Berberine alleviates hyperglycemia by targeting hepatic glucokinase in diabetic db/db mice
Bibliographic record
Abstract
Berberine (BBR) is a widely used anti-diabetic agent, and liver glucokinase (GK) has been reported to be involved. However, the mechanisms of BBR in regulating GK are still unknown. Here, we found that BBR upregulated GK immunofluorescence expression in AML12 cells cultured in high glucose and increased glycogen content simultaneously. BBR improved hyperglycemia in db/db mice, and increased liver glucose-6-phosphate/glucose-1-phosphate (G-6-P/G-1-P) was found by analyzing metabolites (serum, liver, and feces) based on gas chromatography-mass spectrometry (GC-MS) metabolomics. Pharmacokinetics-pharmacodynamics (PK-PD) assessment revealed enriched BBR distribution in the liver, and liver G-6-P had the same trend as the concentration-time curve of BBR. G-6-P is solely catalyzed by GK, and GK activity and expression showed a positive correlation with liver BBR levels. In db/db mice, BBR also upregulated GK in liver fractions (cytoplasm and nucleus) and liver glycogen content. GK functionally worked by dissociating from GK regulatory protein (GKRP), and although GKRP expression was not affected, we found a decreased ratio of GK binding with GKRP in BBR treated db/db mice. In conclusion, our study suggests the dissociation of GK from GKRP as the potential mechanism for liver GK increase upon BBR treatment, which contributes to the anti-diabetic effect of BBR.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".