Preparation and processing of dried blood spots for microRNA sequencing
Bibliographic record
Abstract
Dried blood spots (DBS) are biological samples commonly collected from newborns and in geographic areas distanced from laboratory settings for the purposes of disease testing and identification. MicroRNAs (miRNAs)-small non-coding RNAs that regulate gene activity at the post-transcriptional level-are emerging as critical markers and mediators of disease, including cancer, infectious diseases, and mental disorders. This protocol describes optimized procedural steps for utilizing DBS as a reliable source of biological material for obtaining peripheral miRNA expression profiles. We outline key practices, such as the method of DBS rehydration that maximizes RNA extraction yield, and the use of degenerate oligonucleotide adapters to mitigate ligase-dependent biases that are associated with small RNA sequencing. The standardization of miRNA readout from DBS offers numerous benefits: cost-effectiveness in sample collection and processing, enhanced reliability and consistency of miRNA profiling, and minimal invasiveness that facilitates repeated testing and retention of participants. The use of DBS-based miRNA sequencing is a promising method to investigate disease mechanisms and to advance personalized medicine.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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".