Differences and Compatibility between Human and Porcine Fibrinolytic Components toward Plasmin Generation and Fibrin Degradation
Bibliographic record
Abstract
Fibrinolysis is the process of blood clot breakdown by the enzyme plasmin. Despite increased usage of large animals such as pigs to study fibrinolysis in human disease models, a comprehensive study comparing the human and porcine fibrinolytic factors has not been reported. To directly compare and characterize structural and functional differences between human and porcine fibrinolytic factors. Using human or porcine source of plasminogen, tissue-type plasminogen activator (tPA), and fibrinogen, we investigated how various permutations of the three fibrinolytic factors affect overall plasmin generation. Human or porcine plasmin breakdown of fibrin generated from human or porcine fibrinogen was also investigated using turbidity-based lysis assay and visualized using SDS-PAGE. Primary structures of the various proteins were also compared. All-human components had a 24-fold higher plasmin generation than all-porcine components. Species dependence on plasmin generation was the most dependent on fibrin source, where human fibrin presence led to a 2- to 34-fold higher plasmin generation than porcine fibrin. Porcine plasmin was the better enzyme for human or porcine fibrin breakdown due to a 2.7-fold and 6.7-fold higher kcat, respectively. Peptide sequence analyses show the greatest differences lie in Kringle domain 1 for plasminogen and Kringle domain 2 for tPA, both of which bind fibrin. Fibrinogen chains also show the greatest difference within the αC domain, which has known plasminogen and tPA binding sites. Although similar, there are notable and specific differences between the human and porcine fibrinolytic systems, particularly toward plasmin generation and fibrin breakdown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".