The mucosal immune system at the gastrointestinal barrier

The immune system faces a considerable challenge in its efforts to maintain tissue homeostasis in the intestinal mucosa. It is constantly confronted with a large array of antigens, and has to prevent the dissemination and proliferation of potentially harmful agents while sparing the vital structures of the intestine from immune-mediated destruction. Complex interactions between the highly adapted effector cells and mechanisms of the innate and adaptive immune system generally prevent the luminal microflora from penetrating the intestinal mucosa and from spreading systemically. Non-haematopoietic cells critically contribute to the maintenance of local tissue homeostasis in an antigen-rich environment by producing protective factors (e.g. production of mucus by goblet cells, or secretion of microbicidal defensins by Paneth cells) and also through interactions with the adaptive and innate immune system (such as the production of chemotactic factors that lead to the selective recruitment of immune cell subsets). The complexity of the regulatory mechanisms that control the local immune response to luminal antigens is also reflected in the observation that mutations in immunologically relevant genes often lead to the development of uncontrolled inflammatory reactions in the microbially colonized intestine of experimental animals.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models

Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

TNM staging of midgut and hindgut (neuro) endocrine tumors: a consensus proposal including a grading system

Criteria for the staging and grading of neuroendocrine tumors (NETs) of midgut and hindgut origin were established at the second Consensus Conference in Frascati (Rome) organized by the European Neuroendocrine Tumor Society (ENETS). The proposed tumor-node-metastasis (TNM) classifications are based on the recently published ENETS Guidelines for the Diagnosis and Treatment of gastroenteropancreatic NETs and follow our previous proposal for foregut tumors. The new TNM classifications for NETs of the ileum, appendix, colon, and rectum, and the grading system were designed, discussed, and consensually approved by all conference participants. These proposals need to be validated and are meant to help clinicians in the stratification, treatment and follow-up of patients.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Innate and adaptive immunity in host-microbiota mutualism

Healthy individuals live in peaceful co-existence with an immense load of intestinal bacteria. This symbiosis is advantageous for both the host and the bacteria. For the host it provides access to otherwise undigestible nutrients and colonization resistance against pathogens. In return the bacteria receive an excellent nutrient habitat. The mucosal immune adaptations to the presence of this commensal intestinal microflora are manifold. Although bacterial colonization has clear systemic consequences, such as maturation of the immune system, it is striking that the mutualistic adaptive (T and B cells) and innate immune responses are precisely compartmentalized to the mucosal immune system. Here we summarize the mechanisms of mucosal immune compartmentalization and its importance for a healthy host-microbiota mutualism.

[How the bovine viral diarrhea virus outwits the immune system]

The interaction of bovine viral diarrhea virus (BVD virus) with its host has several unique features, most notably the capacity to infect its host either transiently or persistently. The transient infection stimulates an antiviral immune reaction similar to that seen in other transient viral infections. In contrast, being associated with immunotolerance specific for the infecting BVD viral strain, the persistent infection differs fundamentally from other persistent infections like those caused by lentiviruses. Whereas the latter are characterized by complex viral evasion of the host's adaptive immune response by mechanisms such as antigenic drift and interference with presentation of T cell epitopes, BVD virus avoids the immune response altogether by inducing both humoral and cellular immune tolerance. This is made possible by invasion of the fetus at an early stage of development. In addition to adaptive immunity, BVD virus also manipulates key elements of the host's innate immune response. The non-cytopathic biotype of BVD virus, which is capable of persistently infecting its host, fails to induce type I interferon. In addition, persistently infected cells are resistant to the induction of apoptosis by double-stranded RNA and do not produce interferon when treated with this pathogen-associated molecular pattern (PAMP) that signals viral infection. Moreover, when treated with interferon, cells persistently infected with non-cytopathic BVD virus do not clear the virus. Surprisingly, however, despite this lack of effect on persistent infection, interferon readily induces an antiviral state in these cells, as shown by the protection against infection by unrelated viruses. Overall, BVD virus manipulates the host's interferon defense in a manner that optimises its chances of maintaining the persistent infection as well as decreasing the risks that heterologous viral infections may carry for the host. Thus, since not all potential host cells are infected in animals persistently infected with BVD virus, heterologous viruses replicating in cells uninfected with BVD virus will still trigger production of interferon. Interferon produced by such cells will curtail the replication of heterologous viruses only, be that in cells already infected with BVD virus, or in cells in which the heterologous virus may replicate alone. From an evolutionary viewpoint, this strategy clearly enhances the chances of transmission of BVD virus to new hosts, as it attenuates the negative effects that a global immunosuppression would have on the survival of persistently infected animals.

A Novel Implantable Hearing System with Direct Acoustic Cochlear Stimulation

A new implantable hearing system, the direct acoustic cochlear stimulator (DACS) is presented. This system is based on the principle of a power-driven stapes prosthesis and intended for the treatment of severe mixed hearing loss due to advanced otosclerosis. It consists of an implantable electromagnetic transducer, which transfers acoustic energy directly to the inner ear, and an audio processor worn externally behind the implanted ear. The device is implanted using a specially developed retromeatal microsurgical approach. After removal of the stapes, a conventional stapes prosthesis is attached to the transducer and placed in the oval window to allow direct acoustical coupling to the perilymph of the inner ear. In order to restore the natural sound transmission of the ossicular chain, a second stapes prosthesis is placed in parallel to the first one into the oval window and attached to the patient's own incus, as in a conventional stapedectomy. Four patients were implanted with an investigational DACS device. The hearing threshold of the implanted ears before implantation ranged from 78 to 101 dB (air conduction, pure tone average, 0.5-4 kHz) with air-bone gaps of 33-44 dB in the same frequency range. Postoperatively, substantial improvements in sound field thresholds, speech intelligibility as well as in the subjective assessment of everyday situations were found in all patients. Two years after the implantations, monosyllabic word recognition scores in quiet at 75 dB improved by 45-100 percent points when using the DACS. Furthermore, hearing thresholds were already improved by the second stapes prosthesis alone by 14-28 dB (pure tone average 0.5-4 kHz, DACS switched off). No device-related serious medical complications occurred and all patients have continued to use their device on a daily basis for over 2 years. Copyright (c) 2008 S. Karger AG, Basel.