HOLISTIC APPROACH TO THE IMMUNOBIOLOGY OF AGING (VIEW ON THE TURN OF MILLENIUM).

Among many functions of the body affected by the complex process of aging, the immune system, primarily due to declining of thymic function, the ancient and conserved evolutionary process, undergoes complex remodelling in the second part of life, with recapitulation of inversely evolutionary pattern of the immune system. In approaching the complex analysis of age-associated derangement of the immune system, homeostasis, and its clinical consequences, classical monoclonal lymphoproliferative syndrome (CLL) accompanied by a myriad of cellular and humoral defects, has been selected as appropriate and useful model for studies of impact T-cell and B-cell defects on appearance, evolution of clinical manifestations and outcome of CLL syndrome. Therefore imbalance in cascade secretion of a number of Th-1 (pro-inflammatory cytokines) and/or Th-2 (anti-inflammatory cytokines) in CLL patients with their pleiotropy, redundancy, synergistic and antagonistic activity and parallelism can cause variety of clinical manifestations as recurrent infections, systemic inflammation/sepsis, immunodeficiency, autoimmune disorder, indolent antiself malignancy, and/or other diverse secondary tumours.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

AIM: To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. BACKGROUND: Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. METHODS: The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. FINDINGS: A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

A view at the future--a dynamical, protocol-based and computationally intensive approach in cardiovascular risk assessment.

Knowledge of cardiovascular risk factors is increasing. At the same time, risk estimation becomes more and more difficult. The need for a more comprehensive, but more individually based approach is evident. To achieve this aim, we propose a systems biology approach in cardiovascular risk assessment. This means that a large amount of health data, describing many aspects of the health-status of patients, is collected and computed and the results are compared with existing knowledge. Finally, a clinical model is created, which can be the first step in ongoing research protocol, aimed at assessing cardiovascular risk. By using this approach, all potentially relevant risk factors can be identified on a small sample. Moreover, risk groups can be more specifically defined, based on the "natural" clustering of data, according to their predictive load. We tested this possibility on an example of hyperhomocysteinemia which is a well-known complex cardiovascular risk factor.

Prediction of influenza vaccination outcome by neural networks and logistic regression.

The major challenge in influenza vaccination is to predict vaccine efficacy. The purpose of this study was to design a model to enable successful prediction of the outcome of influenza vaccination based on real historical medical data. A non-linear neural network approach was used, and its performance compared to logistic regression. The three neural network algorithms were tested: multilayer perceptron, radial basis and probabilistic in conjunction with parameter optimization and regularization techniques in order to create an influenza vaccination model that could be used for prediction purposes in the medical practice of primary health care physicians, where the vaccine is usually dispensed. The selection of input variables was based on a model of the vaccine strain which has frequently been changed and on which a poor influenza vaccine response is expected. The performance of models was measured by the average hit rate of negative and positive vaccine outcome. In order to test the generalization ability of the models, a 10-fold cross-validation procedure revealed that the model obtained by multilayer perceptron produced the highest average hit rate among neural network algorithms, and also outperformed the logistic regression model with regard to sensitivity and specificity. Sensitivity analysis was performed on the best model and the importance of input variables was discussed. Further research should focus on improving the performance of the model by combining neural networks with other intelligent methods in this field.