Delivery of the gene encoding the tumor suppressor Sef into prostate tumors by therapeutic-ultrasound inhibits both tumor angiogenesis and growth
Bibliographic record
Abstract
Carcinomas constitute over 80% of all human cancer types with no effective therapy for metastatic disease. Here, we demonstrate, for the first time, the efficacy of therapeutic-ultrasound (TUS) to deliver a human tumor suppressor gene, hSef-b, to prostate tumors in vivo. Sef is downregulated in various human carcinomas, in a manner correlating with tumor aggressiveness. In vitro, hSef-b inhibited proliferation of TRAMP C2 cells and attenuated activation of ERK/MAPK and the master transcription factor NF-κB in response to FGF and IL-1/TNF, respectively. In vivo, transfection efficiency of a plasmid co-expressing hSef-b/eGFP into TRAMP C2 tumors was 14.7 ± 2.5% following a single TUS application. Repeated TUS treatments with hSef-b plasmid, significantly suppressed prostate tumor growth (60%) through inhibition of cell proliferation (60%), and reduction in blood vessel density (56%). In accordance, repeated TUS-treatments with hSef-b significantly inhibited in vivo expression of FGF2 and MMP-9. FGF2 is a known mitogen, and both FGF2/MMP-9 are proangiogenic factors. Taken together our results strongly suggest that hSef-b acts in a cell autonomous as well as non-cell autonomous manner. Moreover, the study demonstrates the efficacy of non-viral TUS-based hSef-b gene delivery approach for the treatment of prostate cancer tumors, and possibly other carcinomas where Sef is downregulated.
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".