A Nuclear Magnetic Resonance (NMR)- and Mass Spectrometry (MS)-Based Saturation Kinetics Model of a Bryophyllum pinnatum Decoction as a Treatment for Kidney Stones
Bibliographic record
Abstract
Bryophyllum pinnatum (BP) is a medicinal plant used to treat many conditions when taken as a leaf juice, leaves in capsules, as an ethanolic extract, and as herbal tea. These preparations have been chemically analyzed except for decoctions derived from boiled green leaves. In preparation for a clinical trial to validate BP tea as a treatment for kidney stones, we used NMR and MS analyses to characterize the saturation kinetics of the release of metabolites. During boiling of the leaves, (a) the pH decreased to 4.8 within 14 min and then stabilized; (b) regarding organic acids, citric and malic acid were released with maximum release time (tmax) = 35 min; (c) for glycoflavonoids, quercetin 3-O-α-L-arabinopyranosyl-(1 → 2)-α-L-rhamnopyranoside (Q-3O-ArRh), myricetin 3-O-α-L-arabinopyranosyl-(1 → 2)-α-L-rhamnopyranoside (M-3O-ArRh), kappinatoside, myricitrin, and quercitrin were released with tmax = 5–10 min; and (d) the total phenolic content (TPC) and the total antioxidant capacity (TAC) reached a tmax at 55 min and 61 min, respectively. In summary, 24 g of leaves boiled in 250 mL of water for 61 min ensures a maximal release of key water-soluble metabolites, including organic acids and flavonoids. These metabolites are beneficial for treating kidney stones because they target oxidative stress and inflammation and inhibit stone formation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".