NMR-based metabolomics reveals compartmental metabolic heterogeneity in liver of Arctic char (<i>Salvelinus</i> <i>alpinus</i>)
Bibliographic record
Abstract
Metabolomics involves systematic study of low-molecular-mass metabolites in cells, tissues, or biofluids and is nowadays widely applied to characterize the physiological status of aquatic organisms under a set of conditions, such as disease and toxin exposure. Liver, an important metabolic center in the fish body, is often used for metabolomics analysis. Compared with the whole fish liver, the proportion of liver sample needed for metabolomics analysis is relatively small. The homogeneity of metabolites in liver is thus an important issue, especially for comparative studies and biomarker discovery. This study examined the homogeneity of the metabolic profile in liver of Arctic char (Salvelinus alpinus (L., 1758)) using a NMR-based metabolomics approach. For the analysis, whole liver samples were cut into four parts along the direction of gall bladder and at right angles to this, and metabolites in each part of the liver were extracted and analyzed by multivariate and univariate data analyses. Although the multivariate model was not significant due to variation within the data, the metabolic differences in polar portion of liver extract between the parts were seen, indicating non-homogeneity of Arctic char liver. Therefore, when sampling fish liver for further metabolomics studies, this heterogeneity should be taken into consideration.
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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 | 0.000 | 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 | 0.000 |
| 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".