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 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 | 0.002 | 0.001 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".