TASK DEFINITION: In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.
PROBLEM: Context: Dipeptidyl peptidase-4 (DPP4) was recently proposed as a novel adipokine linked to insulin resistance (IR). As IR represents a cluster of disorders in hepatic and muscle cell insulin signalisation, we aimed to assess the possible correlation between fasting serum DPP4 activity, IR and liver enzymes in order to elucidate the question of hepatic contribution to serum DPP4 activity.', 'This cross-sectional study comprised 44 T1DM patients aged 18 to 65years. IR was estimated using the equation derived from euglycemic-hyperinsulinemic clamp studies-estimated glucose disposal rate (eGDR). DPP4 serum activity was determined spectrophotometrically as a rate of cleavage of 7-amino-4-methyl coumarin (AMC) from H-Gly-Pro-AMC. The patients were divided into two groups according to the mean value of fasting serum DPP4 activity (31.42U/L).', 'The group with lower fasting serum DPP4 activity had lower mean rate of liver biomarkers alanine aminotransferase (ALT) (p=0.001) and aspartate aminotransferase (AST) (p=0.002) while higher eGDR (p=0.003) compared to group with higher DPP4 activity. DPP4 activity showed positive correlation with AST (r=0.358, p=0.017) and ALT (r=0.364, p=0.015) while negative correlation with eGDR (r=-0.612, p<0.001). ALT remained positively associated with fasting serum DPP4 activity after controlling for age, gender, diabetes duration, the use of statins and antihypertensives (p=0.025).\Question: Is fasting serum dipeptidyl peptidase-4 activity independently associated with alanine aminotransferase in type 1 diabetic patients?

SOLUTION: Fasting serum DPP4 activity might be associated with hepatic IR in T1DM patients and a part of soluble DPP4 activity might be of a hepatic origin. Further study investigation is warranted to elucidate this topic.

PROBLEM: Context: Some subjects with suspected asthma and a negative exercise challenge test (ECT) demonstrate improved expiratory flow rates after administration of bronchodilators (BD) at the end of the ECT (unpublished observation). This study investigated whether this response predicts the presence of bronchial hyperreactivity (BHR).', 'The study population included 133 young adults (29.4% women) 21.1 +/- 4.2 years of age who underwent ECT and a methacholine challenge test (MCT). A receiver-operator-characteristic curve was used to calculate the optimal cutoff level of the response to BD as a predictor of BHR according to MCT.', 'Using a MCT cutoff level of PC(20) </= 4 mg/mL showed BHR in 12.8% of subjects. Failure to improve FEV(1) by 8% after BD administration predicted the absence of BHR with sensitivity, specificity, and positive and negative predictive values of 76.5%, 68.4%, 25.3%, and 95.5%, respectively. Avoiding MCT in subjects with less than 8% response to BD would have saved 62.5% of the MCTs and would have missed only four (3%) patients with BHR.\Question: Does response to bronchodilators after exercise challenge predict bronchial hyperreactivity?

SOLUTION: BD should be administered routinely after ECT, as the response may be used as a simple, inexpensive tool to predict BHR in young patients and may substantially reduce the number of unnecessary MCTs.

PROBLEM: Context: Mass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed "frequent itemset mining" can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks.', 'First a model is proposed that describes the origin of the observed peaks in a mass spectrum. For this purpose we use the classical correlative database search algorithm. Peaks that support a positive identification of the spectrum are termed explained peaks. Next, frequent itemset mining techniques are introduced to infer which unexplained peaks are associated in a spectrum. The method is validated on two types of experimental proteomic data. First, peptide mass fingerprint data is analyzed to explain the unassigned peaks in a full scan mass spectrum. Interestingly, a large numbers of experimental spectra reveals several highly frequent unexplained masses, and pattern mining on these frequent masses demonstrates that subsets of these peaks frequently co-occur. Further evaluation shows that several of these co-occurring peaks indeed have a known common origin, and other patterns are promising hypothesis generators for further analysis. Second, the proposed methodology is validated on tandem mass spectrometral data using a public spectral library, where associations within the mass differences of unassigned peaks and peptide modifications are explored. The investigation of the found patterns illustrates that meaningful patterns can be discovered that can be explained by features of the employed technology and found modifications.\Question: Does unravelling associations between unassigned mass spectrometry peak with frequent itemset mining techniques?

SOLUTION:
This simple approach offers opportunities to monitor accumulating unexplained mass spectrometry data for emerging new patterns, with possible applications for the development of mass exclusion lists, for the refinement of quality control strategies and for a further interpretation of unexplained spectral peaks in mass spectrometry and tandem mass spectrometry.