The Effectiveness of Bioresonance Method on Human Health

Introduction: Bioresonance is a holistic physical method that can be used diagnostically and therapeutically to treat various diseases. Bioresonance uses the electromagnetic waves it receives from the patient. This biophysical method of treatment alters the energy field of the affected organism, increasing the effectiveness of the autoimmune system, thus improving the patient’s overall health. Methods: A quasi-experimental design was used to examine whether bioresonance had an effect on the symptoms of the patients. Statistical data processing involves a study of various parameters such as age, gender, studies, occupation, intolerance or illness as well as the existence and balancing of allergic charges. The study included 311 patients from doctors and bio-coordinating laboratories in Athens, Thessaloniki, Volos and Xanthi. The sample of the study included both men (120 subjects - 38.58%) and women (191 subjects – 61.42%), aged from 2 to 76 years old. The age group with the largest number of patients included those aged from 31 to 45. Results: The patients under treatment came forward with symptoms mostly nasal (61.09%), followed by eye, respiratory, cutaneous, and gastrointestinal symptoms. Most of the patients (90%) observe no symptoms at all or show significant improvement of their symptoms after a period of 12 months of bioresonance treatment. These results show that bioresonance intervention had a significant effect on the improvement of symptoms and this improvement is even obvious 12 months after the intervention; p<.01. Conclusion: We believe that the forthcoming results contribute to a better understanding of the effect of bioresonance on the human body and its affect in preventing and restoring health issues.

Binary Regression Models with Log-Link in the Cohort Studies

Regression models have been used to control confounding in food borne cohort studies, logistic regression has been commonly used due to easy converge. However, logistic regression provide estimates for OR only when RR estimate is lower than 10%, an unlikely situation in food borne outbreaks. Recent developments have resolved the binary model convergence problems applying log link. Food items significant in the univariable analysis were included for the multivariable analysis of two recent Finnish norovirus outbreaks. We used both log and logistic regression models in R and Bayesian model in Winbugs by SPSS and R. The log-link model could be used to identify the vehicle in the two norovirus outbreak datasets. Convergence problems were solved using Bayesian modelling. Binary model applying log link provided accurate and useful estimates of RR estimating the true risk, a suitable method of choice for multivariable analysis of outbreak cohort studies.

Cryptococcosis, A Risk for Immunocompromised and Immunocompetent Individuals

The genus Cryptococcus includes at least 37 different species, of which, two are important human pathogens: Cryptococcus neoformans and Cryptococcus gattii. These fungi are opportunistic pathogens and etiologic agents of cryptococcosis disease in humans and animals. A variety of virulence factors interfere with the establishment of cryptococcal infection is usually acquired via inhalation of environmental basidiospores or desiccated yeasts. Cryptococcosis has gained medical importance over the last decade due to the AIDS pandemic, and become an emerging pathogen of immunocompetent individuals, especially in children. This disease in humans may involve every tissue, including cutaneous and pulmonary sites, but the most serious manifestation is central nervous system involvement with meningoencephalitis. In this review, we briefly described the taxonomy, the fungus biology, epidemiology and clinical manifestations of cryptococcosis in immunocompetent and immunocompromised individuals.

Workshop Report: Evaluation of Epidemiological Data Consistency for Application in Regulatory Risk Assessment

Epidemiological study results have a key role in the assessment of health risks associated with exposures to chemicals and pollutants, and often serve as the basis for the development of regulatory limits for environmental and occupational health. A key uncertainty in the application of epidemiological study results in risk assessments stems from variability in defining and operationalizing the concept of consistency of findings across studies, with assessments of consistency often a controversial component of risk assessments. Although assessment of consistency of findings across a diverse collection of epidemiological studies is central to evaluating that body of evidence for supporting causal inferences, the variability in definition and formal evaluation methods strongly suggest the need for constructive approaches to consistently and transparently evaluate data consistency. In response to the need to improve approaches to assessing consistency in epidemiological study results, the Johns Hopkins Risk Sciences and Public Policy Institute organized a workshop held in Baltimore, Maryland in September 2010 to identify and discuss key methodological issues, and to develop recommendations for qualitative and quantitative approaches to addressing those issues. A multi-disciplinary approach was utilized for the workshop, involving invited experts from a variety of fields, and the invited participants were drawn from academia, industry, government, and the public interest sectors. This report provides a summary of selected epidemiology methodological issues discussed by the workshop participants and provides the workshop’s key findings and recommendations for future approaches to addressing this issue.