Quantification of Cytokinins Using High-Resolution Accurate-Mass Orbitrap Mass Spectrometry and Parallel Reaction Monitoring (PRM)
Bibliographic record
Abstract
Cytokinins (CKs) are adenine derivatives that act as phytohormones. These signaling molecules control plant cell division and differentiation, organ growth, and senescence, and they orchestrate plant interactions with biotic and abiotic environments. While CKs are predominately recognized as plant-based substances, CKs have been found across different domains of life, including microorganisms, insects, mammals, and humans. In plants, CKs act at trace, often low femtomolar concentrations; therefore, sensitive and precise analytical techniques are required to accurately detect and quantify them from complex biological matrices. Here, we report the first comprehensive CK quantification method using a QExactive Orbitrap mass spectrometer in high-resolution with a parallel reaction monitoring (PRM)-based approach. The current method progresses upon multiple reaction monitoring (MRM) methods, previously used for CK profiling on triple quadrupole mass spectrometers. This method offers improved mass accuracy and the complete product ion mass spectra (MS/MS) for compound determination with increased specificity, and sensitivity comparable with triple quadrupole instruments. The presented PRM approach was successfully applied to quantify 32 CKs in several biological samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".