Circular RNAs in cancer: new insights into functions and implications in ovarian cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Circular RNAs (circRNAs) are a class of long non-coding RNAs (lncRNAs) which have a circular and closed loop structure. They are ubiquitous, stable, conserved and diverse RNA molecules with a range of activities such as translation and splicing regulation, which are able to interacting with RNA-binding proteins and specially miRNA sponge. The expression patterns of the circRNAs exhibited tissue specificity and also, step and stage specificity. Accumulating evidences approved the critical role of circular RNAs in many cancers such as ovarian cancer. Given that these molecules exert their effects through multiple cellular and molecular mechanisms (i.e., angiogenesis, apoptosis, growth, and metastasis) which are involved in cancer pathogenesis, circular RNAs, in particular, act by controlling cell proliferation in ovarian cancer, so that, it has been shown that the deregulation of these molecules is associated with initiation and progression of ovarian cancer. Therefore, they are attractive molecules which have introduced them as cancer biomarkers. Moreover, they could be used as new therapeutic candidates for developing novel treatment strategies. Here, for first time, we have provided a comprehensive review on the recent knowledge of circular RNAs and their pathological roles in the ovarian cancer.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it