RNA-based therapies for neurodevelopmental disorders: innovative tools for molecular correction
Bibliographic record
Abstract
Modulation of RNA and protein expression to restore or normalize neuronal function has emerged as a powerful therapeutic strategy for neurodevelopmental disorders (NDDs) tailoring individual genetic mutations causing intellectual disability (ID), or autism spectrum disorder (ASD), or developmental epileptic encephalopathy (DEE). In recent years, diverse classes of RNA-based molecules have been developed with therapeutic potential, including antisense oligonucleotides (ASOs), oligonucleotides targeting natural antisense transcripts (antagoNATs), Short Interspersed Nuclear Element UP-regulating RNAs (SINEUPs), interfering RNAs (RNAi), Exon-Specific engineering U1 small nuclear RNAs (ExSpeU1s), and small-activating RNA (saRNA) This review highlights the promising advances of these RNA-based therapeutics in addressing syndromic ID, such as Fragile X syndrome, MECP2 duplication syndrome, FOXG1 -gene related Rett syndrome and Angelman syndrome, which are characterized by well-defined genetic mutations with limited treatment options. Moreover, ASD-related condition linked to mutations in CHD8 is under investigation, extending the therapeutic landscape to complex behavioral and cognitive disorders. In the same way, several DEEs caused by mutations in CDKL5 , DNM1 , KCNT1, SCN1A , SCN2A , SCN8A , and UBA5 genes, which present severe pharmaco-resistant epilepsy, are increasingly becoming targets for RNA molecules that aim to restore neuronal excitability and network function. Together, these findings underscore the expanding therapeutic landscape enabled by RNA technologies, offering unprecedented specificity and flexibility for gene-targeted interventions in NDDs. As the field of RNA medicine continues to evolve across genomics and neuroscience, we aim to provide a resource for researchers and clinicians on promising innovative tools for molecular correction.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".