MicroRNAs and apoptosis: implications in the molecular therapy of human disease
Bibliographic record
Abstract
1. MicroRNAs (miRNAs), the small non-coding RNAs of approximately 22 nucleotides, are now recognized as a very large family present throughout the genomes of plants and metazoans. These small transcripts modulate protein expression by binding to complementary or partially complementary target protein-coding mRNAs and targeting them for degradation or translational inhibition. 2. The discovery of miRNAs has revolutionized our understanding of the mechanisms that regulate gene expression, with the addition of an entirely novel level of regulatory control. Considerable information on miRNAs has been accumulated in this rapidly evolving research field. We now know that miRNAs play pivotal roles in diverse processes, such as development and differentiation, control of cell proliferation and death, stress response and metabolism. Indeed, aberrant miRNA expression has been documented in human disease as well as in animal models, with evidence for a causative role in tumourigenesis. 3. One of the most active fields of miRNA research is miRNA regulation of apoptosis, a programmed cell death implicated in many human diseases, such as cancer, Alzheimer's disease, hypertrophy and heart failure. Thus far, nearly 30 of 500 human miRNAs have been validated experimentally to regulate apoptosis; this number is likely to increase with future studies. 4. The present review provides a comprehensive summary and analysis of the currently available data, focusing on the transcriptional controls, target genes and signalling pathways linking the apoptosis-regulating miRNAs and apoptotic cell death.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".