MicroRNA-181a Suppresses Progestin-Stimulated Breast Cancer Cell Growth
Bibliographic record
Abstract
Despite all our efforts, breast cancer remains a major public health problem threatening women's health all around the world.The morbidity of breast cancer is rising in most countries and is going to rise further over the next 20 years.Hormone treatment is widely used and it is the most efficient method of reducing menopausal symptoms or preventing abortion and pregnancy.However, multiple studies have demonstrated that hormones, especially synthetic progesterone, remain an indisputable risk factor for breast cancer.MicroRNAs are a group of endogenous small non-coding single strand RNAs which play regulatory roles in the initiation, development, and progression of different types of cancer.Evidence from multiple sources indicates that microRNA-181a exerts anti-breast cancer effects by inducing cancer cell death and preventing tumor invasion, infiltration and metastasis, etc.Our recent studies revealed that microRNA-181a not only suppresses breast cancer MCF-7 cell growth but also abrogates progestin-provoked cell growth.In this review, several interesting aspects of hormone replacement therapy and the role of microRNA-181a during progestin treatment are discussed.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".