Effect of traditional Chinese medicine components on multidrug resistance in tumors mediated by P-glycoprotein
Bibliographic record
Abstract
Multidrug resistance (MDR) is a major cause of chemotherapy failure. It occurs when an organism is resistant to one type of drug, but also develops resistance to other drugs with different structures and targets. There is a high incidence of MDR in cancer chemotherapy, therefore, finding an effective and non-toxic MDR reversal agent is an important goal, particularly for P-glycoprotein-mediated MDR in cancer. Improvements continue to be made to the status and understanding of traditional Chinese medicine (TCM), due to the advantages of low toxicity and relatively minor side effects. Therefore TCM is currently being used in the treatment of various types of diseases. In recent years, numerous components of TCM have been identified to be effective in reversing MDR by downregulating expression of the drug transporter membrane protein, recovering changes in enzymes involved in detoxification and metabolism and repairing the cell apoptosis pathway. The present study summarizes the anticancerous properties and MDR reversing components of traditional medicinal plants commonly used in the treatment of cancer.
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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".