Bibliographic record
Abstract
Quality management of blood products is essential for blood banking. It is influenced by both processing and donor characteristics and assured by monitoring routine in vitro parameters to defined product specifications. However, these measures correlate poorly with the in vivo behavior of transfused platelets and cannot be used to select optimal donors. Since radiolabeled platelet recovery and survival studies are expensive and time consuming, there is an ongoing search for simpler measures that predict platelet transfusion outcomes. We performed a pilot study using semi-qualitative proteomics to assess changes in the platelet protein profile of donors with either acceptable or unacceptable in vivo radiolabeled autologous platelet recovery and survival measurements. Proteins changing during a 9-day storage period included cytoskeletal elements talin, vinculin and moesin as well as signal transduction proteins 14-3-3, RhoGDI and Rap1. Two of nine donations exhibited a decrease in these proteins and poor in vivo platelet recovery and survival whereas the remaining donors showed acceptable platelet recovery and survival and expected protein profiles. Analyses revealed a significant correlation between protein levels of Rap1 and RhoGDI during storage and platelet recovery and survival. This study provides for the first time preliminary data showing evidence of the utility of protein profiling to predict platelet transfusion quality. This article is part of a Special Issue entitled: Integrated omics.
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 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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".