Using skin mucus RNA to study gene expression in pacific bluefin tuna
Bibliographic record
Abstract
The bluefin tuna species have been overfished throughout the world with stocks declining significantly over the past two decades. Studies employing electronic tags describe tunas in extreme ocean environments with depths up to 500 meters and water temperatures ranging from 5 to 33°C. Physiological adaptations such as high cardiac output and endothermy lead tunas to be a good model for studies in cardiac physiology and stress response. However, due to the high cost of tuna maintenance in captivity, studies on tuna physiology and gene expression are rare. Here, we described the use of pacific bluefin tuna ( Thunnus orientalis ) skin mucus as a possible source for RNA and its potential to be used in gene expression studies of fishes. The skin mucus samples were taken from tunas in captivity at the Tuna Research and Conservation Center and stored at −20°C in RNAlater tubes (Invitrogen). Total RNA extraction from the mucus samples were performed using RNAeasy Kit (Qiagen), posterior RNA clean‐up was performed using Ambion Filter Cartridge. The RNA concentration and quality was spectrophotometrically analyzed by A 260 /A 280 ratio and from agarose gel electrophoresis. Reverse transcription of RNA from mucus samples into cDNA was performed using iScript cDNA kit (Bio‐Rad). PCR products from mucus cDNA samples, were successfully amplified using bluefin tuna gene specific primers (Hsp70; β‐actin; IL‐1β). Our results indicate that bluefin tuna skin mucus is a potential source of RNA for gene expression studies in fishes. Funding: NOAA, MBA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".