siRNA therapy for cancer and non-lethal diseases such as arthritis and osteoporosis
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Gene silencing mediated by siRNA has been widely investigated as a potential therapeutic approach. The success of these therapies depends on effective systems capable of selectively and efficiently conveying siRNA to targeted cells/organs with minimal toxicity. AREAS COVERED IN THIS REVIEW: This review discusses current experimental approaches to siRNA delivery strategies available for arthritis treatment and the management of other musculoskeletal disorders. The review covers literature on the subject from 2000 to 2010. WHAT THE READER WILL GAIN: In the last decade, extensive improvements have been made to optimize siRNA-based gene therapy and have been tested on several arthritis and orthopedic conditions. However, except for Phase I - II DNA-based gene therapy trials on arthritis, no clinical studies have reported siRNA application in these domains. TAKE HOME MESSAGE: Most musculoskeletal disorders, such as rheumatoid arthritis, osteoarthritis, fracture, aseptic loosening, cartilage and intervertebral disc degeneration are non-fatal and age-related chronic inflammatory conditions, but represent significant morbidity and a socio-economic burden. siRNA-based gene therapy offers treatment opportunities that are less invasive, more effective and less expensive than existing modalities. Future directions for siRNA therapy include the development of safe and more efficient delivery systems and the selection of optimal gene targets for disease control.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".