Mitochondrion-targeted RNA therapies as a potential treatment strategy for mitochondrial diseases
Bibliographic record
Abstract
Mitochondrial diseases are one of the largest groups of neurological genetic disorders. Despite continuous efforts of the scientific community, no cure has been developed, and most treatment strategies rely on managing the symptoms. After the success of coronavirus disease 2019 (COVID-19) mRNA vaccines and accelerated US Food and Drug Administration (FDA) approval of four new RNAi drugs, we sought to investigate the potential of mitochondrion-targeting RNA-based therapeutic agents for treatment of mitochondrial diseases. Here we describe the causes and existing therapies for mitochondrial diseases. We then detail potential RNA-based therapeutic strategies for treatment of mitochondrial diseases, including use of antisense oligonucleotides (ASOs) and RNAi drugs, allotopic therapies, and RNA-based antigenomic therapies that aim to decrease the level of deleterious heteroplasmy in affected tissues. Finally, we review different mechanisms by which RNA-based therapeutic agents can be delivered to the mitochondrial matrix, including mitochondrion-targeted nanocarriers and endogenous mitochondrial RNA import pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".