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Hashimoto's thyroiditis (HT) is a prevalent autoimmune disorder marked by chronic inflammation of the thyroid gland, predominantly affecting children and adolescents. In a previous study, we developed a "maximal" mathematical model of thyroid physiology to simulate the complex interactions within the thyroid gland. The present research introduces an enhanced version of the "maximal" model, integrating the pathophysiological impacts of HT. It specifically models the adverse effects of thyroid peroxidase (TPO) and thyroglobulin (Tg) antibodies (TPOAb and TgAb) on TPO, Tg, sodium iodide symporter (NIS), albeit indirectly, and thyroid volume. Additionally, we present a new "minimal" model offering a streamlined interpretation of thyroid physiology and pathophysiology, designed for faster computational analysis while maintaining essential physiological interactions. Both models were fitted against longitudinal clinical data from patients with HT, assessing the concentrations of Thyroid Stimulating Hormone (TSH), Thyroxine (T4), and thyroid volume over 36 months, in both untreated patients and those receiving levothyroxine (LT4) treatment. The adaptation of the models to data shows that both of them accurately reproduce the available observed clinical outcomes, with the "maximal" model providing more detailed physiological insights but requiring extensive data and longer computation times. In contrast, the "minimal" model, despite exhibiting less realistic TSH oscillations, offers rapid parameter estimation and may be more feasible in clinical settings. These models hold significant potential as tools for detailed study and management of HT, enabling simulations of disease progression and therapeutic responses, thus paving the way for personalized treatment strategies. [ABSTRACT FROM AUTHOR]

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients. [ABSTRACT FROM AUTHOR]

Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management. [ABSTRACT FROM AUTHOR]

The dynamics of pedestrian crowds during exceptional tragic events are very complex depending on a series of human behaviors resulting from combinations of basic interaction principles and self-organization. The Alert–Panic–Control (APC) model is one of the mathematical models in the literature for representing such complicated processes, mainly focusing on psychologists' points of view (i.e., emotion contagion). This work proposes a Hybrid APC (HAPC) model including new processes, such as the effect of resonance, the victims caused by people in state of panic, new interactions between populations based on imitation and emotional contagion phenomena and the ability to simulate multiple disaster situations. Results from simulated scenarios showed that in the first 5 min 54.45% of population move towards a state of alert, 13.82% enter the control state and 31.73% pass to the state of panic, highlighting that individuals respond to a terrible incident very quickly, right away after it occurs. [ABSTRACT FROM AUTHOR]