OA4‐AM23‐MN‐13 | Post‐Transfusion Laboratory Parameters of Patients Transfused with Low Volume Red Blood Cell Products
Bibliographic record
Abstract
different blood centers across the United States as part of the Recipient Epidemiology and Donor Evaluation Study-REDS RBC Omics.These donors were asked to donate a second unit of packed red blood cells, which was stored for up to 42 days.Ultrahigh pressure liquid chromatography coupled to mass metabolomics was used to investigate the metabolic phenotypes of end of storage samples from both donations, for a total of 1286 samples evaluated at the metabolic level in this study.Results/Findings: Of over 200 endogenous metabolites monitored in this study, 170 (85%) were significantly correlated (p < .05Spearman) between two independent donations from the same donor.Of these, 30 metabolites showed rho >0.5 (p-values <1 E-35), suggesting strong cross-donation reproducibility.This group was enriched for small molecules in the carnitine synthesis and metabolic pathway (L-carnitine-rho: 0.73, p = 1.98 E-109; and its precursors, methyl-lysine rho: 0.83, p = 1.68 E-164; and trimethyl-lysine rho: 0.67, p=7.4 E-84; and short-chain acyl-carnitines C4, C5-OH, C5), choline, urate, spermine, creatine, spermine, sphingosine 1-phosphate.Conclusions: Many metabolite levels are reproducible within the same donor across multiple donations.Our study identifies carnitine metabolism, one-carbon metabolism, urate, sphingosine 1-phosphate as key pathways that are strongly linked to donor biology and stable over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".