Bibliographic record
Abstract
MicroRNA/miRNA refers to types of RNA which are non-coding; they are21 to 25 nucleotides in length. In most cases, at particular nucleotide locations, they relate to one or more mRNAs. Deadenylation, cleavage, and alternative procedures of translation’s suppression are the means by which miRNA disturb gene repression. Recent investigations seem to propose that miRNAs are involved in many cell procedures and for this they became perfect targets for usage in healing purposes. Various miRNAs are involved in the development of human vascular diseases, due to their function in modulating vascular cell proliferation, differentiation, migration and apoptosis via their targeted genes. Since vascular diseases are multifactorial and complex, numerous genes may be involved in their progression and regulation. Therefore, miRNAs can also have multiple gene targets, and in some instances, one gene can be modulated by various miRNAs. As of now, more than 800 human microRNAs have been identified using miBase, and work is still being conducted to search for and characterize new miRNAs. With rigorous clinical and fundamental studies, a clear understanding of how miRNAs function, in addition to the ways they can be used as biomarkers and targets for cancer and cardiovascular illnesses, will progress.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".