Bibliographic record
Abstract
MicroRNAs are a class of non-coding small RNAs, which posttranscriptionally regulate gene expression through mainly binding to 3' untranslated region of mRNA. Most microRNAs are evolutionally conserved cross species; whereas, novel microRNAs expressed in different organisms are also identified with next generation sequencing technology. MicroRNAs play crucial roles in development, stem cells self-renewal, apoptosis and cell cycle. Aberrant microRNA expression in cancer and other diseases has been extensively investigated; the specific microRNAs have been developed for cancer diagnosis, prediction of drug-response and therapeutic outcome. Given the roles of microRNAs in pathophysiological conditions, it is conceivable that development of “miR-drugs” with different strategies (miR mimics, anti-miR, small molecule inhibitors of specific miRs) provides great hope to fight against cancer in combination of conventional treatment. In this review, the course of microRNA research to understand cancer biology is briefly introduced, the translation of miRNA studies from bench to bedside, particularly, microRNA implication in cancer with patents for diagnosis, prognosis will be described; the current status and challenges of “miR-drugs” development will be discussed. Keywords: microRNA, gene expression, patents, treatment, miR-drugs, miRNome, MicroRNAs Patents, Bench to Bedsides, Caenorhabditis elegan, hematopoietic malignancy, lymphocytic leukemia, solid tumors, MTg-AMO, MiR-21, ANP32A/ SMARCA4, pharmacodynamics, pharmacokinetics
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".