Polymeric Delivery of siRNA against Integrin‐β1 (CD29) to Reduce Attachment and Migration of Breast Cancer Cells
Bibliographic record
Abstract
Cell surface integrins, which play important roles in the survival, proliferation, migration, and invasion of cancer cells, are a viable target for treatment of metastatic breast cancer. This line of therapy still remains challenging due to the lack of proper identification and validation of effective targets as well as the lack of suitable therapeutic agents for treatment. The focus is on one such molecular target for this purpose, namely integrin-β1, and effective lowering of integrin-β1 levels on a breast cancer model (MDA-MB-231 cells) is achieved by delivering a dicer-substrate short interfering RNA (siRNA) targeting integrin-β1 with lipid-modified low molecular weight polyethylenimine polymers. Reduction of integrin-β1 levels leads to reduced adhesion of MDA-MB-231 cells to extracellular matrix component fibronectin as well as to human bone marrow cells. A reduced migration of the breast cancer cells is also observed after integrin-β1 silencing in "scratch" and "transwell" migration assays. These results highlight the importance of integrin-β1 for the migration of metastatic breast cancer cells by effectively silencing this target with a practical dose of siRNA.
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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".