Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1H NMR data.

INTRODUCTION: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price. OBJECTIVE: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high-field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. METHODS: We compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop proton (1H) NMR and high-field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils. RESULTS: We demonstrate that PLS-DA models trained on low-field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high-field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.

Arthritis knowledge: a survey of health professional students.

BACKGROUND: Recent reports indicate that disease-specific educational modules have resulted in improved knowledge among resident physicians. These findings suggest that graduate level health professional students may also benefit from similar strategies focused on arthritis, the leading cause of disability in the United States. The purpose of this pilot study was to examine arthritis knowledge among healthcare professional students whose current curriculums include didactic arthritis content. METHODS: A survey of arthritis knowledge was developed to examine general arthritis knowledge and the epidemiological impact of arthritis among healthcare professional students. The survey was administered to 164 first, second, and third year students enrolled in graduate occupational and physical therapy programs. RESULTS: The findings of the current study address risk factors recognition and arthritis knowledge. Risk factors recognition -- 94% identified age; 68% identified gender; 94% identified genetics; 90% identified joint injuries; 73% identified obesity as a risk factor; and 55% identified occupation. More than 50% of the students identified the six risk factors most likely to lead to arthritis. ARTHRITIS KNOWLEDGE: prevalence, disability impact, and cost -- 100% correctly identified arthritis symptoms; 26% of the students surveyed reported arthritis as the third leading cause of disability; 85% of the survey respondents reported that in the US women have higher rates of arthritis; and 31% knew the health care cost of arthritis in the US. CONCLUSIONS: All students recognized the symptoms of arthritis and most recognized its individual risk factors, but few exhibited knowledge of the financial and disability burden associated with arthritis. These findings suggested that despite didactic arthritis content in both the occupational and physical therapy programs arthritis risk factors recognition and arthritis knowledge were mixed. Further research examining arthritis knowledge among a broader range of health professional students may be important to determine if disease-specific educational modules are needed in all graduate health professional programs.