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Record W4390543528 · doi:10.1080/14786419.2023.2299302

Rapid classification and identification of chemical compositions of Pu-zhi-hui-ling decoction by UHPLC-Q-Orbitrap HRMS

2024· article· en· W4390543528 on OpenAlex
Jia Shao, Yanxue Zheng, Yuanyuan Wang, Guohui Li, Jinxia Wei, Wenbo Cheng, Yubo Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNatural Product Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Analysis
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersNational Natural Science Foundation of China
KeywordsOrbitrapDecoctionChemical constituentsChemistryChromatographyMass spectrometryPinelliaTraditional medicineTraditional Chinese medicineMedicine

Abstract

fetched live from OpenAlex

Pu-zhi-hui-ling decoction (PZHLD) is a traditional Chinese medicine (TCM) formula for the treatment of Alzheimer's disease (AD), but its chemical composition has not been reported. In this study, we aimed to establish a mass spectrometry (MS) analysis method for rapid classification and identification of the chemical constituents in PZHLD. The sample was analysed by ultrahigh-performance liquid chromatography coupled to quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). The chemical constituents of PZHLD were identified based on accurate MS data, fragmentation characteristics of MS/MS, and reference information described in the literature. A total of 123 chemical constituents were identified. In addition, we summarised the fragmentation pathways of the chemical constituents in PZHLD. Our finding might lay the foundation for the further pharmacodynamic study and clinical application of PZHLD.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.412
Teacher spread0.337 · 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