Optimizing Serum RNA Isolation: A Comparative Analysis of Commercial Kits for Yield, Purity, and Contamination Control
Bibliographic record
Abstract
Objective: Isolation of RNA from serum samples has gained importance, especially in studies on the use of small RNA molecules such as miRNA as biomarkers. Selection of the optimal kit is critical for the accuracy of downstream processing. The aim of this study was to compare the performance of different commercial kits in terms of efficiency, RNA purity and contamination control during the isolation process. Methods: Three different RNA isolation kits were used for 5 serum samples: 1.miRNeasy Serum/Plasma Kit (Cat. No: 217184, Qiagen, USA), 2.Norgen Plasma/serum RNA purification kit (Cat. No: 55000, Norgen, Canada), 3.Nucleogene RNA isolation kit (Cat. No: NG044, Nucleogene, Turkey). The purity and intensity of the obtained RNAs were evaluated by measuring A260/280 ratios with a nanodrop spectrophotometer. Results: When the concentrations and A260/280 ratios obtained from the kits were evaluated by One Way Anova Test using GraphPad Prism (V10.4.0), it was observed that there was a statistically significant difference between the concentrations and A260/280 ratios of the 3 kits (p≤0.05 and p≤ 0.001). RNAs obtained from Norgene had the lowest concentration and the lowest A260/280 ratio, while Nucleogene had the highest RNA concentration and A260/280 ratio of 2 and above among the three kits (p≤0.05).
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".