Regulatory non-coding RNA: new instruments in the orchestration of cell death
Bibliographic record
Abstract
Non-coding RNA (ncRNA) comprises a substantial portion of primary transcripts that are generated by genomic transcription, but are not translated into protein. The possible functions of these once considered 'junk' molecules have incited considerable interest and new insights have emerged. The two major members of ncRNAs, namely micro RNA (miRNA) and long non-coding RNA (lncRNA), have important regulatory roles in gene expression and many important physiological processes, which has recently been extended to programmed cell death. The previous paradigm of programmed cell death only by apoptosis has recently expanded to include modalities of regulated necrosis (RN), and particularly necroptosis. However, most research efforts in this field have been on protein regulators, leaving the role of ncRNAs largely unexplored. In this review, we discuss important findings concerning miRNAs and lncRNAs that modulate apoptosis and RN pathways, as well as the miRNA-lncRNA interactions that affect cell death regulation.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".