Preparation and characterization of human thrombin for use in a fibrin glue
Bibliographic record
Abstract
Cryoprecipitate is frequently combined with thrombin to produce a fibrin sealant to enhance haemostasis during surgical procedures. We evaluated the thrombin produced from plasma using the Thrombin Processing Device (TPD)trade mark (Thermogenesis, Rancho Cordova, CA, USA). Plasma (250 mL) was processed in the CryoSeal FS System using the CP-3 disposable to produce cryoprecipitate by automated freezing and thawing. Simultaneously, thrombin was generated using the attached TPD. The cryoprecipitate and thrombin were harvested after approximately 50 min and then frozen and stored at -80 degrees C until analysis of total protein, fibrinogen, factor VIII (FVIII) activity, von Willebrand factor (vWF) and thrombin activity. Sodium dodecyl sulphate (SDS) gel electrophoresis was used to compare thrombin. After combining the thrombin with cryoprecipitate, the rate of clot initiation and strength was measured using a Thromboelastograph (TEG) (Haemoscope Corp, Skokie, IL, USA). Cryoprecipitate was produced, with a fibrinogen concentration of 22 +/- 7.7 g L(-1) (20 +/- 2% recovery), FVIII activity of 14.2 +/- 4.0 IU mL(-1) and vWF of 19.9 +/- 5.2 IU mL(-1). The separate thrombin product had a concentration of 64.3 +/- 16.7 IU mL(-1) of thrombin and a total protein of 0.39 +/- 0.1 g, with SDS gel electrophoresis showing a major band at 37 kD, as did the commercial human thrombin. The TEG curves of cryoprecipitate and TPD-produced or commercial thrombin were compared. The R values (time to clot initiation) were somewhat slower with the TPD-produced thrombin, but the maximum strength (MA) of the clots was similar. In conclusion, human thrombin can be produced during automated cryoprecipitate production. This thrombin is in sufficient concentration to initiate clotting and cross-linking of fibrin from cryoprecipitate to produce an entirely autologous fibrin glue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".