<i>Morus alba</i> derived Kuwanon‐A combined with 5‐fluorouracil reduce tumor progression via synergistic activation of GADD153 in gastric cancer
Bibliographic record
Abstract
Abstract Despite the application of conventional strategies including chemotherapy, radiotherapy, surgery, or immunotherapy, the mortality of gastric cancer (GC) patients remains high. Often, GC is not diagnosed until it has reached late stage, resulting in a missed surgical window. Therefore, a new therapeutic intervention for GC is necessary. Here, the combined application of Kuwanon‐A (KA) and 5‐fluorouracil (5‐FU) was evaluated for its potential to combat GC for the first time. To determine the anticancer activity of KA (from Morus alba ) along with 5‐FU against GC, and their mechanism via GADD153, we examained anticancer potential of KA along with 5‐FU via in vitro assays with GC cells, namely MKN‐45, SGC‐7901, HGC‐27, and BGC‐823, and in vivo assays with mouse xenograft of GC. KA alone could induce G2/M phase arrest and apoptosis in GC cells by activating GADD153 through the PERK/elF2α/ATF4 and IRE1/XBP1 signaling pathways, suggesting a critical role of increased endoplasmic reticulum stress in KA‐induced apoptosis of GC cells. Moreover, the combination of KA and 5‐FU showed an enhanced synergistic anticancer effect against GC both in vitro and in vivo. Conclusively, the combination of KA and 5‐FU can act as an effective anticancer regimen in combating GC.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".