Enzymatic validation of species‐specific protocols for metal subcellular fractionation in freshwater animals
Bibliographic record
Abstract
Abstract The use of fractionation protocols to determine metal subcellular distribution in aquatic organisms has gained much interest over the last 15 yr, however, accurate separations among the different components of cells are challenging. Subcellular fractions separated with such an approach are operationally defined and a potentially significant difference can exist between anticipated and resulting fractions. This study customizes and validates subcellular partitioning protocols, for three different freshwater organisms representing a diversity of challenges for subcellular fractionation:Daphnia magna,Chironomus riparius, and liver ofOncorhynchus mykiss. Several protocols involving different homogenization methods, centrifugation speeds, or conservation conditions were tested, and their efficiencies were assessed using enzymatic biomarker assays. Our work allowed us to identify critical steps to improve separations. First, forD. magna, a crustacean with a reinforced chitinous exoskeleton, the use of a strong homogenization method using a sonicator is necessary. Second, for both invertebrates, we observed the leaking of the mitochondrial matrix during cell fractionation, regardless of the homogenization strength and conservation conditions. Therefore, we propose that the mitochondria fraction should be referred to as the mitochondrial membrane fraction, and the cytosol fraction should be identified as the cytosol and mitochondrial matrix fraction. Third, the presence of a lipid‐rich layer duringO. mykissliver fractionation may lead to an overlap between mitochondria and cytosol and must be considered in the protocol development. Finally, lysosomes should not be pooled with the microsomes fraction without prior validation. Overall, this study provides a benchmark for future methodological studies on similar taxa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".