The Application of mRNA Technology for Vaccine Production—Current State of Knowledge
Bibliographic record
Abstract
Over the past 20 years, intensive research has been conducted on the development of therapeutic mRNA, leading to numerous discoveries that have enabled its use in therapy. The main achievements in this field include increasing mRNA stability, reducing its immunogenicity (i.e., its ability to trigger an immune response), and solving the challenge of delivering mRNA into cells-all to achieve a therapeutic effect. The aim of this study was to review the scientific literature on the use of mRNA technology in the production of vaccines. Various methods of applying mRNA technology that could potentially be introduced into clinical practice in the future are described. A detailed analysis was conducted on the approved COVID-19 vaccines developed by Pfizer/BioNTech (New York, NY, USA) and Moderna (Kirkland, QC, Canada), as their introduction marked a groundbreaking moment in the advancement of mRNA technology. This study was based on the latest scientific literature from reputable publishers and medical databases such as PubMed and ClinicalTrials. In conclusion, mRNA technology is currently experiencing rapid development, significantly driven by the ongoing COVID-19 pandemic. The application of this technology holds great potential not only for vaccines against infectious diseases but also for cancer treatment. However, further research is necessary to facilitate its broader clinical implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".