<scp>p38MAPK</scp> is involved in apoptosis development in apheresis platelet concentrates after riboflavin and ultraviolet light treatment
Bibliographic record
Abstract
BACKGROUND: Pathogen inactivation (PI) accelerates the platelet (PLT) storage lesion, including apoptotic-like changes. Proteomic studies have shown that phosphorylation levels of several kinases increase in PLTs after riboflavin and UV light (RF-PI) treatment. Inhibition of p38MAPK improved in vitro PLT quality, but the biochemical basis of this kinase's contribution to PLT damage requires further analysis. STUDY DESIGN AND METHODS: In a pool-and-split design, apheresis PLT concentrates were either treated or kept untreated with or without selected kinase inhibitors. Samples were analyzed throughout 7 days of storage, monitoring in vitro quality variables including phosphatidylserine exposure, degranulation, and glucose metabolism. Changes in the protein expression of Bax, Bak, and Bcl-xL and the activities of caspase-3 and -9 were determined by immunoblot analysis and flow cytometry, respectively. RESULTS: The expression levels of the proapoptotic proteins Bax and Bak, but not the antiapoptotic protein Bcl-xL, were significantly increased after the RF-PI treatment. This trend was reversed in the presence of p38MAPK inhibitor SB203580. As a result of increasing proapoptotic protein levels, caspase-3 and -9 activities were significantly increased in RF-PI treatment during storage compared with control (p < 0.05). Similarly, p38MAPK inhibition significantly reduced these caspase activities compared with vehicle control after RF-PI treatment (p < 0.05). CONCLUSION: These findings revealed that p38MAPK is involved in signaling leading to apoptosis triggered by RF-PI. Elucidation of the biochemical processes influenced by PI is a necessary step in the development of strategies to improve the PLT quality and ameliorate the negative effects of PI treatment.
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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 | 0.000 |
| 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 | 0.000 |
| 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".