Quantitative Evaluation of Non-basic Nitrogen-Containing Compounds in Petroleum-Derived Samples by Direct Injection ESI (-) Orbitrap MS.

The quantification of non-basic nitrogen-containing compounds (NCCs) in petroleum-derived samples has become a critical issue due to the undesirable effects of these compounds on the petroleum industry. In addition, there is a lack of analytical methods that allow the direct quantification of NCCs in these matrices. This paper provides strategies for obtaining quantitative information of NCCs in petroleum-derived samples using direct flow injection electrospray ionization (ESI) (-) Orbitrap mass spectrometry without fractionation steps. Benzocarbazole (BC) quantification was performed using the standard addition method. The method was validated, and all analytical parameters demonstrated satisfactory results in the matrix-mix. Paired Student's t-test exhibited the matrix effect (95% confidence level, p < 0.05). Limits of detection ranged from 2.94 to 14.91 µg L-1, and the limits of quantification ranged from 9.81 to 49.69 µg L-1. Intraday and interday accuracy and precision were not above 15%. Quantification of non-basic NCCs was carried out based on two approaches. In approach 1, the non-basic NCCs' total content in petroleum-derived samples was determined by the BC concentration and the total abundance correction. The method presented good performance with the average error of 21, 8.3, and 28% for crude oil, gas oil, and diesel samples, respectively. Approach 2 was based on the multiple linear regression model with regression significant at a 0.05 significance level within average relative errors of 16, 7.8, and 17% for the crude oil, gas oil, and diesel samples, respectively. Then, both approaches successfully predicted the quantification of non-basic NCCs by ESI direct flow injection.

LC-HRMS/MS-Based Metabolomics Approaches Applied to the Detection of Antifungal Compounds and a Metabolic Dynamic Assessment of Orchidaceae.

The liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach is a powerful technology for discovering novel biologically active molecules. In this study, we investigated the metabolic profiling of Orchidaceae species using LC-HRMS/MS data combined with chemometric methods and dereplication tools to discover antifungal compounds. We analyze twenty ethanolic plant extracts from Vanda and Cattleya (Orchidaceae) genera. Molecular networking and chemometric methods were used to discriminate ions that differentiate healthy and fungal-infected plant samples. Fifty-three metabolites were rapidly annotated through spectral library matching and in silico fragmentation tools. The metabolomic profiling showed a large production of polyphenols, including flavonoids, phenolic acids, chromones, stilbenoids, and tannins, which varied in relative abundance across species. Considering the presence and abundance of metabolites in both groups of samples, we can infer that these constituents are associated with biochemical responses to microbial attacks. In addition, we evaluated the metabolic dynamic through the synthesis of stilbenoids in fungal-infected plants. The tricin derivative flavonoid- and the loliolide terpenoidfound only in healthy plant samples, are promising antifungal metabolites. LC-HRMS/MS, combined with state-of-the-art tools, proved to be a rapid and reliable technique for fingerprinting medicinal plants and discovering new hits and leads.

Experience in resistance training does not prevent reduction in muscle strength evoked by passive static stretching.

This study examined whether passive static stretching reduces the maximum muscle strength achieved by different body segments in untrained and resistance-trained subjects. Twenty adult men were assigned to 1 of the following groups: untrained (UT, N = 9) and resistance-trained (RT, N = 11) groups. The subjects performed six 1 repetition maximum (1RM) load tests of the following exercises: horizontal bench press, lat pull-downs, bicep curls, and 45° leg press. The results achieved in the last two 1RM tests were used for statistical analyses. A passive static stretching program was incorporated before the sixth 1RM test. The body fat content was significantly higher in the UT group compared with the RT group (p < 0.0001). Moreover, the RT group showed significantly higher proportion of lean body mass compared with the UT group (p < 0.0001). Maximum muscle strength on all 4 exercises was significantly reduced in both groups after stretching (p < 0.01). Furthermore, the magnitude of muscle strength reduction was similar for the UT and the RT groups. The exception was for barbell curls, in which the muscle strength depression was significantly higher in the UT group compared with the RT group (p < 0.0001). In conclusion, the passive static stretching program was detrimental to upper- and lower-body maximal muscle strength performance in several body segments. The negative effects of stretching were similar for subjects participating in resistance training regimens.