Microrna Let-7: An Emerging Next-Generation Cancer Therapeutic
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
In recent years, various RNA-based technologies have been under evaluation as potential next-generation cancer therapeutics. Micrornas (miRNAS), known to regulate the cell cycle and development, are deregulated in various cancers. Thus, they might serve as good targets or candidates in an exploration of anticancer therapeutics. One attractive candidate for this purpose is let-7 ("lethal-7"). Let-7 is underexpressed in various cancers, and restoration of its normal expression is found to inhibit cancer growth by targeting various oncogenes and inhibiting key regulators of several mitogenic pathways. In vivo, let-7 administration was found effective against mouse-model lung and breast cancers, and our computational prediction supports the possible effectiveness of let-7 in estrogen receptor (ER)-positive metastatic breast cancer. Data also suggest that let-7 regulates apoptosis and cancer stem cell (CSC) differentiation and can therefore be tested as a potential therapeutic in cancer treatment. However, the exact role of let-7 in cancer is not yet fully understood. There is a need to understand the causative molecular basis of let-7 alterations in cancer and to develop proper delivery systems before proceeding to therapeutic applications. This article attempts to highlight certain critical aspects of let-7's therapeutic potential in cancer.
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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 | 0.000 | 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 | 0.000 |
| 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