Using a Chemical Biopsy for Graft Quality Assessment
Bibliographic record
Abstract
Kidney transplantation is a life-saving treatment for a large number of people with end-stage renal dysfunction worldwide. The procedure is associated with an increased survival rate and greater quality of patient's life when compared to conventional dialysis. Regrettably, transplantology suffers from a lack of reliable methods for organ quality assessment. Standard diagnostic techniques are limited to macroscopic appearance inspection or invasive tissue biopsy, which do not provide comprehensive information about the graft. The proposed protocol aims to introduce solid phase microextraction (SPME) as an ideal analytical method for comprehensive metabolomics and lipidomic analysis of all low molecular compounds present in kidneys allocated for transplantation. The small size of the SPME probe enables performance of a chemical biopsy, which enables extraction of metabolites directly from the organ without any tissue collection. The minimum invasiveness of the method permits execution of multiple analyses over time: directly after organ harvesting, during its preservation, and immediately after revascularization at the recipient's body. It is hypothesized that the combination of this novel sampling method with a high-resolution mass spectrometer will allow for discrimination of a set of characteristic compounds that could serve as biological markers of graft quality and indicators of possible development of organ dysfunction.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".