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Jeffrey M Wilson, Rung-chi Li, Emily C McGowan Division of Allergy and Immunology, University of Virginia, Charlottesville, VA, USACorrespondence: Jeffrey M WilsonDivision of Allergy & Clinical Immunology, University of Virginia, Charlottesville, VA 22908-1355, USATel +1 (434) 924-5917Fax +1 (434) 924-5779Email jmw2gc@virginia.eduAbstract: Food allergy is often understood as an IgE-mediated hypersensitivity, characterized by allergic symptoms which occur “immediately” after the ingestion of a relevant food allergen. Increasingly, however, other food-related immune-mediated disorders are recognized in which symptoms can have a delayed onset and IgE does not play a central role. One of the described examples of the latter is eosinophilic esophagitis (EoE) – a disease defined pathologically by local eosinophilic inflammation in the esophagus in the setting of symptoms of esophageal dysfunction. The evidence that EoE is a food-mediated allergic disease includes i) almost all patients respond to an elemental diet and many respond to a diet in which dairy, wheat, eggs and/or soy are eliminated, ii) the presence of food-specific IgE and Th2 cells are consistent with a loss of tolerance to trigger foods and iii) many EoE patients have concomitant IgE-mediated food allergy and other allergic co-morbidities. This narrative review focuses on the hypothesis that EoE is a form of chronic food allergy. The goal is to describe similarities and differences in EoE and IgE-mediated food allergy, and to consider ways that these two increasingly common forms of food allergy are related to each other.Keywords: food allergy, eosinophilic esophagitis, IgE, IgG4, Th2, Treg, barrier hypothesis

© 2019 Elsevier B.V. Chronic diseases often cause several medical complications. This paper aims to predict multiple complications among patients with a chronic disease. The literature uses single-task learning algorithms to predict complications independently and assumes no correlation among complications of chronic diseases. We propose two methods (independent prediction of complications with single-task learning and concurrent prediction of complications with multi-task learning) and show that medical complications of chronic diseases can be correlated. We use a case study and compare the performance of these two methods by predicting complications of hypertrophic cardiomyopathy on 106 predictors in 1078 electronic medical records from April 2009-April 2017, inclusive. The methods are implemented using logistic regression, artificial neural networks, decision trees, and support vector machines. The results show multi-task learning with logistic regression improves the performance of predictions in terms of both discrimination and calibration.

© 2018 This paper aims at time-series prediction of disease counts when the trend changes unexpectedly. There is a body of literature in time-series analysis that focuses on improving the prediction when a change in the trend is observed. Following this line of literature, a method was recently proposed that outperforms the previous proposals. To utilize this method for prediction of disease counts, the present study employs a commonly used method called local estimators for estimation near boundaries. This provides two advantages for disease count prediction. First, the prior method is limited to only smooth trend changes. However, the current study using local estimators also encounters abrupt changes. Taking both smooth and abrupt changes into account is important in predicting disease counts, because, for variety of different reasons, such as vaccination programs, the disease counts may encounter abrupt changes. Second, the current study, despite the previous approach, can also model uncertainty. The parameter uncertainty plays a significant role when predicting disease counts. Compared to current literature, the proposed method outperformed to predict counts for 12 diseases in Nevada. The paper demonstrates that the proposed methods improved the prediction Q-Score in comparison to both outbreak and structural change methods available in literature. Although this method has been specifically demonstrated for prediction of disease counts, it can be generalized for different applications. With the recent utilization of analytics techniques and the increased use of time-series analysis in information systems, the suggested method provides implications for the literature of information systems when predicting with structural trend changes in data.

In all vertebrates studied to date, CO₂ excretion depends on the enzyme carbonic anhydrase (CA) that catalyses the rapid conversion of HCO₃− to CO₂ at the gas-exchange organs. The largest pool of CA is present within red blood cells (RBC) and, in some vertebrates, plasma-accessible CA (paCA) isoforms participate in CO₂ excretion. However, teleost fishes typically do not have paCA at the gills and CO₂ excretion is reliant entirely on RBC CA; a strategy that is not possible in icefishes. As the result of a natural knockout, Antarctic icefishes (Channichthyidae) are the only known vertebrates that do not express haemoglobin (Hb) as adults, and largely lack RBC in the circulation (haematocrit

© 2018 Elsevier B.V. Background: The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares the performance of these classification algorithms to identify patients who are at risk of developing T2D in short, medium and long terms. In addition, the list of predictor variables important for prediction for T2D progression is provided. Methods: This study uses 10,911 records generated in 36 clinics from the 15th of November 2008–15th of November 2016. Syntactic minority oversampling and random under sampling were used to create a balanced dataset. The performance of Neural Networks, Support Vector Machines, Decision Tress and Logistic Regression to identify patients developing T2D in short, medium and long terms was compared. The measures were Area Under Curve, Sensitivity, Specificity, Matthew correlation coefficient and Mean Calibration Error. Through importance analysis and information fusion techniques the predictors of developing T2D were identified for short, medium and long-term risk analysis. Results: The findings show that the performance of analytics techniques depends on both period and purpose of prediction whether the prediction is to identify people who will not develop T2D or to determine at risk patients. Oversampling as opposed to under sampling improved performance. 16 predictors and their importance to determine patients at risk of T2D in short, medium and long terms were identified. Conclusions: This study provides guidelines for an automated system to prompt patients for screening. Several predictors are reportable by patients, others can be examined by physicians or ordered for further lab examination, which offers a potential reduction of the burden placed upon the clinical settings.

BackgroundCystic fibrosis (CF) is a chronic, life-limiting disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene leading to abnormal airway surface ion transport, chronic lung infections, inflammation and eventual respiratory failure. With the exception of the small-molecule potentiator, ivacaftor (Kalydeco®, Vertex Pharmaceuticals, Boston, MA, USA), which is suitable for a small proportion of patients, there are no licensed therapies targeting the basic defect. The UK Cystic Fibrosis Gene Therapy Consortium has taken a cationic lipid-mediatedCFTRgene therapy formulation through preclinical and clinical development.ObjectiveTo determine clinical efficacy of the formulation delivered to the airways over a period of 1 year in patients with CF.DesignThis was a randomised, double-blind, placebo-controlled Phase IIb trial of theCFTRgene–liposome complex pGM169/GL67A. Randomisation was performed via InForm™ version 4.6 (Phase Forward Incorporated, Oracle, CA, USA) and was 1 : 1, except for patients in the mechanistic subgroups (2 : 1). Allocation was blinded by masking nebuliser chambers.SettingsData were collected in the clinical and scientific sites and entered onto a trial-specific InForm, version 4.6 database.ParticipantsPatients with CF aged ≥ 12 years with forced expiratory volume in the first second (FEV1) between 50% and 90% predicted and any combination ofCFTRmutations. The per-protocol group (≥ 9 doses) consisted of 54 patients receiving placebo (62 randomised) and 62 patients receiving gene therapy (78 randomised).InterventionsSubjects received 5 ml of nebulised pGM169/G67A (active) or 0.9% saline (placebo) at 28 (±5)-day intervals over 1 year.Main outcome measuresThe primary end point was the relative change in percentage predicted FEV1over the 12-month period. A number of secondary clinical outcomes were assessed alongside safety measures: other spirometric values; lung clearance index (LCI) assessed by multibreath washout; structural disease on computed tomography (CT) scan; the Cystic Fibrosis Questionnaire – Revised (CFQ-R), a validated quality-of-life questionnaire; exercise capacity and monitoring; systemic and sputum inflammatory markers; and adverse events (AEs). A mechanistic study was performed in a subgroup in whom transgene deoxyribonucleic acid (DNA) and messenger ribonucleic acid (mRNA) was measured alongside nasal and lower airway potential difference.ResultsThere was a significant (p = 0.046) treatment effect (TE) of 3.7% [95% confidence interval (CI) 0.1% to 7.3%] in the primary end point at 12 months and in secondary end points, including forced vital capacity (FVC) (p = 0.031) and CT gas trapping (p = 0.048). Other outcomes, although not reaching statistical significance, favoured active treatment. Effects were noted by 1 month and were irrespective of sex, age orCFTRmutation class. Subjects with a more severe baseline FEV1had a FEV1TE of 6.4% (95% CI 0.8% to 12.1%) and greater changes in many other secondary outcomes. However, the more mildly affected group also demonstrated benefits, particularly in small airway disease markers such as LCI. The active group showed a significantly (p = 0.032) greater bronchial chloride secretory response. No difference in treatment-attributable AEs was seen between the placebo and active groups.ConclusionsMonthly application of the pGM169/GL67A gene therapy formulation was associated with an improvement in lung function, other clinically relevant parameters and bronchial CFTR function, compared with placebo.LimitationsAlthough encouraging, the improvement in FEV1was modest and was not accompanied by detectable improvement in patients’ quality of life.Future workFuture work will focus on attempts to increase efficacy by increasing dose or frequency, the coadministration of a CFTR potentiator, or the use of modified viral vectors capable of repeated administration.Trial registrationClinicalTrials.gov NCT01621867.FundingThis project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a Medical Research Council and National Institute for Health Research partnership.

Results: WPI and RPI showed a significant difference between T max for essential amino acids (EAA: RPI 87 ± 7 min, WPI 67 ± 4 min, p=0.03), non-essential amino acids (NEA: RPI 97 ± 4 min, WPI 71 ± 5 min, p

Objective: The purpose of this study was to create a global medical earnings index, called the Medical MAC Index, to enable a comparison of what medical specialists earn in the countries included in the study.Design: The study gathered data on the earnings of specialist anaesthetists employed in state hospitals with five years’ work experience, across 49 developed and developing countries.Setting and subjects: The earnings of anaesthetists were deemed to be indicative of all medical specialities because anaesthesia is one of the largest specialities, and most state hospitals do not distinguish between medical specialities from an earnings perspective. It is likely that a specialist with five years’ work experience would have reached the higher echelons of state salary scales, but it is unlikely that he or she would have moved into a management role yet.Outcome measures: To calculate a Medical MAC Index for a specific country, the net earnings of anaesthetists in the countries included in the study were converted into USA dollars, and adjusted using Xpatulator’s purchasing power parity model, and then compared to the net earnings of an anaesthetist in that specific country. The specific country was the baseline. Countries above the line pay more, and those below the line, less.Results: The Medical MAC Index for South Africa showed that medical specialists employed by the state in South Africa earn more than they would in most developing countries, but their earnings lag behind those of many developed countries.Conclusion: South Africa could become more competitive if tax incentives were used to manipulate the data.