Unravelling the Effects of Berberine on Metabolic Activity, Apoptosis and Proliferation of Transformed Cells
Bibliographic record
Abstract
Berberine (BBR) is an isoquinoline that inhibits the proliferation of transformed cells in vitro, but due to its poor solubility and bioavailability, it has only moderate therapeutic potential in vivo. Increasing evidence indicates that BBR specifically targets several metabolic, signaling and gene transcription events in transformed cells, altering their progression through the cell cycle and decreasing their metabolic rate and proliferation. In order to further develop BBR as a therapeutic, its mode of cellular internalization and localization within the cell needs to be further examined. BBR’s molecular targets and interactions with kinases, transcription factors and some enzymes are discussed in an attempt to better understand BBR’s role in these important pathways and how they may lead to changes in metabolism. Lastly, this review examines some of the benefits and challenges of using BBR as an inhibitor in cancer cell proliferation in breast cancer and glioblastoma, as two examples. BBR is a potent drug with multiple targets and it is this multiplicity of function that makes BBR such a promising drug for targeting cell metabolism and proliferation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".