Κυρατζοπούλου Ελένη

Η Ελένη Κυρατζοπούλου έλαβε το πτυχίο της το 2008 απο το τμήμα Μοριακής Βιολογίας και Γενετικής του Δημοκριτείου Πανεπιστημίου Θράκης. Το 2010 πήρε το Μεταπτυχιακό Δίπλωμα από το Τμήμα Χημείας του Εθνικού και Καποδιστριακού Πανεπιστημίου Αθηνών στην ειδίκευση της βιοχημείας. Την ερευνητική της εργασία στα πλαίσια του μεταπτυχιακού της την εκπόνησε στο Ε.ΚΕ.ΦΕ ΔΗΜΟΚΡΙΤΟ στο Ινστιτούτο Ραδιοϊσοτόπων και Ραδιοδιαγνωστικών προϊόντων. Από το 2010 ήταν υπότροφος στο Ελληνικό Ινστιτούτο Παστέρ στο εργαστήριο Μοριακής Βιολογίας και Ανοσοβιοτεχνολογίας όπου συμμετείχε σε ερευνητικά προγράμματα. Έχει συμμετέχει και σε ερευνητικά προγράμματα στην Κωνσταντινούπολη της Τουρκίας στο T.C Istanbul University Institute of Experimental Medical Resarch Department of Molecular Medicine (DETAE). Ηταν και αναπληρώτρια εκπαιδευτικός στο μάθημα της Βιολογίας στην δευτεροβάθμια εκπαίδευση, Το Μάρτιο του 2024 ξεκίνησε την συνεργασίας της με το ΙΕΛ / Ε.Κ. “Αθηνά” Θράκης όπου συμμετέχει στο πρόγραμμα “ALGEBRA – Καινοτόμος πρακτική βιοθεραπείας λυμάτων για την απομάκρυνση τοξικών ενώσεων με τη χρήση μικροφυκών στο πλαίσιο μιας κυκλικής οικονομίας” ως βιολόγος του προγράμματος.

@article{heritage8110447,
title = {From Contemporary Datasets to Cultural Heritage Performance: Explainability and Energy Profiling of Visual Models Towards Textile Identification},
author = {Evaggelos Nerantzis and Lamprini Malletzidou and Eleni Kyratzopoulou and Nestor Tsirliganis and Nikolaos Kazakis},
url = {https://www.mdpi.com/2571-9408/8/11/447},
doi = {10.3390/heritage8110447},
issn = {2571-9408},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Heritage},
volume = {8},
number = {11},
abstract = {The identification and classification of textiles play a crucial role in archaeometric studies, in the vicinity of their technological, economic, and cultural significance. Traditional textile analysis is closely related to optical microscopy and observation, while other microscopic, analytical, and spectroscopic techniques prevail over fiber identification for composition purposes. This protocol can be invasive and destructive for the artifacts under study, time-consuming, and it often relies on personal expertise. In this preliminary study, an alternative, macroscopic approach is proposed, based on texture and surface textile characteristics, using low-magnification images and deep learning models. Under this scope, a publicly available, imbalanced textile image dataset was used to pretrain and evaluate six computer vision architectures (ResNet50, EfficientNetV2, ViT, ConvNeXt, Swin Transformer, and MaxViT). In addition to accuracy, energy efficiency and ecological footprint of the process were assessed using the CodeCarbon tool. The results indicate that two of the convolutional neural network models, Swin and EfficientNetV2, both deliver competitive accuracies together with low carbon emissions, in comparison to the transformer and hybrid models. This alternative, promising, sustainable, and non-invasive approach for textile classification demonstrates the feasibility of developing a custom, heritage-based image dataset.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{cleantechnol7020035,
title = {The Efficiency of Chlorella vulgaris in Heavy Metal Removal: A Comparative Study of Mono- and Multi-Component Metal Systems},
author = {Eleni Kyratzopoulou and Nikoletta Kyzaki and Lamprini Malletzidou and Evaggelos Nerantzis and Nikolaos Kazakis},
url = {https://www.mdpi.com/2571-8797/7/2/35},
doi = {10.3390/cleantechnol7020035},
issn = {2571-8797},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Clean Technologies},
volume = {7},
number = {2},
abstract = {The occurrence of heavy metals in aquatic ecosystems is a serious environmental hazard, and their effective removal is imperative. In this regard, the feasibility of living microalga Chlorella vulgaris (C. vulgaris) to remove heavy metals (Ni, Pb, Zn, Cd, and Cu) is investigated by using 1, 5, and 10 ppm concentrations of single- and multiple-metal-treated (MT) cultures. Experiments were performed in controlled laboratory conditions, and metal removal analysis was performed through atomic absorption spectroscopy (AAS). The cultures were also examined by means of optical microscopy, UV-Vis spectrophotometry, and Fourier transform infrared (FTIR) spectroscopy to follow the cultures’ pigment content, cell population, and functional group changes during cultivation. The removal efficiency results of both single and multiple MT cultures were evaluated using the Langmuir isotherm model. The results indicate that C. vulgaris presents potential for heavy metal bioremediation, even towards multi-MT conditions, despite the influence of a competitive uptake in multi-MT cultures. In mono-MT cultures, the removal efficiency of C. vulgaris presents values of 65–99% on Day 3 and 72–99% on Day 7 of cultivation, while the results for the multi-MT cultures are 49–99% and 62–99% for Days 3 and 7 of cultivation, respectively. The research illustrates the potential for C. vulgaris as a promising biosorbent for heavy metal remediation along with its post-treatment use in applications supporting the green circular economy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Κυρατζοπούλου Ελένη

Δημοσιεύσεις

2026

2025