Synergistic effects of autophagy inhibitors combined with cisplatin against cisplatin-resistant nasopharyngeal cancer cells
Bibliographic record
Abstract
This study explored the synergistic effects of autophagy inhibitors combined with cisplatin against cisplatin-resistant nasopharyngeal cancer cells by treating HNE-1 and cisplatin (diamminedichloroplatinum; DDP)-resistant HNE1/DDP nasopharyngeal cancer cell lines with DDP, autophagy inhibitors, or a combination of autophagy inhibitors and DDP. Cell viability was determined via MTT (colorimetric) and colony-forming assays, and the rate of apoptosis was determined using propidium iodide (PI) and annexin V double-staining. The expressions of proteins were determined by Western blotting. For our in-vivo studies, a murine xenograft model was established to evaluate the anti-tumor effects of the combination of autophagy inhibitor and DDP. The results showed that treatment with DDP increased the expressions of ATP-binding cassette sub-family B member 1 (ABCB1), ATP Binding Cassette Subfamily C Member 1 (ABCC1), and P-glycoprotein 1 (P-gp) in the HNE1/DDP cell lines. Treatment with chloroquine decreased the expression levels of ABCB1, ABCC1, and P-gp, and increased the formation of LC3-II and the expression levels of p62 in the HNE1/DDP cells. Additionally, the combination of autophagy inhibitors and DDP produced a synergistic effect on DDP-induced cell death and apoptosis. Furthermore, the combination of the autophagy inhibitor and DDP showed significant anti-tumor effects in the xenograft mouse model. In summary, autophagy inhibitors show synergistic anti-tumor effects with DDP in vitro against DDP-resistant nasopharyngeal cancer cells and in vivo in our xenograft murine model.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".