Adaptive data collection for intraindividual studies affected by adherence.

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.

Glycosylation status of serum in inflammatory arthritis in response to anti-TNF treatment.

OBJECTIVE: Glycosylation is the most common post-translational modification and is altered in disease. The typical glycosylation change in patients with inflammatory arthritis (IA) is a decrease in galactosylation levels on IgG. The aim of this study is to evaluate the effect of anti-TNF therapy on whole serum glycosylation from IA patients and determine whether these alterations in the glycome change upon treatment of the disease. METHODS: Serum samples were collected from 54 IA patients before treatment and at 1 and 12 months after commencing anti-TNF therapy. N-linked glycans from whole serum samples were analysed using a high-throughput hydrophilic interaction liquid chromatography-based method. RESULTS: Glycosylation on the serum proteins of IA patients changed significantly with anti-TNF treatment. We observed an increase in galactosylated glycans from IgG, also an increase in core-fucosylated biantennary galactosylated glycans and a decrease in sialylated triantennary glycans with and without outer arm fucose. This increase in galactosylated IgG glycans suggests a reversing of the N-glycome towards normal healthy profiles. These changes are strongly correlated with decreasing CRP, suggesting a link between glycosylation changes and decreases in inflammatory processes. CONCLUSION: Glycosylation changes in the serum of IA patients on anti-TNF therapy are strongly associated with a decrease in inflammatory processes and reflect the effect of anti-TNF on the immune system.

Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution.

BACKGROUND: Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates. To this end, the serum glycome has been proposed as a potential source of biomarkers for different types of cancers.High-throughput hydrophilic interaction liquid chromatography (HILIC) technology for glycan analysis allows for the detailed quantification of the glycan content in human serum. However, the experimental data from this analysis is compositional by nature. Compositional data are subject to a constant-sum constraint, which restricts the sample space to a simplex. Statistical analysis of glycan chromatography datasets should account for their unusual mathematical properties.As the volume of glycan HILIC data being produced increases, there is a considerable need for a framework to support appropriate statistical analysis. Proposed here is a methodology for feature selection in compositional data. The principal objective is to provide a template for the analysis of glycan chromatography data that may be used to identify potential glycan biomarkers. RESULTS: A greedy search algorithm, based on the generalized Dirichlet distribution, is carried out over the feature space to search for the set of "grouping variables" that best discriminate between known group structures in the data, modelling the compositional variables using beta distributions. The algorithm is applied to two glycan chromatography datasets. Statistical classification methods are used to test the ability of the selected features to differentiate between known groups in the data. Two well-known methods are used for comparison: correlation-based feature selection (CFS) and recursive partitioning (rpart). CFS is a feature selection method, while recursive partitioning is a learning tree algorithm that has been used for feature selection in the past. CONCLUSIONS: The proposed feature selection method performs well for both glycan chromatography datasets. It is computationally slower, but results in a lower misclassification rate and a higher sensitivity rate than both correlation-based feature selection and the classification tree method.

Novel glycan biomarkers for the detection of lung cancer.

Lung cancer has a poor prognosis and a 5-year survival rate of 15%. Therefore, early detection is vital. Diagnostic testing of serum for cancer-associated biomarkers is a noninvasive detection method. Glycosylation is the most frequent post-translational modification of proteins and it has been shown to be altered in cancer. In this paper, high-throughput HILIC technology was applied to serum samples from 100 lung cancer patients, alongside 84 age-matched controls and significant alterations in N-linked glycosylation were identified. Increases were detected in glycans containing Sialyl Lewis X, monoantennary glycans, highly sialylated glycans and decreases were observed in core-fucosylated biantennary glycans, with some being detectable as early as in Stage I. The N-linked glycan profile of haptoglobin demonstrated similar alterations to those elucidated in the total serum glycome. The most significantly altered HILIC peak in lung cancer samples includes predominantly disialylated and tri- and tetra-antennary glycans. This potential disease marker is significantly increased across all disease groups compared to controls and a strong disease effect is visible even after the effect of smoking is accounted for. The combination of all glyco-biomarkers had the highest sensitivity and specificity. This study identifies candidates for further study as potential biomarkers for the disease.