Bibliographic record
Abstract
Progressive cell loss due to apoptosis is a pathological hallmark implicated in a wide spectrum of degenerative diseases such as heart disease, atherosclerotic arteries and hypertensive vessels, Alzheimer's disease and other neurodegenerative disorders. Tremendous efforts have been made to improve our understanding of the molecular mechanisms and signaling pathways involved in apoptosistic cell death. Once ignored completely or overlooked as cellular detritus, microRNAs (miRNAs) that were discovered only a decade ago, have recently taken many by surprise. The importance of miRNAs has steadily gained appreciation and miRNA biology has exploded into a massive swell of interest with enormous range and potential in almost every biological discipline because of their widespread expression and diverse functions in both animals and humans. It has been established that miRNAs are critical regulators of apoptosis of various cell types. These small molecules act by repressing the expression of either the proapoptotic or antiapoptotic genes to produce antiapoptotic or proapoptotic effects. Appealing evidence has been accumulating for the involvement of miRNAs in human diseases associated with apoptotic cell death and the potential of miRNAs as novel therapeutic targets for the treatment of the diseases. This editorial aims to convey this message and to boost up the research interest by providing a timely, comprehensive overview on regulation of apoptosis by miRNAs and a synopsis on the pathophysiologic implications of this novel regulatory network based on the currently available data in the literature. It begins with a brief introduction to apoptosis and miRNAs, followed by the description of the fundamental aspects of miRNA biogenesis and action, and the role of miRNAs in regulating apoptosis of cancer cells and cardiovascular cells. Speculations on the development of miRNAs as potential therapeutic targets are also presented. Remarks are also provided to point out the unanswered questions and to outline the new directions for the future research of the field.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".