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Record W4406081162 · doi:10.1021/acs.analchem.4c05018

Comprehensive, Quantitative Analysis of SRM 1950: the NIST Human Plasma Reference Material

2025· article· en· W4406081162 on OpenAlexafffund
Rupasri Mandal, Jiamin Zheng, Lun Zhang, Eponine Oler, Marcia LeVatte, Mark Berjanskii, Matthias Lipfert, Jun Han, Christoph H. Borchers, David S. Wishart

Bibliographic record

VenueAnalytical Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsMcGill UniversityJewish General HospitalUniversity of VictoriaUniversity of Alberta
FundersGenome AlbertaCanada Foundation for Innovation
KeywordsChemistryMetaboliteMetabolomicsChromatographyMetabolomeNISTTandem mass spectrometryMass spectrometryBiochemistry

Abstract

fetched live from OpenAlex

Many analytical methods have been developed for performing targeted metabolomics. By combining multiple analytical techniques, comprehensive coverage of the metabolome can be achieved. We combined multiple analytical techniques to comprehensively and quantitatively characterize the widely studied NIST human plasma reference material, SRM 1950. Our goal was to provide a large, well-validated list of confident metabolite concentration values (i.e., benchmarks) to assist the metabolomics community in its calibration and comparison efforts. We used four analytical platforms: high-resolution NMR spectroscopy, direct injection tandem MS (DI-MS/MS), liquid chromatography tandem MS (LC-MS/MS), and inductively coupled plasma MS (ICP-MS). Eight validated analytical assays were run, yielding accurate quantitative measurements for 728 unique metabolites or metabolite species. Through computer-aided literature mining, we identified another 330 unique metabolites previously quantified in SRM 1950. We compared NIST-certified values along with literature-derived concentrations/ranges to the metabolite concentrations measured by our four platforms and eight assays. From these assays/platforms, we generated a list of high-confidence concentration values of 1058 metabolites or metabolite species in SRM 1950 including data for 60 amino acids/related compounds, 48 bile acids, 72 amines/sugars/alcohols, 21 metals, 8 catecholamines, 11 vitamins, 92 organic acids, 40 fatty acids/steroids/nucleobases/indole derivatives, 5 polyfluorinated compounds, 7 carotenoids, 39 acylcarnitines, 76 oxylipins, 13 sterols, and 566 lipids/lipid species. This data set represents the most complete quantitative characterization of SRM 1950. An online database (SRM1950-DB) containing 1058 plasma metabolites/metabolite species in SRM 1950, their structures, HMDB IDs, mass, chemical class, concentrations, references, and reliability is freely available at https://srm1950-data.wishartlab.com.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.324
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2025
Admission routes2
Has abstractyes

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