Ultraviolet-Based Pathogen Inactivation Systems: Untangling the Molecular Targets Activated in Platelets
Bibliographic record
Abstract
Transfusions of platelets are an important cornerstone of medicine; however, recipients may be subject to risk of adverse events associated with the potential transmission of pathogens, especially bacteria. Pathogen inactivation (PI) technologies based on ultraviolet illumination have been developed in the last decades to mitigate this risk. This review discusses studies of platelet concentrates treated with the current generation of PI technologies to assess their impact on quality, PI capacity, safety, and clinical efficacy. Improved safety seems to come with the cost of reduced platelet functionality, and hence transfusion efficacy. In order to understand these negative impacts in more detail, several molecular analyses have identified signaling pathways linked to platelet function that are altered by PI. Because some of these biochemical alterations are similar to those seen arising in the context of routine platelet storage lesion development occurring during blood bank storage, we lack a complete picture of the contribution of PI treatment to impaired platelet functionality. A model generated using data from currently available publications places the signaling protein kinase p38 as a central player regulating a variety of mechanisms triggered in platelets by PI systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".