Bibliographic record
Abstract
MicroRNA (miRNA) is a nonprotein coding small RNA molecule that negatively regulates gene expression by degradation of mRNA or suppression of mRNA translation. MiRNA plays important roles in physiological processes such as cellular development, differentiation, proliferation, apoptosis, and stem cell self-renewal. Studies show that the deregulation of miRNA expression is closely associated with tumorigenicity, invasion, and metastasis. The functionality of miRNAs may act as oncogenes or tumor suppressors during tumor initiation and progression. miRNomes in almost all types of cancers started to develop the regulatory network of miRNA::mRNA interaction in the view of systems biology. Experimental evidence demonstrates that the modulation of specific miRNA alterations in cancer cells using miRNA replacement or anti-miRNA technologies can restore miRNA activities and repair the gene regulatory network and signaling pathways, in turn, reverse the phenotype of cancerous cells. Numerous animal studies for miRNA-based therapy offer the hope of targeting miRNAs as alternative cancer treatment. Developing the small molecules to interfere with miRNAs could be of great pharmaceutical interest in the future. Interestingly, specific miRNA is capable of reprogramming the cancer cells into a pluriopotent embryonic stem cell-like state (mirPS), which could be induced into tissue-specific mature cell types. This chapter will present the various strategies of modulation of miRNAs in vitro and in vivo.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".