🧫
ROC curve analysis for predicting sEV ineffectiveness
active
experiment
Created: 2026-04-06T12:31:46
By: etl-v1-backfill
Quality:
50%
✓ SciDEX
ID: exp-6323f55d-28ce-49f5-9301-d43907b8eb96
🧫 Experiment Protocol
Clinicaltype 2 diabetes mellitus, obesity, ischemic diseasemiR-130ahuman patientsproposed
This clinical diagnostic study used Receiver Operating Characteristic (ROC) curve analysis to develop a predictive model for identifying ineffective sEV based on miR-130a and TGF-β content. The analysis aimed to create a clinically useful tool for identifying patients whose autologous sEV would be ineffective for pro-angiogenic therapy. ROC curve analyses were performed using miR-130a and TGF-β content measurements 'in Series' to predict sEV ineffectiveness. The results revealed that sEV ineffectiveness could be predicted with a Likelihood Ratio positive (LH+) of 3.3 with 95% confidence interval from 2.6 to 3.9. This diagnostic analysis provided a practical framework for patient stratification in potential sEV-based therapeutic applications.
PRIMARY OUTCOME
prediction of sEV ineffectiveness
EXPECTED OUTCOMES
development of predictive model for sEV effectiveness
SUCCESS CRITERIA
statistically significant predictive capability with clinically useful likelihood ratios
PROTOCOL
ROC curve analysis using miR-130a and TGF-β content measurements in series
Source: PMID 31959759 ↗
🧫 Experiment Extras
PATHWAY
TGF-β signaling pathway, angiogenesis pathway
MARKET PRICE
$0.50
STATUS
proposed
▸Metadataorigin_type: v1_polymorphic_backfill
| origin_type | v1_polymorphic_backfill |
| source_table | experiments |
| _schema_version | 1 |
📊 Evidence Profile
Evidence Balance
+0%
Certainty
0%
Debates
0
Incoming
0
Outgoing
0
0 supporting
0 contradicting
0 neutral
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