Publications by authors named "G Tzedakis"

Adjuvant Temozolomide is considered the front-line Glioblastoma chemotherapeutic treatment; yet not all patients respond. Latest trends in clinical trials usually refer to Doxorubicin; yet it can lead to severe side-effects if administered in high doses. While Glioblastoma prognosis remains poor, little is known about the combination of the two chemotherapeutics.

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The regulation policies implemented, the characteristics of vaccines, and the evolution of the virus continue to play a significant role in the progression of the SARS-CoV-2 pandemic. Numerous research articles have proposed using mathematical models to predict the outcomes of different scenarios, with the aim of improving awareness and informing policy-making. In this work, we propose an expansion to the classical SEIR epidemiological model that is designed to fit the complex epidemiological data of COVID-19.

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The development of label-free non-destructive techniques to be used as diagnostic tools in cancer research is of great importance for improving the quality of life for millions of patients. Previous studies have demonstrated that Third Harmonic Generation (THG) imaging could differentiate malignant from benign unlabeled human breast biopsies and distinguish the different grades of cancer. Towards the application of such technologies to clinic, in the present report, a deep learning technique was applied to THG images recorded from breast cancer tissues of grades 0, I, II, and III.

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Glioblastoma is the most malignant brain tumor among adults. Despite multimodality treatment, it remains incurable, mainly because of its extensive heterogeneity and infiltration in the brain parenchyma. Recent evidence indicates dysregulation of the expression of the Promyelocytic Leukemia Protein (PML) in primary Glioblastoma samples.

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Tumors are complex, dynamic, and adaptive biological systems characterized by high heterogeneity at genetic, epigenetic, phenotypic, as well as tissue microenvironmental level. In this work, utilizing cellular automata methods, we focus on intrinsic heterogeneity with respect to cell cycle duration and explore whether and to what extent this heterogeneity affects cancer cell growth dynamics when cytotoxic treatment is applied. We assume that treatment acts on cancer cells specifically during mitosis and compare it with a (cell cycle-non-specific) cytotoxic treatment that acts randomly regardless of the cell cycle phase.

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