Survivin silencing improved the cytotoxicity of carboplatin and melphalan in Y79 and primary retinoblastoma cells
Bibliographic record
Abstract
Survivin stands out as one of the most specific cancer targets discovered to date. Although single inhibition, e.g. through small interfering RNA (siRNA), has shown modest results in clinical trials, its combination with drugs holds promise to sensitize cancer cells to chemotherapeutics. In this study, we propose a sequential treatment of siRNA survivin followed by chemotherapy. Firstly, we demonstrated that siRNA-loaded switchable lipid nanoparticles (siLNP) silence survivin in a panel of cancer cell lines. Subsequently, we selected retinoblastoma (RB) as our model to screen four chemotherapeutic agents: carboplatin, topotecan, melphalan or teniposide. The effect of drugs on survivin expression and caspase-3 was investigated by RT-qPCR. The best drug combination was selected measuring the viability, survivin expression and the selectivity of the treatment. Our stepwise method revealed that siRNA delivery by switchable LNP sensitized Y79, but not the healthy APRE-19 cell line, to carboplatin and melphalan cytotoxicity. This ability was validated on primary human RB cells. Finally, the distinct behavior of the drugs demonstrated that a diligent screening of drugs should be envisioned when looking for synergy with survivin. Our sequential approach highlighted carboplatin and melphalan as agents to be investigated in future survivin-associated in vivo testing to tackle RB.
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) | 0.001 | 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 | 0.001 |
| 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".