Could MicroRNAs Be Useful Tools to Improve the Diagnosis and Treatment of Rare Gynecological Cancers? A Brief Overview
Bibliographic record
Abstract
Gynecological cancers pose an important public health issue, with a high incidence among women of all ages. Gynecological cancers such as malignant germ-cell tumors, sex-cord-stromal tumors, uterine sarcomas and carcinosarcomas, gestational trophoblastic neoplasia, vulvar carcinoma and melanoma of the female genital tract, are defined as rare with an annual incidence of <6 per 100,000 women. Rare gynecological cancers (RGCs) are associated with poor prognosis, and given the low incidence of each entity, there is the risk of delayed diagnosis due to clinical inexperience and limited therapeutic options. There has been a growing interest in the field of microRNAs (miRNAs), a class of small non-coding RNAs of ∼22 nucleotides in length, because of their potential to regulate diverse biological processes. miRNAs usually induce mRNA degradation and translational repression by interacting with the 3' untranslated region (3'-UTR) of target mRNAs, as well as other regions and gene promoters, as well as activating translation or regulating transcription under certain conditions. Recent research has revealed the enormous promise of miRNAs for improving the diagnosis, therapy and prognosis of all major gynecological cancers. However, to date, only a few studies have been performed on RGCs. In this review, we summarize the data currently available regarding RGCs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".