Potential of miRNA-Based Nanotherapeutics for Uveal Melanoma
Bibliographic record
Abstract
Uveal melanoma (UM) is the most common adult intraocular cancer, and metastatic UM remains deadly and incurable. UM is a complex disease associated with the deregulation of numerous genes and redundant intracellular signaling pathways. As understanding of epigenetic dysregulation in the oncogenesis of UM has increased, the abnormal expression of microRNAs (miRNAs) has been found to be an epigenetic mechanism underlying UM tumorigenesis. A growing number of miRNAs are being found to be associated with aberrant signaling pathways in UM, and some have been investigated and functionally characterized in preclinical settings. This review summarizes the miRNAs with promising therapeutic potential for UM treatment, paying special attention to the therapeutic miRNAs (miRNA mimics or inhibitors) used to restore dysregulated miRNAs to their normal levels. However, several physical and physiological limitations associated with therapeutic miRNAs have prevented their translation to cancer therapeutics. With the advent of nanotechnology delivery systems, the development of effective targeted therapies for patients with UM has received great attention. Therefore, this review provides an overview of the use of nanotechnology drug delivery systems, particularly nanocarriers that can be loaded with therapeutic miRNAs for effective delivery into target cells. The development of miRNA-based therapeutics with nanotechnology-based delivery systems may overcome the barriers of therapeutic miRNAs, thereby enabling their translation to therapeutics, enabling more effective targeting of UM cells and consequently improving therapeutic outcomes.
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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 | 0.001 |
| 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".