Dual Functional Roles of Molecular Beacon as a MicroRNA Detector and Inhibitor
Bibliographic record
Abstract
MicroRNAs are essential in many cellular processes. The ability to detect microRNAs is important for understanding its function and biogenesis. This study is aimed at using a molecular beacon to detect miR-430 in developing zebrafish embryos as a proof of principle. miR-430 is crucial for the clearance of maternal mRNA during maternal zygotic transition in embryonic development. Despite its known function, the temporal and spatial expression of miR-430 remains unclear. We used various imaging techniques, including laser scanning confocal microscopy, spinning disk, and lightsheet microscopy, to study the localization of miR-430 and any developmental defects possibly caused by the molecular beacon. Our results show that miR-430 is expressed early in development and is localized in distinct cytoplasmic granules where its target mRNA can be detected. We also show that the designed molecular beacon can inhibit the function of miR-430 and cause developmental defect in the brain, notochord, heart, and kidney, depending on the delivery site within the embryo, suggesting that miR-430 plays a diverse role in embryonic morphogenesis. When compared with morpholino, molecular beacon is 2 orders of magnitude more potent in inhibiting miR-430. Thus, our results reveal that in addition to being used as a valuable tool for the detection of microRNAs in vivo , molecular beacons can also be employed to inhibit microRNAs in a specific manner.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".