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Abstract A method is proposed for the determination of some ketosteroids in human urine, including enzymatic hydrolysis using β-glucuronidase from E. coli followed by dispersive liquid-liquid microextraction, derivatization of analytes with hydroxylamine, and detection by reversed-phase ultra-HPLC–quadrupole time-of-flight mass spectrometry. Optimization of extraction and derivatization conditions of the studied compounds made it possible to find that the highest recoveries were achieved using an acetone–chloroform mixture as a dispersant and an extractant, and the completeness of the derivatization reaction was achieved by thermostating the sample at 70°C for 90 min. The proposed method has high sensitivity (limits of detection in the range of 0.1–0.25 ng/mL) and a wide linearity range.

In clinical practice, exhaled air analysis is a promising approach for diagnosing lung cancer due to a possibility of rapid and noninvasive sampling. We analyzed the exhaled air of 80 patients with lung cancer to identify differences in the profiles of volatile organic compounds in patients with different histological types and localization of cancer using gas chromatography–mass spectrometry. Some volatile organic compounds and their ratios have been identified, statistically signficant differences in concentrations of which have been found in samples with different localization and histological type.

Exhaled breath analysis is an interesting and promising approach for the diagnostics of various diseases. Being non-invasive, convenient and simple, this approach has tremendous potential utility for further translation into clinical practice. In this study, gas chromatography-mass spectrometry (GC-MS) and quartz microbalance sensor-based "electronic nose" were applied for analysis of the exhaled breath of 40 lung cancer patients and 40 healthy individuals. It was found that the electronic nose was unable to distinguish the samples of different groups. However, the application of GC-MS allowed identifying statistically significant differences in compound peak areas and their ratios for investigated groups. Diagnostic models were created using random forest classifier based on peak areas and their ratios with the sensitivity and specificity of peak areas (ratios) of 85.7-96.5% (75.0-93.1%) and 73.3-85.1% (90.0-92.5%) on training data and 63.6-75.0% (72.7-100.0%) and 50.0-69.2% (76.9-84.6%) on test data, respectively. The exhaled breath samples of lung cancer patients and healthy volunteers could be distinguished by GC-MS with the use of individual compounds, but application of their ratios could help to determine specific differences between investigated groups and the level the influence of individual metabolism features alternating from one person to another as well as daily instrument reproducibility deviations. The electronic nose has to be significantly improved to apply it to lung cancer diagnostics of exhaled breath analysis and the influence of water vapour has to be lowered to increase the sensitivity of the sensors to detect lung cancer biomarkers.

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography–mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

Several new cyclopropylisonitriles were synthesized from the corresponding cyclopropylamines (41-69% yield) and utilized in Ugi four-component reactions to furnish 12 new dipeptides in 38-86% yield. The structures of four of these dipeptides were determined by X-ray crystallography.