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 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".