Listen folks, untargeted metabolomics, it’s a big deal, a powerful tool in studying tumor mechanisms, discovering metabolic markers, you name it. But let me tell you, it’s not easy. The data analysis, it’s a challenge, from extracting raw data to identifying differential metabolites. There are so many tools, so many processes, and they all give different results. It’s a mess, believe me.

But we’ve got a solution. We’re building a platform, a great platform, easy to operate, and it gives repeatable results. We’re using the R language, the LAMP architecture, top-notch stuff. We’ve even built an open-source analysis software, openNAU, it’s fantastic. It does everything, from extracting raw mass data to quality control. We’ve also got a reference metabolomics database, based on public databases.

This platform, it’s a complete analysis system for untargeted metabolomics. It’s got a template interface for adding and updating the analysis process. It’s so simple, folks. You can do complex analyses with simple human-computer interactions. And the best part? You can download the source code from https://github.com/zjuRong/openNAU. It’s all there, folks. We’re making untargeted metabolomics great again.

Aims: Folks, we’ve got the Alcohol Use Disorders Identification Test (AUDIT), a fantastic tool used all over the world. But, let me tell you, not many countries have their own versions, and Russia just recently validated theirs. We’re here to show you how we made a Russian-specific AUDIT (RUS-AUDIT). Methods: We translated the AUDIT into Russian, made it fit the Russian context, and tested it out, all according to WHO rules. We had three pilot phases with 134 patients from primary healthcare and 33 from specialized alcohol treatment facilities. We made changes based on the results and discussions. Results: After three pilot phases, we’ve got the RUS-AUDIT, a paper-and-pencil interview for healthcare professionals. We had some issues with the second test item, so we made a special card to help with the assessment. Preliminary scores showed that over a third of women (34.2%) and about half of men (50.9%) from healthcare facilities are at risk. Conclusions: The RUS-AUDIT is a great tool for interviewers and patients. The high number of patients at risk shows the need for validation and Russia-specific cut-off scores, considering their specific drinking patterns. It’s going to be huge!