Epidemiology of childhood acute leukemias in marginalized populations of the central-south region of Mexico: results from a population-based registry.

Introduction: Acute leukemias (AL) are the main types of cancer in children worldwide. In Mexico, they represent one of the main causes of death in children under 20 years of age. Most of the studies on the incidence of AL in Mexico have been developed in the urban context of Greater Mexico City and no previous studies have been conducted in the central-south of the country through a population-based study. The aim of the present work was to identify the general and specific incidence rates of pediatric AL in three states of the south-central region of Mexico considered as some of the marginalized populations of Mexico (Puebla, Tlaxcala, and Oaxaca). Methods: A population-based study was conducted. Children aged less than 20 years, resident in these states, and newly diagnosed with AL in public/private hospitals during the period 2021-2022 were identified. Crude incidence rates (cIR), standardized incidence rates (ASIRw), and incidence rates by state subregions (ASIRsr) were calculated. Rates were calculated using the direct and indirect method and reported per million children under 20 years of age. In addition, specific rates were calculated by age group, sex, leukemia subtype, and immunophenotype. Results: A total of 388 cases with AL were registered. In the three states, the ASIRw for AL was 51.5 cases per million (0-14 years); in Puebla, it was 53.2, Tlaxcala 54.7, and Oaxaca de 47.7. In the age group between 0-19 years, the ASIRw were 44.3, 46.4, 48.2, and 49.6, in Puebla, Tlaxcala, and Oaxaca, respectively. B-cell acute lymphoblastic leukemia was the most common subtype across the three states. Conclusion: The incidence of childhood AL in the central-south region of Mexico is within the range of rates reported in other populations of Latin American origin. Two incidence peaks were identified for lymphoblastic and myeloid leukemias. In addition, differences in the incidence of the disease were observed among state subregions which could be attributed to social factors linked to the ethnic origin of the inhabitants. Nonetheless, this hypothesis requires further investigation.

Gene Regulatory Network Dynamical Logical Models for Plant Development.

Mathematical and computational approaches that integrate and model the concerted action of multiple genetic and nongenetic components holding highly nonlinear interactions are fundamental for the study of developmental processes. Among these, gene regulatory network (GRN) dynamical models are very useful to understand how diverse types of regulatory constraints restrict the multigene expression patterns that characterize different cell fates. In this chapter we present a hands-on approach to model GRN dynamics, taking as a working example a well-curated and experimentally grounded GRN developmental module proposed by our group: the flower organ specification gene regulatory network (FOS-GRN). We demonstrate how to build and analyze a GRN model according to the following steps: (1) integration of molecular genetic data and formulation of logical rules specifying the dynamic behavior of each gene; (2) determination of steady states (attractors) corresponding to each cell type; (3) validation of the GRN model; and (4) extension of the deterministic model with the inclusion of stochasticity in order to model cell-state transitions dependent on noise due to fluctuations of the involved gen products. The methodologies explained here in detail can be applied to any other developmental module.

Medical Systems Biology.

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.

Modeling Procedures.

Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.

Case Studies.

The aim of this chapter is to illustrate the modeling procedures discussed in the previous chapter via three well-chosen examples.