Metabolomics: The Principles and Potential Applications to Transplantation
Bibliographic record
Abstract
This review provides a summary of the applications and potential applications of metabolite profiling (i.e. metabolomics) in monitoring organ transplants. While the concept of metabolomics is relatively new to organ transplantation, the idea of measuring metabolites as a quick, noninvasive probe of organ function is not. Indeed, metabolite measurements of serum creatinine have long been used to assess pre- and post-operative organ function. Over the past 10 years, a number of lesser-known, organ-specific metabolites have also been shown to be good diagnostic indicators of both organ function and viability. In general, metabolomics offers a complementary picture to what can be revealed via techniques based on genomics, proteomics or histology. Because metabolic changes typically happen within seconds or minutes after an 'event', whereas some transcript, protein abundance or tissue changes may take place over days or weeks, metabolomic measurements may offer a particularly useful and inexpensive diagnostic tool to monitor donor organ viability or to detect organ rejection. The excitement associated with metabolomics, however, must be tempered by the fact that the technology for rapid metabolite identification is still in its infancy, and that metabolites are but one part of a very complex picture pertaining to organ function.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".