Validation of reference gene stability for miRNA quantification by reverse transcription quantitative PCR in the peripheral blood of patients with COVID-19 critical illness
Bibliographic record
Abstract
The COVID-19 pandemic has created an urgency to study the host gene response that leads to variable clinical presentations of the disease, particularly the critical illness response. miRNAs have been implicated in the mechanism of host immune dysregulation and thus hold potential as biomarkers and/or therapeutic agents with clinical application. Hence, further analyses of their altered expression in COVID-19 is warranted. An important basis for this is identifying appropriate reference genes for high quality expression analysis studies. In the current report, NanoString technology was used to study the expression of 798 miRNAs in the peripheral blood of 24 critically ill patients, 12 had COVID-19 and 12 were COVID-19 negative. A list of potentially stable candidate reference genes was generated that included ten miRNAs. The top six were analyzed using reverse transcription quantitative polymerase chain reaction (RT-qPCR) in a total of 41 patients so as to apply standard computational algorithms for validating reference genes, namely geNorm, NormFinder, BestKeeper and RefFinder. There was general agreement among all four algorithms in the ranking of four stable miRNAs: miR-186-5p, miR-148b-3p, miR-194-5p and miR-448. A detailed analysis of their output rankings led to the conclusion that miR-186-5p and miR-148b-3p are appropriate reference genes for miRNA expression studies using PaxGene tubes in the peripheral blood of patients critically ill with COVID-19 disease.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".