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Hypersensitivity of the immune system is caused by elevated immunoglobulin E (IgE) levels in the serum, in response to a discrete allergen leading to allergic reactions. IgE-mediated inflammation is regulated by the cascade of defense related signaling molecules including interleukin-6 (IL-6) that plays pivotal role in the survival and maturation of mast cells during an allergic reaction. IL-6 mediated defense responses are tightly regulated by Suppressor of Cytokine Signaling 3 (SOCS3), an inhibitory molecules of Janus Kinase-Signal Transducers and Activators of Transcription (JAK-STAT) signaling, in a negative feedback mechanism. The given study focuses on the assessment of crosstalk between SOCS3 and IL-6 to unravel the molecular significance of SOCS3 and IL-6 in the diagnosis and prognosis of allergy. The expression study of SOCS3 through real-time PCR analysis revealed, a 5.9 mean fold increase in SOCS3 expression in atopic cases in comparison to control cases. Moreover, IL-6 has, also, been found significantly enhanced in the serum level of atopic cases (26.4 pg/ml) as compared to control cases (3.686 pg/ml). Female population was found to be at a higher risk to develop atopic condition than male population as females exhibited higher expression of both SOCS3 and IL-6 than males. Furthermore, the polymorphic study of IL-6 promoter region (IL-6 174-G/C) in atopic population has reasserted the importance of SOCS3 and IL-6 in the diagnosis and prognosis of allergy. Expression of SOCS3 and IL-6 serum levels were found to be highly correlated. Therefore establishing the role of IL-6 (-174-G/C) polymorphism on the expression of SOCS3 and IL-6 in atopic cases. Notably, the study established SOCS3 and IL-6 as potential targets for the diagnosis/prognosis of allergy and for the development of reliable therapeutic strategies to control atopic conditions in the near future. [ABSTRACT FROM AUTHOR]

Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson’s disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results. [ABSTRACT FROM AUTHOR]

Background: Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts. Methods: Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event. Conclusions: We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings. [ABSTRACT FROM AUTHOR]

Background: In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends. Methodology: We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data. Conclusions: We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model. [ABSTRACT FROM AUTHOR]