Liquid chromatography-inductively coupled plasma-based metallomic approaches to probe health-relevant interactions between xenobiotics and mammalian organisms
Bibliographic record
Abstract
In mammals, the transport of essential elements from the gastrointestinal tract to organs is orchestrated by biochemical mechanisms which have evolved over millions of years. The subsequent organ-based assembly of sufficient amounts of metalloproteins is a prerequisite to maintain mammalian health and well-being. The chronic exposure of various human populations to environmentally abundant toxic metals/metalloid compounds and/or the deliberate administration of medicinal drugs, however, can adversely affect these processes which may eventually result in disease. A better understanding of the perturbation of these processes has the potential to advance human health, but their visualization poses a major problem. Nonetheless, liquid chromatography-inductively coupled plasma-based 'metallomics' methods, however, can provide much needed insight. Size-exclusion chromatography-inductively coupled plasma atomic emission spectrometry, for example, can be used to visualize changes that toxic metals/medicinal drugs exert at the metalloprotein level when they are added to plasma in vitro. In addition, size-exclusion chromatography-inductively coupled plasma mass spectrometry can be employed to analyze organs from toxic metal/medicinal drug-exposed organisms for metalloproteins to gain insight into the biochemical changes that are associated with their acute or chronic toxicity. The execution of such studies-from the selection of an appropriate model organism to the generation of accurate analytical data-is littered with potential pitfalls that may result in artifacts. Drawing on recent lessons that were learned by two research groups, this tutorial review is intended to provide relevant information with regard to the experimental design and the practical application of these aforementioned metallomics tools in applied health research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".