miRNA Library Preparation Optimisation for Low-Concentration and Low-Volume Paediatric Plasma Samples
Bibliographic record
Abstract
Background: Analysing circulating miRNAs in paediatric plasma is challenging due to typically low sample volumes. The QIAseq miRNA UDI Library Kit (Qiagen, Hilden, Germany) was selected as it has a proven track record with a specific protocol for plasma and serum. The protocol, however, required optimisation for use with low-volume paediatric plasma samples before generating acceptable yields in our cohort. Methods: The miRNeasy Serum/Plasma kit (Qiagen) and the MagMAX miRVana Total Isolation kit (ThermoFisher Scientific, Waltham, MA, USA) were assessed following the manufacturer’s instructions with 100 µL and 200 µL of paediatric plasma. Libraries were prepared using the QIAseq miRNA UDI Library Kit (Qiagen). Optimisations were made for the QIAseq miRNA UDI Library Kit (Qiagen) using total RNA extracted with the miRNeasy Serum/Plasma kit (Qiagen) from 100 µL of plasma. Results: Prior to optimisation, both RNA extraction kits underperformed with the QIAseq miRNA UDI Library kit, producing low miRNA library yields ranging between 0 and 1.42 ng/µL. Plasma input volumes of 100 µL and 200 µL demonstrated no significant differences. Adjusting the QIAseq protocol for low RNA concentrations improved miRNA library yields, an average of 5.6 ng/µL and a maximum of 24.3 ng/µL across 92 samples. The optimised protocol showed no age or gender biases with the QIAseq kit. Conclusions: Failure rates in miRNA library preparations are rarely reported, making it hard to gauge whether the 8.7% failure rate observed here is typical. However, given the challenges of using low-concentration, low-volume paediatric plasma, this represents a significant improvement over previous attempts, supporting further research in the field.
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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".