Generation of Kinins during Preparation and Storage of Leukoreduced Platelet Concentrates.
Bibliographic record
Abstract
Abstract BACKGROUND. Severe hypotensive reactions can occur following transfusion of blood components, particularly with platelets concentrates (PCs) leukoreduced with negatively- charged filters. Bradykinin (BK)-related peptides were proposed as a possible cause of this side-effect. This study evaluated the effects of leukoreduction and storage conditions on the levels of two kinins (BK and des-Arg9-BK) in PCs. METHODS. Whole blood donations (n=35) were processed using current PRP (platelet-rich plasma) procedure to prepare leukoreduced and unfiltered components by Leukotrap® RC-PL Whole Blood Collection, Filtration and Storage System. PCs and plasma were stored for 7 days at 20–24°C with agitation. Levels of BK and des-Arg9-BK were measured by specific enzyme immunoassays and HPLC at day 0, 2, 5 and 7 of storage. Mechanisms potentially responsible for accumulation of BK and des-Arg9-BK were studied. RESULTS. On day 0, kinins were measured in significantly higher concentrations in leukoreduced (BK: 101 ± 157 pg/mL; des-Arg9-BK: 194 ± 191 pg/mL) vs unfiltered PCs (BK: 71 ± 121 pg/mL; des-Arg9-BK: 98 ± 114 pg/mL). During storage, both kinins peaked on day 5, with concentrations higher than 1 ng/mL in 22% of leukoreduced as well as unfiltered PCs. Physicochemical and pharmacological characterization of immunoreactive kinins confirmed their nature. In vitro activation of the contact system of the corresponding platelet-poor plasma (PPP) showed that a high kinin concentration on day 5 of the storage corresponded to a low kinin-forming capacity of plasma. On day 7, BK was no longer elevated presumably due to its degradation and the depletion of kinin-forming capacity of the plasma in stored PCs. The activity of metallopeptidases that metabolize BK-related peptides in plasma from PCs were at levels similar to those recorded in the plasma of a normal reference population and were unaffected by storage. CONCLUSIONS. Our results indicate that filtration and storage conditions of PCs contribute to generation of pharmacologically relevant bradykinin levels that might pose a risk in susceptible patients. The clinical relevance of such high concentrations of kinins in PCs remains to be established but could potentially be significant especially in patients treated with angiotensin I-converting enzyme-inhibitors that affect the pathway of BK degradation.
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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 | 0.000 |
| 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) | 0.001 | 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".