Reproducibility of serum IgE, Ara h2 skin prick testing and fraction of exhaled nitric oxide for predicting clinical peanut allergy in children

Background: Ara h2 sIgE serum levels improve the diagnostic accuracy for predicting peanut allergy, but the use of Ara h2 purified protein as a skin prick test (SPT), has not been substantially evaluated. The fraction of exhaled nitric oxide (FeNO) shows promise as a novel biomarker of peanut allergy. Reproducibility of these measures has not been determined. The aim was to assess the accuracy and reproducibility (over a time-period of at least 12 months) of SPT to Ara h2 in comparison with four predictors of clinical peanut allergy (Peanut SPT, Ara h2 specific Immunoglobulin E (sIgE), Peanut sIgE and FeNO). Methods: Twenty-seven children were recruited in a follow-up of a prospective cohort of fifty-six children at least 12 months after an open-labelled peanut food challenge. Their repeat assessment involved a questionnaire, SPT to peanut and Ara h2 purified protein, FeNO and sIgE to peanut and Ara h2 measurements. Results: Ara h2 SPT was no worse in accuracy when compared with peanut SPT, FeNO, Ara h2 sIgE and peanut sIgE (AUC 0.908 compared with 0.887, 0.889, 0.935 and 0.804 respectively) for predicting allergic reaction at previous food challenge. SPT for peanut and Ara h2 demonstrated limited reproducibility (ICC = 0.51 and 0.44); while FeNO demonstrated good reproducibility (ICC = 0.73) and sIgE for peanut and Ara h2 were highly reproducible (ICC = 0.81 and 0.85). Conclusions: In this population, Ara h2 SPT was no worse in accuracy when compared with current testing for the evaluation of clinical peanut allergy, but had—like peanut SPT—poor reproducibility. FeNO, peanut sIgE and Ara h2 sIgE were consistently reproducible despite an interval of at least 12 months between the repeated measurements

STAT3 gain-of-function mutations connect leukemia with autoimmune disease by pathological NKG2Dhi CD8+T cell dysregulation and accumulation

The association between cancer and autoimmune disease is unexplained, exemplified by T cell large granular lymphocytic leukemia (T-LGL) where gain-of-function (GOF) somatic STAT3 mutations correlate with co -exist-ing autoimmunity. To investigate whether these mutations are the cause or consequence of CD8+ T cell clonal expansions and autoimmunity, we analyzed patients and mice with germline STAT3 GOF mutations. STAT3 GOF mutations drove the accumulation of effector CD8+ T cell clones highly expressing NKG2D, the receptor for stress-induced MHC-class-I-related molecules. This subset also expressed genes for granzymes, perforin, interferon-y, and Ccl5/Rantes and required NKG2D and the IL-15/IL-2 receptor IL2RB for maximal accumula-tion. Leukocyte-restricted STAT3 GOF was sufficient and CD8+ T cells were essential for lethal pathology in mice. These results demonstrate that STAT3 GOF mutations cause effector CD8+ T cell oligoclonal accumu-lation and that these rogue cells contribute to autoimmune pathology, supporting the hypothesis that somatic mutations in leukemia/lymphoma driver genes contribute to autoimmune disease.Peer reviewe

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

Causal Effects of Language on the Exchange of Social Support in an Online Community

The provision of social support is a common function of many online communities, but a full understanding of the causal effect of emotion language on the provision of support requires experimental study. The frequency of positive- and negative-emotion words in simulated posts requesting emotional support was manipulated and presented to a sample of college students (N = 442) who were randomly assigned to read one of four simulated posts. Participants completed measures of the original poster\u27s (OP\u27s) distress, and they provided a response to the simulated post. These responses were subjected to a computerized text analysis, and their overall effectiveness was rated by two independent judges. Fewer positive-emotion and more negative-emotion words in the simulated post led to perceptions that the OP was distressed and unable to cope. Participant-generated responses to the post were highest in positive-emotion words when the simulated post was high in positive-emotion words, but low in negative-emotion words. Finally, simulated posts that were low in positive-emotion words received responses that were judged to be more effective than did simulated posts that were high in positive-emotion words. These findings have implications for understanding the role of emotion language on the exchange of online social support