Bibliographic record
Abstract
Thrombin-specific inhibitors directly diminish thrombin-induced coagulation and cellular activities without the side effects of heparin. Hirudin is the most potent natural thrombin-specific inhibitor. Recombinant hirudins (such as desirudin) have been shown to be effective in the treatment of heparin-induced thrombocytopenia (HIT) and in the prevention of thrombotic complications after hip or knee surgery. The application of recombinant hirudin has been limited mainly by hemorrhagic complications. Synthetic thrombin-specific inhibitors, including oligopeptides, tripeptides and non-peptide low molecular weight (LMW) thrombin inhibitors, have been designed according to their interactions with the active sites of thrombin. Bivalirudin (an anti-thrombin oligopeptide) has been approved for preventing thrombosis in unstable angina patients following angioplasty in adjunct to aspirin. Argotroban (a tripeptide thrombin inhibitor) has been used for the treatment of HIT, peripheral and cerebral thrombotic diseases. The benefit of using thrombin-specific inhibitors alone in acute myocardial infarction or unstable angina remains uncertain. A number of LMW thrombin-specific inhibitors have been developed. Some of them can be administrated orally, and cause less increase in bleeding time than other thrombin inhibitors. The efficacy, safety, stability and oral bioavailability of the thrombin inhibitors may be considerably improved through structural optimization. Most of the LMW thrombin inhibitors are currently being tested in animal models or at early stages of clinical trials. In this review, we will present an overview of recent advances in thrombin-specific inhibitors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".