bari, 70125, Italy
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, Via G. Amendola 173, Bari, 70125, Italy.
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy.
| | - Nicola Amoroso
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Via A. Orabona 4, Bari, 70125, Italy
| | - Loredana Bellantuono
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Universitá degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, Bari, 70124, Italy
| | - Roberto Cazzolla Gatti
- Department of Biological Sciences, Geological and Environmental (BiGeA), Alma Mater Studiorum - University of Bologna, Piazza Porta S. Donato 1, Bologna, 40126, Italy
| | - Najada Firza
- Dipartimento di Economia e Finanza, Universitá degli Studi di Bari Aldo Moro, Largo Abbazia S. Scolastica, Bari, 70124, Italy
- Catholic University Our Lady of Good Counsel, Rr. Dritan Hoxha 123, Laprake, Tirana, 1031, Albania
| | - Antonio Lacalamita
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, Via G. Amendola 173, Bari, 70125, Italy
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
| | - Ester Pantaleo
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, Via G. Amendola 173, Bari, 70125, Italy
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
| | - Sabina Tangaro
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Via G. Amendola 165/a, Bari, 70126, Italy
| | - Alena Velichevskaya
- Biological Institute, Tomsk State University, Lenin Ave., 36, Tomsk, 634050, Russia
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, Via G. Amendola 173, Bari, 70125, Italy
- Sezione di Bari, Istituto Nazionale di Fisica Nucleare (INFN), Via A. Orabona 4, Bari, 70125, Italy
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7
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Pergola G, Parihar M, Sportelli L, Bharadwaj R, Borcuk C, Radulescu E, Bellantuono L, Blasi G, Chen Q, Kleinman JE, Wang Y, Sripathy SR, Maher BJ, Monaco A, Rossi F, Shin JH, Hyde TM, Bertolino A, Weinberger DR. Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions. Sci Adv 2023; 9:eade2812. [PMID: 37058565 PMCID: PMC10104472 DOI: 10.1126/sciadv.ade2812] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.
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Affiliation(s)
- Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Sportelli
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Rahul Bharadwaj
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Christopher Borcuk
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Eugenia Radulescu
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Loredana Bellantuono
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yanhong Wang
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Srinidhi Rao Sripathy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Brady J. Maher
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Fabiana Rossi
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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8
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Marrone M, Bellantuono L, Stellacci A, Misceo F, Silvestre M, Zotti F, Dell’Erba A, Bellotti R. Haemorrhage and Survival Times: Medical-Legal Evaluation of the Time of Death and Relative Evidence. Diagnostics (Basel) 2023; 13:diagnostics13040732. [PMID: 36832220 PMCID: PMC9955172 DOI: 10.3390/diagnostics13040732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Haemorrhage is the name used to describe the loss of blood from damaged blood vessels (arteries, veins, capillaries). Identifying the time of haemorrhage remains a clinical challenge, knowing that blood perfusion of systemic tissues is poorly correlated with the perfusion of specific tissues. In forensic science, one of the most discussed elements is the time of death. This study aims to provide the forensic scientist with a valid model to establish a precise time-of-death interval in cases of exsanguination following trauma with vascular injury, which can be useful as a technical aid in the investigation of criminal cases. To calculate the calibre and resistance of the vessels, we used an extensive literature review of distributed one-dimensional models of the systemic arterial tree as a reference. We then arrived at a formula that allows us to estimate, based on a subject's total blood volume and the calibre of the injured vessel, a time interval within which a subject's death from haemorrhage from vascular injury falls. We applied the formula to four cases in which death had been caused by the injury of a single arterial vessel and obtained comforting results. The study model we have offered is only a good prospect for future work. In fact, we intend to improve the study by expanding the case and statistical analysis with particular regard to the interference factors to confirm its actual usability in practical cases; in this way, useful corrective factors can be identified.
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Affiliation(s)
- Maricla Marrone
- Dipartimento Interdisciplinare di Medicina, Legal Medicine Section, Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
| | - Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70125 Bari, Italy
- Correspondence:
| | - Alessandra Stellacci
- Dipartimento Interdisciplinare di Medicina, Legal Medicine Section, Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
| | - Federica Misceo
- Dipartimento Interdisciplinare di Medicina, Legal Medicine Section, Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
| | - Maria Silvestre
- Dipartimento Interdisciplinare di Medicina, Legal Medicine Section, Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
| | - Fiorenza Zotti
- Dipartimento Interdisciplinare di Medicina, Legal Medicine Section, Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
| | - Alessandro Dell’Erba
- Dipartimento Interdisciplinare di Medicina, Legal Medicine Section, Università degli Studi di Bari Aldo Moro, I-70124 Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70125 Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, I-70126 Bari, Italy
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9
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Togo MV, Mastrolorito F, Ciriaco F, Trisciuzzi D, Tondo AR, Gambacorta N, Bellantuono L, Monaco A, Leonetti F, Bellotti R, Altomare CD, Amoroso N, Nicolotti O. TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity. J Chem Inf Model 2023; 63:56-66. [PMID: 36520016 DOI: 10.1021/acs.jcim.2c01126] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure-Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it.
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Affiliation(s)
- Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Anna Rita Tondo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy.,Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy.,Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
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10
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Antonucci LA, Bellantuono L, Kleinbub JR, Lella A, Palmieri A, Salvatore S. The harmonium model and its unified system view of psychopathology: a validation study by means of a convolutional neural network. Sci Rep 2022; 12:21789. [PMID: 36526662 PMCID: PMC9758147 DOI: 10.1038/s41598-022-26054-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
The harmonium model (HM) is a recent conceptualization of the unifying view of psychopathology, namely the idea of a general mechanism underpinning all mental disorders (the p factor). According to HM, psychopathology consists of a low dimensional Phase Space of Meaning (PSM), where each dimension of meaning maps a component of the environmental variability. Accordingly, the lower thenumber of independent dimensions in the PSM, and hence its intrinsic complexity, the more limited the way of interpreting the environment. The current simulation study, based on a Convolutional Neural Network (CNN) framework, aims at validating the HM low-dimensionality hypothesis. CNN-based classifiers were employed to simulate normotypical and pathological cognitive processes. Results revealed that normotypical and pathological CNNs were different in terms of both classification performance and layer activation patterns. Using Principal Component Analysis to characterize the PSM associated with the two algorithms, we found that the performance of the normotypical CNN relies on a larger and more evenly distributed number of components, compared with the pathological one. This finding might be indicative of the fact that psychopathology can be modelled as a low-dimensional, poorly modulable PSM, which means the environment is detected through few components of meaning, preventing complex information patterns from being taken into account.
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Affiliation(s)
- Linda A. Antonucci
- grid.7644.10000 0001 0120 3326Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Loredana Bellantuono
- grid.7644.10000 0001 0120 3326Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70124 Bari, Italy ,grid.470190.bIstituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Johann Roland Kleinbub
- grid.5608.b0000 0004 1757 3470Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, 35139 Padua, Italy
| | - Annalisa Lella
- grid.7644.10000 0001 0120 3326Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70124 Bari, Italy
| | - Arianna Palmieri
- grid.5608.b0000 0004 1757 3470Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, 35139 Padua, Italy ,grid.5608.b0000 0004 1757 3470Padova Neuroscience Center, University of Padova, 35129 Padua, Italy
| | - Sergio Salvatore
- grid.9906.60000 0001 2289 7785Department of Human and Social Science, University of Salento, 73100 Lecce, Italy
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11
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Bellantuono L, Monaco A, Amoroso N, Lacalamita A, Pantaleo E, Tangaro S, Bellotti R. Worldwide impact of lifestyle predictors of dementia prevalence: An eXplainable Artificial Intelligence analysis. Front Big Data 2022; 5:1027783. [PMID: 36567754 PMCID: PMC9772995 DOI: 10.3389/fdata.2022.1027783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Dementia is an umbrella term indicating a group of diseases that affect the cognitive sphere. Dementia is not a mere individual health issue, since its interference with the ability to carry out daily activities entails a series of collateral problems, comprising exclusion of patients from civil rights and welfare, unpaid caregiving work, mostly performed by women, and an additional burden on the public healthcare systems. Thus, gender and wealth inequalities (both among individuals and among countries) tend to amplify the social impact of such a disease. Since at present there is no cure for dementia but only drug treatments to slow down its progress and mitigate the symptoms, it is essential to work on prevention and early diagnosis, identifying the risk factors that increase the probability of its onset. The complex and multifactorial etiology of dementia, resulting from an interplay between genetics and environmental factors, can benefit from a multidisciplinary approach that follows the "One Health" guidelines of the World Health Organization. Methods In this work, we apply methods of Artificial Intelligence and complex systems physics to investigate the possibility to predict dementia prevalence throughout world countries from a set of variables concerning individual health, food consumption, substance use and abuse, healthcare system efficiency. The analysis uses publicly available indicator values at a country level, referred to a time window of 26 years. Results Employing methods based on eXplainable Artificial Intelligence (XAI) and complex networks, we identify a group of lifestyle factors, mostly concerning nutrition, that contribute the most to dementia incidence prediction. Discussion The proposed approach provides a methodological basis to develop quantitative tools for action patterns against such a disease, which involves issues deeply related with sustainable, such as good health and resposible food consumption.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, Bari, Italy,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy,*Correspondence: Alfonso Monaco
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy,Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Antonio Lacalamita
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy,Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy,Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
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12
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Lombardi A, Diacono D, Amoroso N, Biecek P, Monaco A, Bellantuono L, Pantaleo E, Logroscino G, De Blasi R, Tangaro S, Bellotti R. A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease. Brain Inform 2022; 9:17. [PMID: 35882684 PMCID: PMC9325942 DOI: 10.1186/s40708-022-00165-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/03/2022] [Indexed: 11/11/2022] Open
Abstract
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer’s disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient’s cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer’s disease progression.
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Affiliation(s)
- Angela Lombardi
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy. .,Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy.
| | - Przemysław Biecek
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.,Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.,Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Ester Pantaleo
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy.,Pia Fondazione "Card. G. Panico", Tricase, Italy
| | | | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.,Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
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13
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Pantaleo E, Monaco A, Amoroso N, Lombardi A, Bellantuono L, Urso D, Lo Giudice C, Picardi E, Tafuri B, Nigro S, Pesole G, Tangaro S, Logroscino G, Bellotti R. A Machine Learning Approach to Parkinson’s Disease Blood Transcriptomics. Genes (Basel) 2022; 13:genes13050727. [PMID: 35627112 PMCID: PMC9141063 DOI: 10.3390/genes13050727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 03/12/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 12/23/2022] Open
Abstract
The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.
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Affiliation(s)
- Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy;
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
- Correspondence:
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy;
| | - Daniele Urso
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London SE5 8AF, UK
| | - Claudio Lo Giudice
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy; (C.L.G.); (E.P.); (G.P.)
| | - Ernesto Picardi
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy; (C.L.G.); (E.P.); (G.P.)
- Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Benedetta Tafuri
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
| | - Salvatore Nigro
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
- Istituto di Nanotecnologia (NANOTEC), Consiglio Nazionale delle Ricerche, Via Monteroni, 73100 Lecce, Italy
| | - Graziano Pesole
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy; (C.L.G.); (E.P.); (G.P.)
- Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy;
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
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14
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Bellantuono L, Monaco A, Amoroso N, Aquaro V, Lombardi A, Tangaro S, Bellotti R. Sustainable development goals: conceptualization, communication and achievement synergies in a complex network framework. Appl Netw Sci 2022; 7:14. [PMID: 35308061 PMCID: PMC8919151 DOI: 10.1007/s41109-022-00455-1] [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] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/01/2022] [Indexed: 06/02/2023]
Abstract
In this work we use a network-based approach to investigate the complex system of interactions among the 17 Sustainable Development Goals (SDGs), that constitute the structure of the United Nations 2030 Agenda for a sustainable future. We construct a three-layer multiplex, in which SDGs represent nodes, and their connections in each layer are determined by similarity definitions based on conceptualization, communication, and achievement, respectively. In each layer of the multiplex, we investigate the presence of nodes with high centrality, corresponding to strategic SDGs. We then compare the networks to establish whether and to which extent similar patterns emerge. Interestingly, we observe a significant relation between the SDG similarity patterns determined by their achievement and their communication and perception, revealed by social network data. The proposed framework represents an instrument to unveil new and nontrivial aspects of sustainability, laying the foundation of a decision support system to define and implement SDG achievement strategies.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli studi di Bari Aldo Moro, 70126 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Vincenzo Aquaro
- Division for Public Institutions and Digital Government, United Nations Department of Economic and Social Affairs (DESA), New York, NY 10017 USA
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli studi di Bari Aldo Moro, 70126 Bari, Italy
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15
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Monaco A, Pantaleo E, Amoroso N, Bellantuono L, Stella A, Bellotti R. Country-level factors dynamics and ABO/Rh blood groups contribution to COVID-19 mortality. Sci Rep 2021; 11:24527. [PMID: 34972836 PMCID: PMC8720090 DOI: 10.1038/s41598-021-04162-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/15/2021] [Indexed: 11/08/2022] Open
Abstract
The identification of factors associated to COVID-19 mortality is important to design effective containment measures and safeguard at-risk categories. In the last year, several investigations have tried to ascertain key features to predict the COVID-19 mortality tolls in relation to country-specific dynamics and population structure. Most studies focused on the first wave of the COVID-19 pandemic observed in the first half of 2020. Numerous studies have reported significant associations between COVID-19 mortality and relevant variables, for instance obesity, healthcare system indicators such as hospital beds density, and bacillus Calmette-Guerin immunization. In this work, we investigated the role of ABO/Rh blood groups at three different stages of the pandemic while accounting for demographic, economic, and health system related confounding factors. Using a machine learning approach, we found that the "B+" blood group frequency is an important factor at all stages of the pandemic, confirming previous findings that blood groups are linked to COVID-19 severity and fatal outcome.
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Affiliation(s)
- Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125, Bari, Italy
- Dipartimento di Scienze mediche di base, Neuroscienze e organi di senso, Piazza G. Cesare 11, 70124, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "'Aldo Moro", Via G. Amendola 173, 70125, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via A. Orabona 4, 70125, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125, Bari, Italy
- Dipartimento di Scienze mediche di base, Neuroscienze e organi di senso, Piazza G. Cesare 11, 70124, Bari, Italy
| | - Alessandro Stella
- Dipartimento di Scienze biomediche e oncologia umana, Università degli Studi di Bari "Aldo Moro", Bari, Italy.
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "'Aldo Moro", Via G. Amendola 173, 70125, Bari, Italy
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16
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Monaco A, Pantaleo E, Amoroso N, Bellantuono L, Lombardi A, Tateo A, Tangaro S, Bellotti R. Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach. Phys Biol 2020; 18:016003. [PMID: 33049726 DOI: 10.1088/1478-3975/abc09a] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.
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Affiliation(s)
- A Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
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17
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Amoroso N, Bellantuono L, Pascazio S, Lombardi A, Monaco A, Tangaro S, Bellotti R. Potential energy of complex networks: a quantum mechanical perspective. Sci Rep 2020; 10:18387. [PMID: 33110089 PMCID: PMC7592062 DOI: 10.1038/s41598-020-75147-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 10/12/2020] [Indexed: 12/26/2022] Open
Abstract
We propose a characterization of complex networks, based on the potential of an associated Schrödinger equation. The potential is designed so that the energy spectrum of the Schrödinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations, which we call ensemble potential, is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the ensemble potential profile is quantified by using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the ensemble potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Loredana Bellantuono
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Saverio Pascazio
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy.
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy.
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
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18
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Bellantuono L, Monaco A, Tangaro S, Amoroso N, Aquaro V, Bellotti R. An equity-oriented rethink of global rankings with complex networks mapping development. Sci Rep 2020; 10:18046. [PMID: 33093554 PMCID: PMC7582917 DOI: 10.1038/s41598-020-74964-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/08/2020] [Indexed: 12/11/2022] Open
Abstract
Nowadays, world rankings are promoted and used by international agencies, governments and corporations to evaluate country performances in a specific domain, often providing a guideline for decision makers. Although rankings allow a direct and quantitative comparison of countries, sometimes they provide a rather oversimplified representation, in which relevant aspects related to socio-economic development are either not properly considered or still analyzed in silos. In an increasingly data-driven society, a new generation of cutting-edge technologies is breaking data silos, enabling new use of public indicators to generate value for multiple stakeholders. We propose a complex network framework based on publicly available indicators to extract important insight underlying global rankings, thus adding value and significance to knowledge provided by these rankings. This approach enables the unsupervised identification of communities of countries, establishing a more targeted, fair and meaningful criterion to detect similarities. Hence, the performance of states in global rankings can be assessed based on their development level. We believe that these evaluations can be crucial in the interpretation of global rankings, making comparison between countries more significant and useful for citizens and governments and creating ecosystems for new opportunities for development.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", 70126, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari "A. Moro", 70126, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy.
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "A. Moro", 70125, Bari, Italy.
| | - Vincenzo Aquaro
- Division for Public Institutions and Digital Government, United Nations Department of Economic and Social Affairs (DESA), New York, NY, 10017, USA
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "A. Moro", 70126, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
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Bellantuono L, Marzano L, La Rocca M, Duncan D, Lombardi A, Maggipinto T, Monaco A, Tangaro S, Amoroso N, Bellotti R. Predicting brain age with complex networks: From adolescence to adulthood. Neuroimage 2020; 225:117458. [PMID: 33099008 DOI: 10.1016/j.neuroimage.2020.117458] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 10/13/2020] [Indexed: 01/21/2023] Open
Abstract
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Luca Marzano
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- University of Southern California, Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Dominique Duncan
- University of Southern California, Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy
| | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy.
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy; Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sez. di Bari, Bari, Italy; Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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20
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Monaco A, Amoroso N, Bellantuono L, Lella E, Lombardi A, Monda A, Tateo A, Bellotti R, Tangaro S. Shannon entropy approach reveals relevant genes in Alzheimer's disease. PLoS One 2019; 14:e0226190. [PMID: 31891941 PMCID: PMC6938408 DOI: 10.1371/journal.pone.0226190] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [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: 06/24/2019] [Accepted: 11/19/2019] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer’s disease.
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Affiliation(s)
- Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
- * E-mail:
| | - Loredana Bellantuono
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Eufemia Lella
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
| | - Anna Monda
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Andrea Tateo
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
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21
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Amoroso N, La Rocca M, Bellantuono L, Diacono D, Fanizzi A, Lella E, Lombardi A, Maggipinto T, Monaco A, Tangaro S, Bellotti R. Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age. Front Aging Neurosci 2019; 11:115. [PMID: 31178715 PMCID: PMC6538815 DOI: 10.3389/fnagi.2019.00115] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/01/2019] [Indexed: 12/27/2022] Open
Abstract
Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Loredana Bellantuono
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy
| | | | | | - Eufemia Lella
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | | | - Tommaso Maggipinto
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
| | | | | | - Roberto Bellotti
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari "A. Moro", Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Bari, Italy
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22
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Bellantuono L. Equilibration of a strongly interacting plasma: holographic analysis of local and nonlocal probes. EPJ Web Conf 2016. [DOI: 10.1051/epjconf/201612900041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Baldassarre ME, Bellantuono L, Mastromarino P, Miccheli A, Fanelli M, Laforgia N. Gut and Breast Milk Microbiota and Their Role in the Development of the Immune Function. Curr Pediatr Rep 2014. [DOI: 10.1007/s40124-014-0051-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Bellantuono L. Hybrid exotic mesons in soft-wall AdS/QCD. EPJ Web of Conferences 2014. [DOI: 10.1051/epjconf/20148000014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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