Targeting Tumor Ubiquitin-Proteasome Pathway with Polyphenols for Chemosensitization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of tumor drug resistance is one of the biggest obstacles on the way to achieve a favorable outcome of chemotherapy. Among various strategies that have been explored to overcome drug resistance, the combination of current chemotherapy with plant polyphenols as a chemosensitizer has emerged as a promising one. Plant polyphenols are a group of phytochemicals characterized by the presence of more than one phenolic group. Mechanistic studies suggest that polyphenols have multiple intracellular targets, one of which is the proteasome complex. The proteasome is a proteolytic enzyme complex responsible for intracellular protein degradation and has been shown to play an important role in tumor growth and the development of drug resistance. Therefore, proteasome inhibition by plant polyphenols could be one of the mechanisms contributing to their chemosensitizing effect. Plant polyphenols that have been identified to possess proteasome-inhibitory activity include (-)-epigallocatechins-3-gallate (EGCG), genistein, luteolin, apigenin, chrysin, quercetin, curcumin and tannic acid. These polyphenols have exhibited an appreciable effect on overcoming resistance to various chemotherapeutic drugs as well as multidrug resistance in a broad spectrum of tumors ranging from carcinoma and sarcoma to hematological malignances. The in vitro and in vivo studies on polyphenols with proteasome-inhibitory activity have built a solid foundation to support the idea that they could serve as a chemosensitizer for the treatment of cancer. In-depth mechanistic studies and identification of optimal regimen are needed in order to eventually translate this laboratory concept into clinical trials to actually benefit current chemotherapy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it