Ultrasound-targeted microbubble destruction (UTMD)—Novel applications for microRNA delivery
Bibliographic record
Abstract
Ultrasound-targeted microbubble destruction (UTMD) is a non-invasive technique for gene delivery, utilizing high power ultrasound and nucleic acid-bearing microbubbles. UTMD has been used in a variety of in vivo applications, including cardiac and skeletal muscle, kidney, liver, cerebral and even lung, and have been studied using many gene vectors, including plasmid, viral and small interfering RNA. The focus of gene therapy has now shifted towards small non-coding RNAs, including microRNAs (miRNA). These non-coding RNAs are important transcriptional and post-transcriptional inhibitors of gene expression that regulate the translational output of target messenger RNAs. MicroRNAs have been shown to participate in a multitude of cellular process, and their dysregulation may play an important pathophysiologic role in many different human pathologies. Coupled with their ability to specifically target particular cellular pathways, makes the possibility of exploiting miRNAs to develop therapeutic strategies extremely attractive. This presentation will focus specifically on several miRNAs with UTMD applications in (1) chronic ischemic peripheral arterial disease, (2) ischemia-reperfusion injury, and (3) abdominal aortic aneurysms (AAA). For each application, we will discuss selection of miRNA, aspects of tissue targeting in vivo, enhancement of therapeutic effect and potential for clinical translation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".