Research note: methodology for high-quality RNA extraction from poultry whole blood for further gene expression analysis
Bibliographic record
Abstract
1. There are no published methods for RNA isolation from avian whole blood where nucleated red blood cells prevent the use of established mammalian protocols. The aim of this study was therefore to develop a protocol for total RNA extraction using avian whole blood by defining the effect of anticoagulants and sample purification protocols on RNA yield and quality. 2. Blood collections from the cutaneous ulnar or medial metatarsal veins of birds yielded adequate blood volume (2-3 ml) draws. The experiment was a 2 × 2 × 3 factorial arrangement of treatments, with two levels of DNase (0 and TURBO DNA-free Kit), two levels of Cleanup (0 and RNeasy MinElute Cleanup Kit), and three anticoagulants (no anticoagulant, EDTA, or sodium citrate). 3. RNA was isolated successfully by adding TRIzol LS to 0.25 ml of chicken whole blood at 3:1 ratio. From 0.125 ml of avian whole blood, 2-3 µg of RNA with RNA integrity number values of 7.75 was successfully isolated with the TRIzol LS extraction and an RNeasy MinElute Cleanup Kit. 4. This reliable protocol can be used to extract high yield and quality of total RNA from a small amount of whole blood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.047 |
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".