Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer.

Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning.

Method for correction of experimental action spectrum using actual overlapping spectra of radiation sources.

Experimental dependency of the photosystem's response on the wavelength of exciting radiation, also known as action spectrum, may be substantially affected by the spectrum shape of this radiation. This is especially important in the case, when different radiation sources are used for the investigation of action spectrum. For instance, too wide emission peaks of radiation sources can blur the scopes of actual action spectrum and distort information about the properties of photosystem at certain wavelength regions.