The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow—from sample collection to data analysis—should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.

We assessed 19,000 scientific introductions to measure the level of undisclosed use of ChatGPT in scientific papers published in 2023 and early 2024. We applied a “stylistics” approach that has previously been shown to be effective at differentiating AI-generated text from human-written text in a variety of venues. Ten different MDPI journals were selected for this study, and the rate of use of undisclosed AI writing in these journals was fairly consistent across the journals. We estimate that ChatGPT was used for writing or significant editing in about 1 to 3% of the introductions tested. This analysis is the first systematic study of detecting undisclosed ChatGPT in published manuscripts in cases where obvious indicators, such as phrases like “regenerate response”, are not present. The work demonstrates that generative AI is not polluting mainstream journals to any appreciable extent and that the overwhelming majority of scientists remain hesitant to embrace this tool for late-stage writing and editing.

Sebum lipids, accessible via groomed latent fingerprints, may be a valuable, underappreciated sample source for future biomarker research. Sampling sebum lipids from the skin is painless for patients, efficient for researchers, and has already demonstrated the potential to contain disease biomarkers. However, before sebum sampling can be implemented in routine studies, more information is needed regarding sampling reproducibility and variability. This information will enable researchers to choose the best practices for sebum-based studies. Herein, we use our recently established workflow for the collection and analysis of groomed fingerprints to assess the reproducibility of lipid profiles obtained via mass spectrometry. Using 180 fingerprint samples collected from 30 participants, we also assess lipid changes according to biological sex and anatomical grooming region (cheek, neck, and forehead) via supervised and unsupervised classification. The results demonstrate that this sampling protocol achieves satisfactory reproducibility, and negligible differences exist between male and female groomed fingerprint lipids. Moreover, the anatomical grooming region can impact the fingerprint lipid profile: cheek- and forehead-groomed fingerprints are more similar to one another than either collection site is to neck-groomed fingerprints. This information will inform future sebum-based biomarker investigations, enabling researchers to collect meaningful lipidomic datasets from groomed fingerprint samples.