Analysis of Circulating microRNAs and Their Post-Transcriptional Modifications in Cancer Serum by On-Line Solid-Phase Extraction–Capillary Electrophoresis–Mass Spectrometry
Bibliographic record
Abstract
In this paper, an on-line solid-phase extraction capillary electrophoresis–mass spectrometry (SPE-CE-MS) method is described for the purification, preconcentration, separation, and characterization of endogenous microRNA (miRNA) and their post-transcriptional modifications in serum. First, analysis by CE-MS was optimized using a standard mixture of hsa-miR-21-5p (miR-21-5p) and hsa-let-7g-5p (let-7g-5p). For SPE-CE-MS, a commercial silicon carbide (SiC) resin was used to prepare the microcartridges. Under the optimized conditions with standards, the microcartridge lifetime (>25 analyses) and repeatability (2.8% RSD for the migration times; 4.4 and 6.4% RSD for the miR-21-5p and let-7g-5p peak areas, respectively) were good, the method was linear between 25 and 100 nmol·L –1, and the limit of detection (LOD) was around 10 nmol·L –1 (50 times lower than by CE-MS). In order to analyze human serum samples, an off-line sample pretreatment based on phenol/chloroform/isoamyl alcohol (PCA) extraction was necessary prior to SPE-CE-MS. The potential of the SPE-CE-MS method to screen for B-cell chronic lymphocytic leukemia (CLL) was demonstrated by an analysis of serum samples from healthy controls and patients. MicroRNAs, specifically miR-21-5p and a 23 nucleotide long 5′-phosphorylated miRNA with 3′-uridylation (iso-miR-16-5p), were only detected in the CLL patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".