Significant Differences in Neutralization of Heparin and Its Low Molecular Fragments by Protamine Sulfate: An In Vivo Study.
Bibliographic record
Abstract
Abstract In clinical practice, patients receiving low molecular weight heparin (LMWH) occasionally suffer bleeding. Protamine sulphate (PS) is often used to reverse the anticoagulant effect of LMWH in such cases. However, the optimal regimen of PS for complete neutralization of LMWH fragments has not been established. Results from our previous in vitro studies indicate that the ability of PS to neutralize LMWHs is inversely related to the charge of the low molecular weight heparin molecule; more heavily charged LMWHs (such as Tinzaparin) are more readily neutralized than less charged LMWHs (such as Enoxaparin). The aim of the current study was to confirm these findings using an in vivo model. Twenty minutes after administration of either saline, unfractionated heparin [UFH, 100U/kg], Tinzaparin [100U/kg] or enoxaparin [100U/kg], 50% of anesthetized rabbits received either saline or PS [1 mg/100 U of heparin or LMWH]. The efficacy of PS neutralization was assessed by serial measurements of anti-factor Xa heparin plasma levels. Results are presented as mean of the anti-factor Xa heparin activities normalized to the level at 10 minutes and summarized in the table below. As expected, PS completely neutralized the anti-factor Xa effect of UFH. However, PS was significantly less effective for neutralization of Tinzaparin (about 66%) and Enoxaparin (about 44%) at the dose tested. We conclude that when tested in an in vivo model LMWHs vary in their protamine neutralizability. More highly charged LMWHs (e.g. Tinazaparin) are more neutralizable than less highly charged products (e.g. Enoxaparin). Residual Anti-Xa heparin effect Time Enoxaparin Tinzaparin UFH Saline + PS Enoxaparin +PS Tinzaparin + PS UFH + PS 10 min 1.00 1.00 1.00 0 1.00 1.00 1.00 20 min 0.85 0.86 0.70 0 0.80 0.85 0.89 Protamine 25 min 0.71 0.75 0.69 0 0.45 0.29 0.01 35 min 0.68 0.64 0.48 0 0.40 0.28 0 50 min 0.48 0.32
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".