Identification of MicroRNAs as Targets for Treatment of Ischemic Stroke
Bibliographic record
Abstract
Ischemic stroke remains a leading cause of death and disability with few treatment options. MicroRNAs (miRs) are short, non-coding RNAs that regulate gene expression. They have important potential applications as biomarkers for stroke severity and outcome, as well as presenting unique possibilities for interventions to minimize injury and improve recovery and outcome following stroke. MiRs function by binding messenger RNAs (mRNA) and silencing translation of target genes. Endogenous miR expression levels change in response to stress, and they can be altered by application of exogenous nucleotides—miR mimics—to increase or inhibitors to decrease levels of specific miRs. By virtue of their relatively short binding sequences, a single miR can simultaneously modulate numerous related gene targets. As miR expression can be cell-type specific, miRs can also be used to target specific brain cell types, such as microglia and astrocytes, which helps determine neuronal cell fate following stress. MiR-based therapeutics may therefore provide a novel approach to the development of effective therapeutics for ischemic stroke.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".