Comprehensive analysis of mammalian miRNA* species and their role in myeloid cells
Bibliographic record
Abstract
Processing of pre-miRNA through Dicer1 generates an miRNA duplex that consists of an miRNA and miRNA* strand. Despite the general view that miRNA*s have no functional role, we further investigated miRNA* species in 10 deep-sequencing libraries from mouse and human tissue. Comparisons of miRNA/miRNA* ratios across the miRNA sequence libraries revealed that 50% of the investigated miRNA duplexes exhibited a highly dominant strand. Conversely, 10% of miRNA duplexes showed a comparable expression of both strands, whereas the remaining 40% exhibited variable ratios across the examined libraries, as exemplified by miR-223/miR-223* in murine and human cell lines. Functional analyses revealed a regulatory role for miR-223* in myeloid progenitor cells, which implies an active role for both arms of the miR-223 duplex. This was further underscored by the demonstration that miR-223 and miR-223* targeted the insulin-like growth factor 1 receptor/phosphatidylinositol 3-kinase axis and that high miR-223* levels were associated with increased overall survival in patients with acute myeloid leukemia. Thus, we found a supporting role for miR-223* in differentiating myeloid cells in normal and leukemic cell states. The fact that the miR-223 duplex acts through both arms extends the complexity of miRNA-directed gene regulation of this myeloid key miRNA.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".