The clinical impact of mRNA therapeutics in the treatment of cancers, infections, genetic disorders, and autoimmune diseases
Bibliographic record
Abstract
mRNA-based therapeutics have revolutionized medicine and the pharmaceutical industry. The recent progress in the optimization and formulation of mRNAs has led to the development of a new therapeutic platform with a broad range of applications. With a growing body of evidence supporting the use of mRNA-based drugs for precision medicine and personalized treatments, including cancer immunotherapy, genetic disorders, and autoimmune diseases, this emerging technology offers a rapidly expanding category of therapeutic options. Furthermore, the development and deployment of mRNA vaccines have facilitated a prompt and flexible response to medical emergencies, exemplified by the COVID-19 outbreak. The establishment of stable and safe mRNA molecules carried by efficient delivery systems is now available through recent advances in molecular biology and nanotechnology. This review aims to elucidate the advancements in the clinical applications of mRNAs for addressing significant health-related challenges such as cancer, autoimmune diseases, genetic disorders, and infections and provide insights into the efficacy and safety of mRNA therapeutics in recent clinical trials.
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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) | 0.000 | 0.001 |
| 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".