Recent research suggests that anti-bullying laws may be effective in reducing risk of bullying victimization among youth, but no research has determined whether these laws are also effective in reducing disparities in bullying. The aim of this paper was to evaluate the effectiveness of anti-bullying legislation in reducing disparities in sex- and weight-based bullying and cyberbullying victimization. Data on anti-bullying legislation were obtained from the U.S. Department of Education, which commissioned a systematic review of 16 key components of state laws in 2011. States were also categorized based on whether their legislation enumerated protected groups and, if so, which groups were enumerated. These policy variables from 28 states were linked to individual-level data on bullying and cyberbullying victimization from students in 9th through 12th grade participating in the 2011 Youth Risk Behavior Surveillance System study (N=79,577). Analyses were conducted in 2016. There was an absence of any kind of moderating effect of anti-bullying legislation on weight-based disparities in bullying and cyberbullying victimization. Only state laws with high compliance to Department of Education enumeration guidelines were associated with lower sex-based disparities in bullying victimization. Anti-bullying policies were not associated with lower weight-based disparities in bullying and cyberbullying victimization among youth, and only one form of policies (high compliance to Department of Education enumeration guidelines) was associated with lower sex-based disparities in bullying victimization. Results therefore suggest that anti-bullying legislation requires further refinement to protect youth who are vulnerable to bullying victimization.

Extracellular vesicles (EVs) are membrane‐bound particles secreted by cells, playing crucial roles in intercellular communication. The composition of EVs can undergo changes in response to stress and disease conditions, making them excellent biomarker candidates. However, extracting protein information from EVs can be challenging due to their low abundance in complex biofluids and copurification with contaminant proteins and particles. Techniques to enrich EVs have their strengths and limitations, without one being able to purify EVs to complete homogeneity. This can lead to compromised recovery rates and increased complexity, making data interpretation difficult. In this viewpoint article, we explore the concept that better characterization of EV composition, followed by quantification of EV proteins in complex samples, might be a more viable route for biomarker development. Mass spectrometers can provide reproducible deep coverage of the EV proteome, despite sample impurities. This paradigm shift presents opportunities to integrate advanced bioinformatics tools to refine the EV proteome landscape, identify novel biomarkers, and streamline validation processes in biomarker development. By focusing on leveraging technology rather than achieving absolute purity, this approach can transform current practices and open opportunities for robust biomarker discovery. Herein, we highlight not only such opportunities but also challenges to implement this concept. Summary: Extracellular vesicles (EVs) have enormous potential as biomarkers of diseases, as they can carry signatures of the cells they are derived from and the pathogenesis process. Biofluids, such as blood plasma, are highly complex and contain many components with physicochemical properties similar to those of EVs, making it challenging to obtain pure EV fractions. Challenges in obtaining pure preparations represent a main hurdle for studying EVs, and their components are potential biomarkers. This article explores the concept of studying EV proteins within complex samples, discussing opportunities and needs to move this field forward.

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.