Carnitine Profile Changes in Pediatric Hematopoietic Stem Cell Transplant: New Role for Carnitine?
Bibliographic record
Abstract
Carnitine is an essential cofactor for mitochondrial import and oxidation of fatty acids. High-dose chemotherapy and radiation, often required for hematopoietic stem cell transplant (HSCT), leads to tissue damage, mitochondrial dysfunction, and alterations in carnitine metabolism. The aim of this pilot cohort study was to describe plasma and urinary carnitine profiles during pediatric HSCT and their relationships with clinical outcomes. Plasma and urinary carnitine samples were collected from 22 pediatric patients before and through day 180 post-HSCT. Associations were observed between graft-versus-host disease and an elevated plasma total carnitine (P=0.019), and also increased plasma acyl:free carnitine ratio with veno-occlusive disease (P=0.016). Mortality was observed in those with their highest urinary total carnitine losses on day 0 (P=0.005), and in those with an abnormal day 28 plasma ratio either above or below the reference range (P=0.007). Changes in carnitine profiles were more reflective of metabolic stress and negative outcomes than of inadequate dietary intake. Associations observed direct larger studies to assess the validity of carnitine profiles as a prognostic indicator and also to assess whether prophylactic carnitine supplementation pre-HSCT could reduce mitochondrial injury and urinary losses and help mitigate inflammatory and metabolic comorbidities of HSCT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".