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Yang Y, Li X, Lu J, Ge J, Chen M, Yao R, Tian M, Wang J, Liu F, Zuo C. Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism. Neural Regen Res 2025; 20:93-106. [PMID: 38767479 PMCID: PMC11246150 DOI: 10.4103/1673-5374.391180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/18/2023] [Indexed: 05/22/2024] Open
Abstract
Nowadays, presynaptic dopaminergic positron emission tomography, which assesses deficiencies in dopamine synthesis, storage, and transport, is widely utilized for early diagnosis and differential diagnosis of parkinsonism. This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism. We conducted a thorough literature search using reputable databases such as PubMed and Web of Science. Selection criteria involved identifying peer-reviewed articles published within the last 5 years, with emphasis on their relevance to clinical applications. The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis. Moreover, when employed in conjunction with other imaging modalities and advanced analytical methods, presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker. This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion. In summary, the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials, ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.
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Affiliation(s)
- Yujie Yang
- Key Laboratory of Arrhythmias, Ministry of Education, Department of Medical Genetics, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinyi Li
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Mingjia Chen
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ruixin Yao
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Mei Tian
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- International Human Phenome Institutes (Shanghai), Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengtao Liu
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
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Niu J, Zhong Y, Jin C, Cen P, Wang J, Cui C, Xue L, Cui X, Tian M, Zhang H. Positron Emission Tomography Imaging of Synaptic Dysfunction in Parkinson's Disease. Neurosci Bull 2024; 40:743-758. [PMID: 38483697 PMCID: PMC11178751 DOI: 10.1007/s12264-024-01188-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/09/2023] [Indexed: 06/15/2024] Open
Abstract
Parkinson's disease (PD) is one of the most common neurodegenerative diseases with a complex pathogenesis. Aggregations formed by abnormal deposition of alpha-synuclein (αSyn) lead to synapse dysfunction of the dopamine and non-dopamine systems. The loss of dopaminergic neurons and concomitant alterations in non-dopaminergic function in PD constitute its primary pathological manifestation. Positron emission tomography (PET), as a representative molecular imaging technique, enables the non-invasive visualization, characterization, and quantification of biological processes at cellular and molecular levels. Imaging synaptic function with PET would provide insights into the mechanisms underlying PD and facilitate the optimization of clinical management. In this review, we focus on the synaptic dysfunction associated with the αSyn pathology of PD, summarize various related targets and radiopharmaceuticals, and discuss applications and perspectives of PET imaging of synaptic dysfunction in PD.
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Affiliation(s)
- Jiaqi Niu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Peili Cen
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Jing Wang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Chunyi Cui
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Le Xue
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Xingyue Cui
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China.
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China.
- Huashan Hospital and Human Phenome Institute, Fudan University, Shanghai, 200040, China.
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China.
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China.
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310014, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310014, China.
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Sung C, Oh SJ, Kim JS. Imaging Procedure and Clinical Studies of [ 18F]FP-CIT PET. Nucl Med Mol Imaging 2024; 58:185-202. [PMID: 38932763 PMCID: PMC11196481 DOI: 10.1007/s13139-024-00840-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/15/2023] [Accepted: 01/05/2024] [Indexed: 06/28/2024] Open
Abstract
N-3-[18F]fluoropropyl-2β-carbomethoxy-3β-4-iodophenyl nortropane ([18F]FP-CIT) is a radiopharmaceutical for dopamine transporter (DAT) imaging using positron emission tomography (PET) to detect dopaminergic neuronal degeneration in patients with parkinsonian syndrome. [18F]FP-CIT was granted approval by the Ministry of Food and Drug Safety in 2008 as the inaugural radiopharmaceutical for PET imaging, and it has found extensive utilization across numerous institutions in Korea. This review article presents an imaging procedure for [18F]FP-CIT PET to aid nuclear medicine physicians in clinical practice and systematically reviews the clinical studies associated with [18F]FP-CIT PET.
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Affiliation(s)
- Changhwan Sung
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505 Republic of Korea
| | - Seung Jun Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505 Republic of Korea
| | - Jae Seung Kim
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505 Republic of Korea
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Di Sarno L, Caroselli A, Tonin G, Graglia B, Pansini V, Causio FA, Gatto A, Chiaretti A. Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives. Biomedicines 2024; 12:1220. [PMID: 38927427 PMCID: PMC11200597 DOI: 10.3390/biomedicines12061220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.
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Affiliation(s)
- Lorenzo Di Sarno
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
| | - Anya Caroselli
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Giovanna Tonin
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Benedetta Graglia
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Valeria Pansini
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Francesco Andrea Causio
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Section of Hygiene and Public Health, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gatto
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Antonio Chiaretti
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
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Wang J, Xue L, Jiang J, Liu F, Wu P, Lu J, Zhang H, Bao W, Xu Q, Ju Z, Chen L, Jiao F, Lin H, Ge J, Zuo C, Tian M. Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:17. [PMID: 38253738 PMCID: PMC10803804 DOI: 10.1038/s41746-024-01012-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.
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Affiliation(s)
- Jing Wang
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Le Xue
- Department of Nuclear Medicine, the Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Fengtao Liu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Weiqi Bao
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Qian Xu
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Zizhao Ju
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Chen
- Department of Ultrasound Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangyang Jiao
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huamei Lin
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Mei Tian
- Huashan Hospital & Human Phenome Institute, Fudan University, Shanghai, China.
- Department of Nuclear Medicine/PET Center, Huashan Hospital, Fudan University, Shanghai, China.
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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