Bibliographic record
Abstract
Here we introduce a method for accurate and sensitive quantitative analysis of mRNA, which does not require calibration with mRNA. The method uses a fluorescently labeled hybridization probe as a reference standard. It involves the following: (i) annealing mRNA to the excess of the fluorescently labeled ssDNA hybridization probe, (ii) separation of the mRNA-probe hybrid from the excess of the probe by gel-free capillary electrophoresis mediated by ssDNA-binding protein, (iii) fluorescence detection of the hybrid and the excess probe, and (iv) quantification of mRNA using a simple algebraic formula. The method also overcomes a number of other limitations of conventional methods: the entire procedure currently takes only 2 h and accurately quantifies 10(5) copies of mRNA. With further improvements to the method, the procedure can be potentially shortened to 10 min, and the limit of quantification can be decreased to as few as 100 copies of mRNA. In this work, we prove the principle of the method by quantifying mRNA of green fluorescent protein in the matrix of total cellular RNA. The developed method is quantitative, simple, fast, and highly sensitive. It requires commercially available instrumentation only. The method will be an indispensable tool for molecular and cell biology studies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".