Mathsmagic

  The Geometric Signatures of Malignancy: Why Negative Ollivier-Ricci Curvature Indicates Invasive Tumor Borders.   Abstract   The tumor microenvironment is a complex and heterogeneous system of interacting cells and for accurate characterization of its structure, advanced mathematical paradigms are needed. The emerging field of spatial omics enables molecular expression to be profiled in their natural geometric context and thus unravels complex biological tissue architectures. In this paper, the authors explore the theoretical and experimental explanation why negative Ollivier-Ricci curvature is a good mathematical predictor for invasive tumor borders. We show that the nature of the geometry of the graph reveals structural bottlenecks and boundaries between different microenvironmental domains intrinsically as negative curvature. Finally, we suggest a comprehensive methodological approach that combines discrete differential geometry and topological data analysi...

Topological Data Analysis and Geometric Graph Theory in Complex Networks: From Social Dynamics to Spatial Omics. Abstract Complex networks have gone through a radical change from just a few graph-theoretic representations of networks that consider only pairs of nodes to advanced geometric and higher-order topological representations. This paper introduces a detailed methodological approach to build a bridge between the classical structural network analysis and contemporary spatial biological systems. We investigate the development of network modeling by the use of dynamic influence matrices, spectral geometry, and persistent homology, introducing a mathematical hierarchy. In conclusion, we show how a mathematical framework such as topological data analysis (TDA) and geometric graph theory are needed to unlock the architectural complexity of biological tissues, especially in the fast evolving field of spatial omics. Introduction Traditionally, complex networks have been thought ...

The Global Mosaic of Modern Oncology The Global Mosaic of Modern Oncology: How Breakthrough Care Meets Real-World Practice A radical transformation is taking place in cancer treatment. For decades the focus of oncology research was only biological; finding the cell mutations, finding drugs that target them and finding ways to make the tumour go away. Although molecular advancements keep changing the course of prognosis, there is a broader reality that has come to light. Nowadays, the fight against cancer doesn't focus solely on cure or control. Regulatory speed, comorbid health conditions, stress from the diagnostic process, cardiovascular side effects, doctor and nurse education, and social determinants of health (SDOH) all influence a patient's journey. Global efforts from expedient drug approval in the Middle East to overhauling cancer training in Europe are changing the face of cancer care into a more unified, fair, and patient...

Dynamic Infinity Mapping Framework (DIMF): An Adaptive Oncology Dosing Reinforcement Learning Approach Author- Shrishti Rastogi Abstract Dynamic Infinity Mapping Framework (DIMF) is a new computational paradigm to manage the stochastic evolution of subpopulations of cancer cells under therapeutic pressure. DIMF combines Markov Decision Processes (MDP), Dynamic Graph Neural Networks (GNNs), and Reinforcement Learning (RL) to offer a powerful framework for optimising the dosage of multiple drugs adaptively. This report outlines the mathematical modelling of state transitions, algorithmic implementation of the RL agent and an empirical calibration using quantitative interaction and toxicity data from the large clinical trials. Our framework shows better ability to cross the balance line between therapeutic effect and total toxicity than static modelling methods. Introduction New challenges for modern oncology are the emergence of acquired resistan...