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Kakafika MG, Lyta AA, Gavriilidis GI, Tsiftsoglou SA, Miliotou AN, Pappas IS, Vizirianakis IS, Papadopoulou LC, Tsiftsoglou AS. Targeting mitochondrial bioenergetics by combination treatment with imatinib and dichloroacetate in human erythroleukemic K‑562 and colorectal HCT‑116 cancer cells. Int J Oncol 2024; 64:42. [PMID: 38426621 PMCID: PMC10919756 DOI: 10.3892/ijo.2024.5630] [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: 07/07/2023] [Accepted: 01/22/2024] [Indexed: 03/02/2024] Open
Abstract
Tumor malignant cells are characterized by dysregulation of mitochondrial bioenergetics due to the 'Warburg effect'. In the present study, this metabolic imbalance was explored as a potential target for novel cancer chemotherapy. Imatinib (IM) downregulates the expression levels of SCΟ2 and FRATAXIN (FXN) genes involved in the heme‑dependent cytochrome c oxidase biosynthesis and assembly pathway in human erythroleukemic IM‑sensitive K‑562 chronic myeloid leukemia cells (K‑562). In the present study, it was investigated whether the treatment of cancer cells with IM (an inhibitor of oxidative phosphorylation) separately, or together with dichloroacetate (DCA) (an inhibitor of glycolysis), can inhibit cell proliferation or cause death. Human K‑562 and IM‑chemoresistant K‑562 chronic myeloid leukemia cells (K‑562R), as well as human colorectal carcinoma cells HCT‑116 (+/+p53) and (‑/‑p53, with double TP53 knock-in disruptions), were employed. Treatments of these cells with either IM (1 or 2 µM) and/or DCA (4 mΜ) were also assessed for the levels of several process biomarkers including SCO2, FXN, lactate dehydrogenase A, glyceraldehyde‑3‑phosphate dehydrogenase, pyruvate kinase M2, hypoxia inducing factor‑1a, heme oxygenase‑1, NF‑κB, stem cell factor and vascular endothelial growth factor via western blot analysis. Computational network biology models were also applied to reveal the connections between the ten proteins examined. Combination treatment of IM with DCA caused extensive cell death (>75%) in K‑562 and considerable (>45%) in HCT‑116 (+/+p53) cultures, but less in K‑562R and HCT‑116 (‑/‑p53), with the latter deficient in full length p53 protein. Such treatment, markedly reduced reactive oxygen species levels, as measured by flow‑cytometry, in K‑562 cells and affected the oxidative phosphorylation and glycolytic biomarkers in all lines examined. These findings indicated, that targeting of cancer mitochondrial bioenergetics with such a combination treatment was very effective, although chemoresistance to IM in leukemia and the absence of a full length p53 in colorectal cells affected its impact.
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MESH Headings
- Humans
- Imatinib Mesylate/pharmacology
- Imatinib Mesylate/therapeutic use
- Tumor Suppressor Protein p53/genetics
- Vascular Endothelial Growth Factor A/metabolism
- Apoptosis
- Cell Line, Tumor
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/pathology
- Energy Metabolism
- Leukemia, Erythroblastic, Acute
- Colorectal Neoplasms/drug therapy
- Colorectal Neoplasms/genetics
- Biomarkers/metabolism
- K562 Cells
- Drug Resistance, Neoplasm/genetics
- Cell Proliferation
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Affiliation(s)
- Maria G. Kakafika
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Department of Biochemistry and Biotechnology, University of Thessaly, Larissa 41500, Greece
| | - Areti A. Lyta
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - George I. Gavriilidis
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki 57001, Greece
| | - Stefanos A. Tsiftsoglou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Androulla N. Miliotou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Department of Health Sciences, KES College, Nicosia 1055, Cyprus
- Department of Health Sciences, School of Life and Health Sciences, University of Nicosia, Nicosia 2417, Cyprus
| | - Ioannis S. Pappas
- Laboratory of Pharmacology and Toxicology, Faculty of Veterinary Science, School of Health Sciences, University of Thessaly, Karditsa 43100, Greece
| | - Ioannis S. Vizirianakis
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Department of Health Sciences, School of Life and Health Sciences, University of Nicosia, Nicosia 2417, Cyprus
| | - Lefkothea C. Papadopoulou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Asterios S. Tsiftsoglou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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Dimitsaki S, Gavriilidis GI, Dimitriadis VK, Natsiavas P. Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence. Artif Intell Med 2023; 137:102490. [PMID: 36868685 PMCID: PMC9846931 DOI: 10.1016/j.artmed.2023.102490] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/19/2023]
Abstract
The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments. Based on this review, the use of an ensemble of ML algorithms that analyze clinical and biological data (i.e., plasma proteomics) of COVID-19 patients is designed and deployed to evaluate the potential use of AI for early COVID-19 patient triage. The proposed pipeline is evaluated using three publicly available datasets for training and testing. Three ML "tasks" are defined, and several algorithms are tested through a hyperparameter tuning method to identify the highest-performance models. As overfitting is one of the typical pitfalls for such approaches (mainly due to the size of the training/validation datasets), a variety of evaluation metrics are used to mitigate this risk. In the evaluation procedure, recall scores ranged from 0.6 to 0.74 and F1-score from 0.62 to 0.75. The best performance is observed via Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms. Additionally, input data (proteomics and clinical data) were ranked based on corresponding Shapley additive explanation (SHAP) values and evaluated for their prognosticated capacity and immuno-biological credence. This "interpretable" approach revealed that our ML models could discern critical COVID-19 cases predominantly based on patient's age and plasma proteins on B cell dysfunction, hyper-activation of inflammatory pathways like Toll-like receptors, and hypo-activation of developmental and immune pathways like SCF/c-Kit signaling. Finally, the herein computational workflow is corroborated in an independent dataset and MLP superiority along with the implication of the abovementioned predictive biological pathways are corroborated. Regarding limitations of the presented ML pipeline, the datasets used in this study contain less than 1000 observations and a significant number of input features hence constituting a high-dimensional low-sample (HDLS) dataset which could be sensitive to overfitting. An advantage of the proposed pipeline is that it combines biological data (plasma proteomics) with clinical-phenotypic data. Thus, in principle, the presented approach could enable patient triage in a timely fashion if used on already trained models. However, larger datasets and further systematic validation are needed to confirm the potential clinical value of this approach. The code is available on Github: https://github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics.
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Affiliation(s)
- Stella Dimitsaki
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece.
| | - George I Gavriilidis
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
| | - Vlasios K Dimitriadis
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
| | - Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
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Gavriilidis GI, Dimitriadis VK, Jaulent MC, Natsiavas P. Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach. Drug Saf 2021; 44:1165-1178. [PMID: 34674190 PMCID: PMC8553681 DOI: 10.1007/s40264-021-01103-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2021] [Indexed: 12/02/2022]
Abstract
Introduction Information technology (IT) plays an important role in the healthcare landscape via the increasing digitization of medical data and the use of modern computational paradigms such as machine learning (ML) and knowledge graphs (KGs). These ‘intelligent’ technical paradigms provide a new digital ‘toolkit’ supporting drug safety and healthcare processes, including ‘active pharmacovigilance’. While these technical paradigms are promising, intelligent systems (ISs) are not yet widely adopted by pharmacovigilance (PV) stakeholders, namely the pharma industry, academia/research community, drug safety monitoring organizations, regulatory authorities, and healthcare institutions. The limitations obscuring the integration of ISs into PV activities are multifaceted, involving technical, legal and medical hurdles, and thus require further elucidation. Objective We dissect the abovementioned limitations by describing the lessons learned during the design and implementation of the PVClinical platform, a web platform aiming to support the investigation of potential adverse drug reactions (ADRs), emphasizing the use of knowledge engineering (KE) as its main technical paradigm. Results To this end, we elaborate on the related ‘business processes’ (i.e. operational processes) and ‘user goals’ identified as part of the PVClinical platform design process based on Design Thinking principles. We also elaborate on key challenges restricting the adoption of such ISs and their integration in the clinical setting and beyond. Conclusions We highlight the fact that beyond providing analytics and useful statistics to the end user, ‘actionability’ has emerged as the operational priority identified through the whole process. Furthermore, we focus on the needs for valid, reproducible, explainable and human-interpretable results, stressing the need to emphasize on usability.
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Affiliation(s)
- George I. Gavriilidis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001 Thermi, Thessaloniki Greece
| | - Vlasios K. Dimitriadis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001 Thermi, Thessaloniki Greece
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d’Informatique Médicale et d’Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006 Paris, France
| | - Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, PO Box 60361, 57001 Thermi, Thessaloniki Greece
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d’Informatique Médicale et d’Ingénierie des Connaissances pour la e-Santé, LIMICS, 75006 Paris, France
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Dimitriadis VK, Gavriilidis GI, Natsiavas P. Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data. Stud Health Technol Inform 2021; 281:555-559. [PMID: 34042637 DOI: 10.3233/shti210232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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] [Indexed: 06/12/2023]
Abstract
Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.
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Affiliation(s)
- Vlasios K Dimitriadis
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
| | - George I Gavriilidis
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
| | - Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
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Gavriilidis GI, Ntoufa S, Papakonstantinou N, Kotta K, Koletsa T, Chartomatsidou E, Moysiadis T, Stavroyianni N, Anagnostopoulos A, Papadaki E, Tsiftsoglou AS, Stamatopoulos K. Stem cell factor is implicated in microenvironmental interactions and cellular dynamics of chronic lymphocytic leukemia. Haematologica 2021; 106:692-700. [PMID: 32336682 PMCID: PMC7927890 DOI: 10.3324/haematol.2019.236513] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Indexed: 01/03/2023] Open
Abstract
The inflammatory cytokine stem cell factor (SCF, ligand of c-kit receptor)
has been implicated as a pro-oncogenic driver and an adverse
prognosticator in several human cancers. Increased SCF levels have
recently been reported in a small series of patients with chronic lymphocytic
leukemia (CLL), however its precise role in CLL pathophysiology
remains elusive. In this study, CLL cells were found to express predominantly
the membrane isoform of SCF, which is known to elicit a more
robust activation of the c-kit receptor. SCF was significantly overexpressed
in CLL cells compared to healthy tonsillar B cells and it correlated with
adverse prognostic biomarkers, shorter time-to-first treatment and shorter
overall survival. Activation of immune receptors and long-term cell-cell
interactions with the mesenchymal stroma led to an elevation of SCF primarily
in CLL cases with an adverse prognosis. Contrariwise, suppression
of oxidative stress and the BTK inhibitor ibrutinib lowered SCF levels.
Interestingly, SCF significantly correlated with mitochondrial dynamics
and hypoxia-inducible factor-1a which have previously been linked with
clinical aggressiveness in CLL. SCF was able to elicit direct biological
effects in CLL cells, affecting redox homeostasis and cell proliferation.
Overall, the aberrantly expressed SCF in CLL cells emerges as a key
response regulator to microenvironmental stimuli while correlating with
poor prognosis. On these grounds, specific targeting of this inflammatory
molecule could serve as a novel therapeutic approach in CLL.
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Affiliation(s)
- George I Gavriilidis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stavroula Ntoufa
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Nikos Papakonstantinou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Konstantia Kotta
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Triantafyllia Koletsa
- Department of Pathology, Faculty of Medicine, Aristotle University, Thessaloniki, Greece
| | - Elisavet Chartomatsidou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Theodoros Moysiadis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece,Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | - Niki Stavroyianni
- Hematology Department and HCT Unit, G. Papanicolaou Hospital, Thessaloniki, Greece
| | | | - Eleni Papadaki
- Department of Medicine, University of Crete, Heraklion, Greece
| | - Asterios S Tsiftsoglou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kostas Stamatopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece,Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
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Natsiavas P, Gavriilidis GI, Linardaki Z, Kolangi G, Gkaliagkousi E, Zamboulis C, Jaulent MC. Supporting Active Pharmacovigilance via IT Tools in the Clinical Setting and Beyond: Regulatory and Management Aspects. Stud Health Technol Inform 2020; 272:342-345. [PMID: 32604672 DOI: 10.3233/shti200565] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Information Technology (IT) could have a prominent role towards the "Active Pharmacovigilance" (AP) paradigm by facilitating the analysis of potential Adverse Drug Reactions (ADRs). PVClinical project aims to build an IT platform enabling the investigation of potential ADRs in the clinical environment and beyond. In this paper, we outline the respective EU regulatory framework and the related Business Processes (BPs), elaborated based on input from clinicians and PV experts as part of the project's "user requirements analysis" phase, highlighting their potential pivotal role in the design of IT tools aiming to support AP.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, F-75006 Paris, France
| | - George I Gavriilidis
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
| | | | | | - Evgenia Gkaliagkousi
- 3rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, F-75006 Paris, France
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Papadopoulou LC, Ingendoh-Tsakmakidis A, Mpoutoureli CN, Tzikalou LD, Spyridou ED, Gavriilidis GI, Kaiafas GC, Ntaska AT, Vlachaki E, Panayotou G, Samiotaki M, Tsiftsoglou AS. Production and Transduction of a Human Recombinant β-Globin Chain into Proerythroid K-562 Cells To Replace Missing Endogenous β-Globin. Mol Pharm 2018; 15:5665-5677. [PMID: 30375878 DOI: 10.1021/acs.molpharmaceut.8b00857] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Protein replacement therapy (PRT) has been applied to treat severe monogenetic/metabolic disorders characterized by a protein deficiency. In disorders where an intracellular protein is missing, PRT is not easily feasible due to the inability of proteins to cross the cell membrane. Instead, gene therapy has been applied, although still with limited success. β-Thalassemias are severe congenital hemoglobinopathies, characterized by deficiency or reduced production of the adult β-globin chain. The resulting imbalance of α-/β-globin chains of adult hemoglobin (α2β2) leads to precipitation of unpaired α-globin chains and, eventually, to defective erythropoiesis. Since protein transduction domain (PTD) technology has emerged as a promising therapeutic approach, we produced a human recombinant β-globin chain in fusion with the TAT peptide and successfully transduced it into human proerythroid K-562 cells, deficient in mature β-globin chain. Notably, the produced human recombinant β-globin chain without the TAT peptide, used as internal negative control, failed to be transduced into K-562 cells under similar conditions. In silico studies complemented by SDS-PAGE, Western blotting, co-immunoprecipitation and LC-MS/MS analysis indicated that the transduced recombinant fusion TAT-β-globin protein interacts with the endogenous native α-like globins to form hemoglobin α2β2-like tetramers to a limited extent. Our findings provide evidence that recombinant TAT-β-globin is transmissible into proerythroid K-562 cells and can be potentially considered as an alternative protein therapeutic approach for β-thalassemias.
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Affiliation(s)
- Lefkothea C Papadopoulou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Alexandra Ingendoh-Tsakmakidis
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Christina N Mpoutoureli
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Lamprini D Tzikalou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Efthymia D Spyridou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - George I Gavriilidis
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Georgios C Kaiafas
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Agoritsa T Ntaska
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
| | - Efthymia Vlachaki
- Adult Thalassemia Unit , Hippokrateion General Hospital , Thessaloniki 54642 , Greece
| | | | | | - Asterios S Tsiftsoglou
- Laboratory of Pharmacology, School of Pharmacy, Faculty of Health Sciences , Aristotle University of Thessaloniki , Thessaloniki 54124 , Macedonia , Greece
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