BackgroundPulmonary imaging often identifies suspicious abnormalities resulting in supplementary diagnostic procedures. This study aims to investigate whether the metabolic fingerprint of plasma allows to discriminate between patients with lung inflammation and patients with lung cancer.MethodsMetabolic profiles of plasma from 347 controls, 269 cancer patients and 108 patients with inflammation were obtained by 1H-NMR spectroscopy. Models to discriminate between groups were trained by PLS-LDA. A test set was used for independent validation. A ROC curve was built to evaluate the diagnostic performance of potential biomarkers.ResultsSensitivity, specificity, PPV and NPV of PET-CT to diagnose cancer are 96, 23, 76 and 71%. Metabolic profiles differentiate between cancer and inflammation with a sensitivity of 89%, a specificity of 87% and a MCE of 12%. Removal of the glutamate metabolite results in an increase of MCE (38%) and a decrease of both sensitivity and specificity (62%), demonstrating the importance of glutamate for discrimination. At the cut-off point 0.31 on the ROC curve, the relative glutamate concentration discriminates between cancer and inflammation with a sensitivity of 85%, a specificity of 81%, and an AUC of 0.88. PPV and NPV are 92 and 69%. In PET-positive patients with a relative glutamate level ≤ 0.31 the sensitivity to diagnose cancer reaches 100% with a PPV of 94%. In PET-negative patients, a relative glutamate level > 0.31 increases the specificity of PET from 23% to 58% and results in a high NPV of 100%. In case of discrepancy between SUVmax and the glutamate concentration, lung cancer is missed in 19% of the cases.ConclusionThis study indicates that the 1H-NMR-derived relative plasma concentration of glutamate allows discrimination between lung cancer and lung inflammation. A glutamate level ≤ 0.31 in PET-positive patients corresponds to the diagnosis of lung cancer with a higher specificity and PPV than PET-CT. Glutamate levels > 0.31 in patients with PET negative lung lesions is likely to correspond with inflammation. Caution is needed for patients with conflicting SUVmax values and glutamate concentrations. Confirmation is needed in a prospective study with external validation and by another analytical technique such as HPLC-MS.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4755-1) contains supplementary material, which is available to authorized users.
Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.
Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by 18F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between 1H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (18F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUVmax), total body metabolic active tumor volumes (MATVWTB) and total body total lesion glycolysis (TLGWTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATVWTB and TLGWTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.
The current outbreak of COVID-19 is a major pandemic that has shaken up the entire world in a short time. South Africa has the highest number of COVID-19 cases in Africa and understanding the country’s disease trajectory is important for government policy makers to plan the optimal COVID-19 intervention strategy. The number of cases is highly correlated with the number of COVID-19 tests undertaking. Thus, current methods of understanding the COVID-19 transmission process in the country based only on the number of cases can be misleading. In light of this, we propose to estimate both the probability of positive cases per tests conducted (the positive testing rate) and the rate in which the positive testing rate changes over time (its derivative) using a flexible semi-parametric model.We applied the method to the observed positive testing rate in South Africa with data obtained from March 5th to September 2nd 2020. We found that the positive testing rate was declining from early March when the disease was first observed until early May where it kept on increasing. In the month of July 2020, the infection reached its peak then its started to decrease again indicating that the intervention strategy is effective. From mid August, 2020, the rate of change of the positive testing rate indicates that decline in the positive testing rate is slowing down, suggesting that a less effective intervention is currently implemented.
Our aim in this study is to develop predictive microbiome biomarkers for intestinal IgA levels. In this article, a operational taxonomic units(OTU)-specific (family-specific) and time-specific joint model is presented as a tool to model the association between OTU (or family) and biological response (measured by IgA level) taking into account the treatment group (Control or PAT) of the subjects. The model allows detecting OTUs (families) that are associated with the IgA; for some OTUs (families), the association is driven by the treatment while for others the association reflects the correlation between the OTUs (families) and IgA.The results of the analysis reveal that: (1) the observed diversity of S24-7 family can be used as a biomarker to classify samples according to treatment group for days 6 and 12; (2) the treatment effect induces the corrlelation between the S24-7 diversity and the IgA level at day 20; (3) The OTUs that are identified to be significantly differentially abundant (FDR level of 0.05) between the two treatment groups for days 12 and 20 are all part of the S24-7 family, although most of the differentially abundant ones at day 1 are from the Lactobacillaceae family; (4) only the Lachnospiraceae family diversity at day 6, and 20 can be used as predictive biomarker for the IgA level at day 20; (5) New.ReferenceOTU513, correlated with the IgA level at day 20, since day 12, belongs to the Lachnospiraceae family and all other OTUs among the top 10 significantly associated OTUs at day 20 are from the S24-7 family; (6) the observed alpha diversity at day 6 is significantly differentially abundant and can be used as predictive biomarker for IgA level at day 20.
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