MicroRNAs as Tools and Effectors for Patient Treatment in Gastrointestinal Carcinogenesis
Bibliographic record
Abstract
In the last 20 years, microRNAs (miRNAs) have become the most promising class of diagnostic and prognostic biomarkers for human cancer. From a therapeutic perspective, advances in the understanding of the molecular role of miRNAs in the pathological processes have significantly influenced the selection of new therapeutic modalities. Moreover, the intrinsic characteristics that confer stability to miRNAs in vitro, allow a longer molecular/structural resistance and activity in vivo. Preclinical models have consistently underlined the feasibility and efficacy of miRNA-based therapies, either alone or in combination with current targeted therapies. The appealing strength of such therapeutic option dwells in miRNAs' ability to concurrently target multiple genes, frequently in the context of a specific network/pathway. This property allows miRNA-based therapy to be extremely efficient in regulating distinct biological processes relevant to normal and pathological cell homeostasis. The purpose of this review is to summarize the role of miRNAs in gastrointestinal carcinogenesis and their potential use as novel biomarkers and therapeutics.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".