Direct detection of microRNA based on plasmon hybridization of nanoparticle dimers
Bibliographic record
Abstract
MicroRNAs (miRNA) are important for regulating a range of biochemical pathways. Abnormal levels of miRNA in cells or secreted into biological fluids have been identified in diseases. MiRNA can therefore be potential biomarkers for early disease diagnosis; however their detection and quantification are challenging. Herein we apply the sensing platform of discrete actuatable dimers for the detection of human miR-210 (hsa-miR-210-3p). The detection signal is a spectral blue shift in the hybridized plasmon mode as monitored by single-nanostructure spectroscopy. We investigate the specificity and detection limit of the platform and quantify miR-210 levels in RNA extracts of cells cultured under different oxygen tensions. In addition we demonstrate the feasibility of detection in complex media by examining miR-210 secreted in cell media. This sensing platform may be developed as a bioanalytical tool for validating miRNA profiles of biological fluids.
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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) | 0.000 | 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 | 0.000 | 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".