Modeling rejection immunity

Background: Transplantation is often the only way to treat a number of diseases leading to organ failure. To overcome rejection towards the transplanted organ (graft), immunosuppression therapies are used, which have considerable side-effects and expose patients to opportunistic infections. The development of a model to complement the physician’s experience in specifying therapeutic regimens is therefore desirable. The present work proposes an Ordinary Differential Equations model accounting for immune cell proliferation in response to the sudden entry of graft antigens, through different activation mechanisms. The model considers the effect of a single immunosuppressive medication ( e.g. cyclosporine), subject to first-order linear kinetics and acting by modifying, in a saturable concentration-dependent fashion, the proliferation coefficient. The latter has been determined experimentally. All other model parameter values have been set so as to reproduce reported state variable time-courses, and to maintain consistency with one another and with the experimentally derived proliferation coefficient. Results: The proposed model substantially simplifies the chain of events potentially leading to organ rejection. It is however able to simulate quantitatively the time course of graft-related antigen and competent immunoreactive cell populations, showing the long-term alternative outcomes of rejection, tolerance or tolerance at a reduced functional tissue mass. In particular, the model shows that it may be difficult to attain tolerance at full tissue mass with acceptably low doses of a single immunosuppressant, in accord with clinical experience. Conclusions: The introduced model is mathematically consistent with known physiology and can reproduce variations in immune status and allograft survival after transplantation. The model can be adapted to represent different therapeutic schemes and may offer useful indications for the optimization of therapy protocols in the transplanted patien

Modeling rejection immunity

Background: Transplantation is often the only way to treat a number of diseases leading to organ failure. To overcome rejection towards the transplanted organ (graft), immunosuppression therapies are used, which have considerable side-effects and expose patients to opportunistic infections. The development of a model to complement the physician’s experience in specifying therapeutic regimens is therefore desirable. The present work proposes an Ordinary Differential Equations model accounting for immune cell proliferation in response to the sudden entry of graft antigens, through different activation mechanisms. The model considers the effect of a single immunosuppressive medication ( e.g. cyclosporine), subject to first-order linear kinetics and acting by modifying, in a saturable concentration-dependent fashion, the proliferation coefficient. The latter has been determined experimentally. All other model parameter values have been set so as to reproduce reported state variable time-courses, and to maintain consistency with one another and with the experimentally derived proliferation coefficient. Results: The proposed model substantially simplifies the chain of events potentially leading to organ rejection. It is however able to simulate quantitatively the time course of graft-related antigen and competent immunoreactive cell populations, showing the long-term alternative outcomes of rejection, tolerance or tolerance at a reduced functional tissue mass. In particular, the model shows that it may be difficult to attain tolerance at full tissue mass with acceptably low doses of a single immunosuppressant, in accord with clinical experience. Conclusions: The introduced model is mathematically consistent with known physiology and can reproduce variations in immune status and allograft survival after transplantation. The model can be adapted to represent different therapeutic schemes and may offer useful indications for the optimization of therapy protocols in the transplanted patien

A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space

Weaning is a period of marked physiological change. The introduction of solid foods and the changes in milk consumption are accompanied by significant gastrointestinal, immune, developmental, and microbial adaptations. Defining a reduced number of infections as the desired health benefit for infants around weaning, we identified in silico (i.e., by advanced public domain mining) infant gut microbes as potential deliverers of this benefit. We then investigated the requirements of these bacteria for exogenous metabolites as potential prebiotic feeds that were subsequently searched for in the natural product space

Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73)

In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time pro les and posterior identi cation of correlation between clusters and predictors. Subjects are rst clustered in groups according to a response variable temporal pro le, using a robust consensus-based strategy. To discover which of the remaining variables are associated with the resulting groups, a non-parametric hypothesis test is performed between groups at every time point, and then the results are aggregated according to the Fisher method. Our approach is tested through its application to the EarlyBird cohort database, which contains temporal variations of clinical, metabolic, and anthropometric pro les in a population of 150 children followed-up annually from age 5 to age 16. Our results show that our consensus-based method is able to overcome the problem of the approach-dependent results produced by current clustering algorithms, producing groups de ned according to Insulin Resistance (IR) and biological age (Tanner Score). Moreover, it provides meaningful biological results con rmed by hypothesis testing with most of the main clinical variables. These results position CClust as a valid alternative for the analysis of multivariate longitudinal data

Alcohol and Substance Use Disorders Diagnostic Criteria Changes and Innovations in ICD-11: An Overview

[Background] The new revision of the ICD came into effect on January 1st, 2022, and significant changes have been introduced in the section related to substance use disorders. [Method] In the present work we describe the new ICD-11 section “Disorders due to Substance Use and Addictive Behaviors” and outline the innovations in classification and diagnosis introduced, with a view to addressing the most important issues in terms of new opportunities for identifying and caring for people in need of treatment. [Results] The main innovations introduced in the ICD-11 chapter of interest are the expanded classes of psychoactive substances, the introduction of single episodes of substance use, the introduction of harmful patterns of substance use and severity qualifiers for substance intoxication. Furthermore, the new category “Disorders due to addictive behaviors” has been added, including “Gambling disorder” and the new diagnostic category “Gaming disorder”. [Conclusions] ICD-11 calls for renewed public health response and policies fostering the multi-professional and multidisciplinary management of alcohol and substance abuse treatment, giving to these forms of addiction new chances also towards the reaching of the UN 2030 Agenda Sustainable Development Goals

Alcohol, youth and sport: recommendation and good practice examples from the FYFA European project.

Focus on Youth Football and Alcohol (FYFA) is a European project (EC, 3rd Health Program, HP-PJ-2016) involving research institutions from Belgium, Finland, Italy, Poland, Slovenia and the UK. The Istituto Superiore di Sanità (ISS), was the project leader of Work Package 5: "Review of national policies and practice in six Member States related to alcohol, young people, sport, marketing and football." The aim of WP5 was to determine the status quo of the policies and practices to reduce heavy episodic drinking related to young people, alcohol and sport at national level. This work investigates knowledge, attitudes and perceptions of experts from sport settings and from the prevention area giving insights on the perceived obstacles and facilitators, whenever available, to promote strategies to reduce alcohol related harm in youth within sport contexts. The presented work describes laws, regulations and attitudes. Furthermore, the results help identifying areas requiring development, highlighting examples of good practices. It emerges that prevention of alcohol-related harm to youth is important within sport settings and should be a priority for all FYFA countries. Despite the presence of regulations, there is a low level of knowledge and enforcement at national level and in the sport contexts; and there is the need of cooperation across organizations to implement alcohol policies for youth within sport settings. More efforts and resources are needed to overcome the main obstacles for effective implementation of alcohol policies, such as regulations on advertising and sponsorship, and alcohol selling, serving and consumption for young players. It is necessary to implement information strategies, prevention initiatives, training programs and to support the dialogue between sporting and prevention settings.info:eu-repo/semantics/publishe

RESEARCH Open Access Modeling rejection immunity

available at the end of the article Background: Transplantation is often the only way to treat a number of diseases leading to organ failure. To overcome rejection towards the transplanted organ (graft), immunosuppression therapies are used, which have considerable side-effects and expose patients to opportunistic infections. The development of a model to complement the physician’s experience in specifying therapeutic regimens is therefore desirable. The present work proposes an Ordinary Differential Equations model accounting for immune cell proliferation in response to the sudden entry of graft antigens, through different activation mechanisms. The model considers the effect of a single immunosuppressive medication (e.g. cyclosporine), subject to first-order linear kinetics and acting by modifying, in a saturable concentration-dependent fashion, the proliferation coefficient. The latter has been determined experimentally. All other model parameter values have been set so as to reproduce reported state variable time-courses, and to maintain consistency with one another and with th

A closed-loop multi-level model of glucose homeostasis

<div><p>Background</p><p>The pathophysiologic processes underlying the regulation of glucose homeostasis are considerably complex at both cellular and systemic level. A comprehensive and structured specification for the several layers of abstraction of glucose metabolism is often elusive, an issue currently solvable with the hierarchical description provided by multi-level models. In this study we propose a multi-level closed-loop model of whole-body glucose homeostasis, coupled with the molecular specifications of the insulin signaling cascade in adipocytes, under the experimental conditions of normal glucose regulation and type 2 diabetes.</p><p>Methodology/Principal findings</p><p>The ordinary differential equations of the model, describing the dynamics of glucose and key regulatory hormones and their reciprocal interactions among gut, liver, muscle and adipose tissue, were designed for being embedded in a modular, hierarchical structure. The closed-loop model structure allowed self-sustained simulations to represent an ideal <i>in silico</i> subject that adjusts its own metabolism to the fasting and feeding states, depending on the hormonal context and invariant to circadian fluctuations. The cellular level of the model provided a seamless dynamic description of the molecular mechanisms downstream the insulin receptor in the adipocytes by accounting for variations in the surrounding metabolic context.</p><p>Conclusions/Significance</p><p>The combination of a multi-level and closed-loop modeling approach provided a fair dynamic description of the core determinants of glucose homeostasis at both cellular and systemic scales. This model architecture is intrinsically open to incorporate supplementary layers of specifications describing further individual components influencing glucose metabolism.</p></div