Advanced approaches of the use of circRNAs as a replacement for cancer therapy
Bibliographic record
Abstract
Cancer is a broad name for a group of diseases in which abnormal cells grow out of control and are characterized by their complexity and recurrence. Although there has been progress in cancer therapy with the entry of precision medicine and immunotherapy, cancer incidence rates have increased globally. Non-coding RNAs in the form of circular RNAs (circRNAs) play crucial roles in the pathogenesis, clinical diagnosis, and therapy of different diseases, including cancer. According to recent studies, circRNAs appear to serve as accurate indicators and therapeutic targets for cancer treatment. However, circRNAs are promising candidates for cutting-edge cancer therapy because of their distinctive circular structure, stability, and wide range of capabilities; many challenges persist that decrease the applications of circRNA-based cancer therapeutics. Here, we explore the roles of circRNAs as a replacement for cancer therapy, highlight the main challenges facing circRNA-based cancer therapies, and discuss the key strategies to overcome these challenges to improve advanced innovative therapies based on circRNAs with long-term health effects.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".