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https://www.selleckchem.com/products/apg-2449.html It also possesses great potential for various target molecules determination at the single-particle level in the future. The development of quantitative metabolomics approaches for future standardized and translational applications has become increasingly important. Data-independent targeted quantitative metabolomics (DITQM) is a newly proposed method providing ion pair information on 1324 metabolites. However, the quantification of more than 1000 metabolites in large sample sizes has still not been implemented. In this study, on the basis of the DITQM concept, scheduled multiple reaction monitoring (MRM) methods for both high-abundant and low-abundant metabolites were established to broaden the quantification coverage, and an open-source program "Quanter_1.0" was coded to facilitate efficient data handling. Our results demonstrated that 1015 metabolites in human plasma met the quantitative requirements and could be relatively determined in an effective manner. The method was then applied to a large-scale sample study of lung cancer consisting of three distinct analytical batches. It was obvious that data quality that originated from quantitative metabolomics was improved, with substantially lower intra- and inter-batch data variation, resulting in a more effective multivariate statistical model. Finally, 26 potential biomarkers of lung cancer were discovered. Collectively, our approach provides a promising tool for quantitative metabolomics research involving large-scale sample sizes and clinical application. Hydrogen sulfide (H2S) is an important endogenous gasotransmitter and has been implicated with a variety of biological processes. The development of an efficient method for monitor H2S fluctuations in biological systems is of great significance to understand its roles in physiological and pathological conditions. In this work, two red-emitting fluorescent probes SNARF-SSPy and SNARF-SeSPy for H2S detection wit

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