Phosphorus-specific, liquid chromatography inductively coupled plasma mass spectrometry for analysis of inositol phosphate and inositol pyrophosphate metabolism
Bibliographic record
Abstract
Inositol phosphate (InsP) and diphosphoinositol phosphate (PP-InsP) analysis in tissues is plagued by multiple difficulties of sensitivity, regioisomer resolution and the need for radiolabelling with metabolic precursors. We describe a liquid chromatography (LC) inductively coupled plasma (ICP) mass spectrometry (MS) method (LC-ICP-MS) that addresses all such issues and use LC-ICP-MS to analyse InsPs in avian tissues. The highly sensitive technique tolerates complex matrices and, by powerful chromatography, resolves in a single run multiple non-enantiomeric myo-inositol tetrakisphosphates, myo-inositol pentakisphosphates and all inositol hexakisphosphates, including myo-inositol 1,2,3,4,5,6-hexakisphosphate (phytate), known in nature. It also separates and quantifies diphospho myo-inositol pentakisphosphate (PP-InsP5) isomers from their biological precursors and from 1,5-bis-diphospho myo-inositol 2,3,4,6 tetrakisphosphate (1,5-[PP]2- InsP4). Gut tissue InsPs, belonging to a non-canonical, lipid-independent pathway, are shown to differ from phytate digestion products and to be responsive to diet.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".