A Computationally Optimized Ribonucleic Acid Circularization Strategy without Byproducts
Bibliographic record
Abstract
Circular mRNA (mRNA) exhibits promising potential in mRNA therapy due to its increased stability and extended duration of protein translation, which has sparked an urgent demand for efficient methods to prepare circular RNAs in vitro . Here, we present a versatile self-circularization strategy that employs simple motifs to synthesize circular RNAs, achieving robust efficiencies for sequences ranging from dozens to thousands of nucleotides. By leveraging an automated computational program, we optimized highly specific lock-key structures to maximize circularization efficiency, particularly for long RNA substrates. Furthermore, the shared sequence and functionality between linear precursor RNAs and circular products eliminate the need for additional purification steps to remove excess nucleic acid components, simplifying the production process. This approach also yields circular RNAs with superior stability and translation efficiency, enabling sustained protein expression in vitro and in vivo . Our computationally optimized, purification-free method holds immense promise for scalable circular RNA production and the development of advanced RNA therapeutics, significantly advancing mRNA therapy.
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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 | 0.001 |
| 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".