The Temozolomide-Doxorubicin paradox in Glioblastoma in vitro-in silico preclinical drug-screening.

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.

Mathematical Modeling Evaluates How Vaccinations Affected the Course of COVID-19 Disease Progression.

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.

Application of a Deep-Learning Technique to Non-Linear Images From Human Tissue Biopsies for Shedding New Light on Breast Cancer Diagnosis.

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.

Modeling How Heterogeneity in Cell Cycle Length Affects Cancer Cell Growth Dynamics in Response to Treatment.

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.

A Framework Linking Glycolytic Metabolic Capabilities and Tumor Dynamics.

Metabolic reprogramming is a hallmark of cancer. The main aim of this paper is to integrate a genome-scale metabolic description of tumor cells into a tumor growth model that accounts for the spatiotemporally heterogeneous tumor microenvironment, in order to study the effects of microscopic characteristics on tumor evolution. A lactate maximization metabolic strategy that allows near-optimal growth solution, while maximizing lactate secretion, is assumed.

The Importance of Neighborhood Scheme Selection in Agent-based Tumor Growth Modeling.

Modeling tumor growth has proven a very challenging problem, mainly due to the fact that tumors are highly complex systems that involve dynamic interactions spanning multiple scales both in time and space. The desire to describe interactions in various scales has given rise to modeling approaches that use both continuous and discrete variables, known as hybrid approaches. This work refers to a hybrid model on a 2D square lattice focusing on cell movement dynamics as they play an important role in tumor morphology, invasion and metastasis and are considered as indicators for the stage of malignancy used for early prognosis and effective treatment.

Employing in-vivo molecular imaging in simulating and validating tumor growth.

During the last decades, especially via the EU initiative related to the Virtual Physiological Human, significant progress has been made in advancing "in-silico" computational models to produce accurate and reliable tumor growth simulations. However, currently most attempts to validate the outcome of the models are either done in-vitro or ex-vivo after tumor resection. In this work, we incorporate information provided by fluorescence molecular tomography performed in-vivo into a mathematical model that describes tumor growth.