Interactions of fibrinolytic system proteins with lysine‐containing surfaces
Bibliographic record
Abstract
Studies on the interactions of tissue plasminogen activator (tPA) and plasminogen with polyurethane surfaces containing epsilon-lysine moieties (epsilon-amino group free) are reported. These surfaces are considered to have the potential to dissolve nascent clots that may be formed on them. For adsorption from both single protein solutions and plasma, the surfaces were found to have a high capacity for tPA as well as plasminogen. A significant fraction of preadsorbed tPA was displaced from the epsilon-lysine surfaces upon contact with plasma. These surfaces, when preadsorbed with tPA and then incubated with plasma, were able to dissolve incipient clots formed around them. However, the clot-dissolving capacity diminished as the time of plasma incubation increased, presumably due to loss of tPA. It was also shown that in plasma, preadsorbed tPA is displaced from these surfaces largely by plasminogen, which thus appears to have a greater binding affinity than tPA for the epsilon-lysine moieties. Finally, it was found that in plasma, the epsilon-lysine surfaces interact with plasminogen in a dynamic manner, and that about 70% of the bound plasminogen is exchanging continuously with plasminogen in the plasma.
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 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 | 0.000 |
| 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) | 0.001 | 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".