An infant mouse model of brain damage in pneumococcal meningitis

Bacterial meningitis due to Streptococcus pneumoniae is associated with an significant mortality rate and persisting neurologic sequelae including sensory-motor deficits, seizures, and impairments of learning and memory. The histomorphological correlate of these sequelae is a pattern of brain damage characterized by necrotic tissue damage in the cerebral cortex and apoptosis of neurons in the hippocampal dentate gyrus. Different animal models of pneumococcal meningitis have been developed to study the pathogenesis of the disease. To date, the infant rat model is unique in mimicking both forms of brain damage documented in the human disease. In the present study, we established an infant mouse model of pneumococcal meningitis. Eleven-days-old C57BL/6 (n = 299), CD1 (n = 42) and BALB/c (n = 14) mice were infected by intracisternal injection of live Streptococcus pneumoniae. Sixteen hours after infection, all mice developed meningitis as documented by positive bacterial cultures of the cerebrospinal fluid. Sixty percent of infected C57BL/6 mice survived more than 40 h after infection (50% of CD1, 0% of BALB/c). Histological evaluations of brain sections revealed apoptosis in the dentate gyrus of the hippocampus in 27% of infected C57BL/6 and in 5% of infected CD1 mice. Apoptosis was confirmed by immunoassaying for active caspase-3 and by TUNEL staining. Other forms of brain damage were found exclusively in C57BL/6, i.e. caspase-3 independent (pyknotic) cell death in the dentate gyrus in 2% and cortical damage in 11% of infected mice. This model may prove useful for studies on the pathogenesis of brain injury in childhood bacterial meningitis.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

A Process Model of Complementarity and Substitution of Contractual and Relational Governance in IS Outsourcing

This paper develops a process model of how and why complementarity and substitution form over time between contractual and relational governance in the context of information systems outsourcing. Our analysis identifies four distinct process patterns that explain this formation as the outcome of interaction processes between key elements of both contractual and relational governance. These patterns unveil the dynamic nature of complementarity and substitution. In particular, we show that the relationship between contractual and relational governance oscillates between complementarity and substitution. Those oscillations are triggered mainly by three types of contextual events (goal fuzziness, goal conflict, and goal misalignment). Surprisingly, substitution of informal control did not occur as an immediate reaction to external events but emerged as a consequence of preceding complementarity. Thus, our study challenges the prevailing view of an either/or dichotomy of complementarity and substitution by showing that they are causally connected over time.

Traumatization and chronic pain: a further model of interaction.

Up to 80% of patients with severe posttraumatic stress disorder are suffering from "unexplained" chronic pain. Theories about the links between traumatization and chronic pain have become the subject of increased interest over the last several years. We will give a short summary about the existing interaction models that emphasize particularly psychological and behavioral aspects of this interaction. After a synopsis of the most important psychoneurobiological mechanisms of pain in the context of traumatization, we introduce the hypermnesia-hyperarousal model, which focuses on two psychoneurobiological aspects of the physiology of learning. This hypothesis provides an answer to the hitherto open question about the origin of pain persistence and pain sensitization following a traumatic event and also provides a straightforward explanatory model for educational purposes.

The role of learning-related dopamine signals in addiction vulnerability

Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signaling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical, and experimental accounts of phasic dopamine signaling, with implications for the role of learning-related dopamine signaling in addiction and related disorders. We describe the theoretical and behavioral characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction.

Improving residency training with the help of multisource feedback: Generation of a model of influencing factors

Background: Feedback is considered to be one of the most important drivers of learning. One form of structured feedback used in medical settings is multisource feedback (MSF). This feedback technique provides the opportunity to gain a differentiated view on a doctor’s performance from several perspectives using a questionnaire and a facilitating conversation, in which learning goals are formulated. While many studies have been conducted on the validity, reliability and feasibility of the instrument, little is known about the impact of factors that might influence the effects of MSF on clinical performance. Summary of Work: To study under which circumstances MSF is most effective, we performed a literature review on Google Scholar with focus on MSF and feedback in general. Main key-words were: MSF, multi-source-feedback, multi source feedback, and feedback each combined with influencing/ hindering/ facilitating factors, effective, effectiveness, doctors-intraining, and surgery. Summary of Results: Based on the literature, we developed a preliminary model of facilitating factors. This model includes five main factors influencing MSF: questionnaire, doctor-in-training, group of raters, facilitating supervisor, and facilitating conversation. Discussion and Conclusions: Especially the following points that might influence MSF have not yet been sufficiently studied: facilitating conversation with the supervisor, individual aspects of doctors-in-training, and the causal relations between influencing factors. Overall there are only very few studies focusing on the impact of MSF on actual and long-term performance. We developed a preliminary model of hindering and facilitating factors on MSF. Further studies are needed to better understand under which circumstances MSF is most effective. Take-home messages: The preliminary model might help to guide further studies on how to implement MSF to use it at its full potential.