MicroRNA-based therapeutics for inflammatory disorders of the microbiota-gut-brain axis
Bibliographic record
Abstract
An emerging but less explored shared pathophysiology across microbiota-gut-brain axis disorders is aberrant miRNA expression, which may represent novel therapeutic targets. miRNAs are small, endogenous non-coding RNAs that are important transcriptional repressors of gene expression. Most importantly, they regulate the integrity of the intestinal epithelial and blood-brain barriers and serve as an important communication channel between the gut microbiome and the host. A well-defined understanding of the mode of action, therapeutic strategies and delivery mechanisms of miRNAs is pivotal in translating the clinical applications of miRNA-based therapeutics. Accumulating evidence links disorders of the microbiota-gut-brain axis with a compromised gut-blood-brain-barrier, causing gut contents such as immune cells and microbiota to enter the bloodstream leading to low-grade systemic inflammation. This has the potential to affect all organs, including the brain, causing central inflammation and the development of neurodegenerative and neuropsychiatric diseases. In this review, we have examined in detail miRNA biogenesis, strategies for therapeutic application, delivery mechanisms, as well as their pathophysiology and clinical applications in inflammatory gut-brain disorders. The research data in this review was drawn from the following databases: PubMed, Google Scholar, and Clinicaltrials.gov. With increasing evidence of the pathophysiological importance for miRNAs in microbiota-gut-brain axis disorders, therapeutic targeting of cross-regulated miRNAs in these disorders displays potentially transformative and translational potential. Further preclinical research and human clinical trials are required to further advance this area of research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".