Assessment of a subcellular metal partitioning protocol for aquatic invertebrates: preservation, homogenization, and subcellular fractionation
Bibliographic record
Abstract
Measurements of the subcellular partitioning of trace metals have been used to predict assimilation efficiencies for metals between trophic levels and to evaluate metal detoxification mechanisms in aquatic organisms. Using field‐collected larvae of the insect Chaoborus, we assessed the performance of two preservation protocols, three homogenization treatments, and a cell fractionation protocol to determine the best combination of techniques for assessing the subcellular partitioning of Cd, Cu, Ni, and Zn. Preservation of larval samples for either 2 years at −80°C or 1 year at −80°C or −196°C had little influence on the subcellular partitioning of these metals. Using several marker enzymes, i.e., cytochrome C oxidase and citrate synthase for mitochondria, β‐hexosaminidase for lysosomes, and lactate dehydrogenase for the cytosol, we show that optimal and high‐yield homogenization is achieved using a combination of mechanical disruption using a micro‐pestle followed by vortexing and that the subsequent fractionation procedure employing differential centrifugation/NaOH digestion was effective. We conclude that the preservation and homogenization protocols as well as the fractionation procedure that we tested using Chaoborus larvae can serve as a model approach for improving current protocols and for designing protocols for previously untested biological samples.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".