Considerations for optimization of microRNA PCR assays for molecular diagnosis
Bibliographic record
Abstract
The remarkable stability of microRNAs in biofluids underlies their potential as biomarkers, but their small size presents challenges for detection by RT-qPCR. The heterogeneity of microRNAs, with each one comprising a series of variants or 'isomiRs', adds additional complexity. Presented here are the key considerations for use of RT-qPCR to measure microRNAs and their isomiRs, with a focus on plasma. Modified nucleotides can be incorporated into primer sequences to enhance affinity and provide increased specificity and sensitivity for RT-qPCR assays. Approaches based upon polyA tailing and use of a common oligo(dT)-based reverse transcription oligonucleotide will detect most isomiRs. Conversely, stem-loop RT oligonucleotides and sequence specific probes can enable detection of specific isomiRs of interest. Next generation sequencing of all the products of a microRNA RT-PCR reaction is a promising new approach for both microRNA quantification and characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".