Bibliographic record
Abstract
PURPOSE OF REVIEW: The success of any given kidney transplant is closely tied to the ability to monitor patients and responsively change their medications. Transplant monitoring is still, however, dependent on relatively old technologies: serum creatinine levels, urine output, blood pressure, blood glucose and histopathology of biopsy samples. These older technologies do not offer sufficient specificity, sensitivity, or accuracy to allow appropriate and timely interventions. Using the tools of genomics, proteomics and metabolomics new biomarkers are being found that may greatly improve transplant monitoring and significantly enhance graft survival. This review describes the basic principles of metabolomics and summarizes a number of recent developments in the use of metabolite biomarkers and metabolomics to monitor kidney transplants. RECENT FINDINGS: Changes in the concentration profiles of a number of small molecule metabolites found in either blood or urine can be used to localize organ damage, identify organs at risk of rejection, assess organs suffering from ischemia-repurfusion injury or identify organs that have been damaged by immunosuppressive drugs. SUMMARY: The application of metabolomics to kidney transplant monitoring is still very much in its infancy. Nevertheless, there are a number of easily measured metabolites in both urine and serum that can provide reliable indications of organ function, organ injury, and immunosuppressive drug toxicity. As the field matures, metabolomics may eventually lead to the development of rapid, inexpensive and noninvasive approaches to assist clinicians in monitoring kidney transplants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".