Differences in Metabolite Profiles of Dihydroberberine and Micellar Berberine in Caco-2 Cells and Humans—A Pilot Study
Bibliographic record
Abstract
, LMB) formulations. A pilot trial involving nine healthy volunteers was conducted over a 24 h period; blood samples were collected and subjected to Ultra High-Performance Liquid Chromatography-High Resolution Mass Spectrometry (UHPLC-HRMS) analyses to quantify the concentrations of berberine and its metabolites. Pharmacokinetic correlations indicated that berberrubine and thalifendine follow distinct metabolic pathways. Additionally, jatrorrhizine sulfate appeared to undergo metabolism differently compared to the other sulfated metabolites. Moreover, berberrubine glucuronide likely has a unique metabolic pathway distinct from other glucuronides. The human trial revealed significantly higher blood concentrations of berberine metabolites in participants of the DHB treatment group compared to the LMB treatment group-except for berberrubine glucuronide, which was only detected in the LMB treatment group. Similarly, results from in vitro investigations showed significant differences in berberine metabolite profiles between DHB and LMB. Dihydroberberine, dihydroxy-berberrubine/thalifendine and jatrorrhizine sulfate were detected in LMB-treated cells, but not in DHB-treated cells; thalifendine and jatrorrhizine-glucuronide were detected in DHB-treated cells only. While DHB treatment provided higher blood concentrations of berberine and most berberine metabolites, both in vitro (Caco-2 cells) and in vivo human studies showed that treatment with LMB resulted in a higher proportion of unmetabolized berberine compared to DHB. These findings suggest potential clinical implications that merit further investigation in future large-scale trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".