Quality of Thrombin Produced From the Patient’s Own Plasma Using the TPD™, a New Thrombin-Processing Device
Bibliographic record
Abstract
Thrombin derived from bovine sources commonly is used to arrest bleeding during surgical procedures. However, complications such as postoperative hemorrhage can occur because of the development of cross-reactive anti-bovine antibodies that inhibit human coagulation factor V. It would thus be advantageous to develop techniques to generate human thrombin. This study evaluated thrombin produced from human plasma using a new Thrombin-Processing Device (TPD™). Plasma was introduced into the TPD, mixed with an ethanol/CaCl2 reagent, incubated for 1 h, and the harvested thrombin was assayed for activity and the ability to activate platelets by in vitro assays. TPD-produced thrombin activity was found to be 51.8 ± 12.4 IU/mL (n = 145). TPD-produced thrombin also stimulated P-selectin (CD62) expression (83 ± 13% of the platelet population) and Annexin V binding (10.3 ± 2% of the platelet population) on platelets in a similar fashion to commercial thrombin (P-selectin expression: 88 ± 3%; Annexin-V binding: 11.4 ± 3%). Compared with CaCl2 and batroxobin, TPDproduced thrombin had a significantly greater ability to activate platelets. TPD-produced thrombin from human plasma has consistent activity and significantly activates platelets and, thus, may have attractive applications such as the production of autologous thrombin for surgical patients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".