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Diabetes is a silent illness that is endangering public health in society. Diabetes is a chronic disease affecting millions of people worldwide, and understanding the underlying mechanisms of glucose homeostasis is crucial for managing this condition. Diabetes is a significant public health issue due to the early morbidity, mortality, shortened life expectancy, and financial and other expenses to the patient, their careers, and the health care system. In this study, we propose a mathematical model consisting of β − cells, insulin, glucose, and growth hormone that incorporates the fractional operator. Using the Lyapunov function, we treated a global stability analysis and investigated the impact of a new wave of dynamical transmission on the equilibrium points of the second derivative. With the Lipschitz criteria and linear growth, the exact singular solution for the proposed model has been determined. Furthermore, we present a detailed analysis of infections, and numerical simulations are conducted using the Mittag-Leffler Kernel mathematical framework to illustrate the theoretical conclusions for various orders of the fractional derivative. Controllability and observability of the linear system are treated for close loop design to check the relation between the glucose and insulin systems. Overall, our results provide a comprehensive understanding of glucose homeostasis and its underlying mechanisms, contributing to the development of effective diabetes management strategies. The proposed model and mathematical framework offer a valuable tool for investigating complex systems and phenomena, with applications beyond the field of diabetes research and helpful to designing the closed loop for the glucose–insulin system. [ABSTRACT FROM AUTHOR]

Introduction: Growth acceleration during postnatal growth only occurs during puberty as a physiological event and during catch-up growth mediated by growth-promoting therapies in growth disorders. Here we report on novel observations of skeletal symptoms during treatment with erdafitinib, a tyrosine kinase inhibitor (TKI) prescribed on the basis of a compassionate-use program., Methods: Analysis of anthropometric, biochemical, clinical, and radiographic data of patients with CNS tumors who revealed an unanticipated growth spurt with initiation of therapy with erdafitinib was performed retrospectively., Results: Linear growth acceleration was independent of sex steroids and IGF1 levels, which is especially remarkable in the context of heavily pretreated pediatric neuro-oncology patients with severe growth impairment before initiation of therapy. Growth acceleration was accompanied by a distinct widening of the growth plate and enhanced metaphyseal mineralization shortly after the start of TKI therapy., Conclusions: While targeted therapies including TKIs have become an essential part of adult cancer treatment, applications in children are still limited. Off-target effects specific to the pediatric population have been observed in various organ systems; however, knowledge about the effect of TKIs on the growing skeleton is scarce. Treatment with erdafitinib inhibits FGFR3-mediated effects and thus represents a very logical hypothetical framework of growth factor and sex steroid-independent growth acceleration., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Abstract: A maximum pseudo-likelihood approach has previously been developed for fitting pairwise interaction models to patterns generated by growth–interaction processes that are sampled at fixed time points. This approach is now extended, not only by estimating the parameters of the process through time, but also by employing least squares estimation since likelihood based approaches are much more computationally demanding. First, simple stochastic models are used to demonstrate that least squares methods are as powerful as likelihood-based approaches, as well as being mathematically and computationally simpler. The algorithm generates simulations of the deterministic growth–interaction and stochastic immigration–death process, and through these the parameter estimates are determined. Logistic and linear growth are then combined with (symmetric) disc-interaction and (asymmetric) area-interaction processes, and between them these generate a variety of mark–point spatial structures. A robustness study shows that the procedure works well in that the presence, structure and strength of a growth–interaction process can be determined even when an incorrectly presumed model is employed. Thus, the technique is likely to prove to be very useful in general practical applications where the underlying process generating mechanism is almost certain to be unknown. Finally, the procedure is applied to the analysis of a new Swedish pine forest data set for which tree location and diameter at breast height were recorded in 1985, 1990 and 1996. [Copyright &y& Elsevier]