In Vitro Modeling of Bradykinin-Mediated Angioedema States
Bibliographic record
Abstract
Kinins (peptides related to bradykinin, BK) are formed from circulating substrates, the kininogens, by the action of two proteases, the kallikreins. The only clinical application of a BK receptor ligand, the B2 receptor antagonist icatibant, is the treatment of the rare hereditary angioedema (HAE) caused by the deficiency of C1-esterase inhibitor (C1-INH). Less common forms of HAE (genetic variants of factor XII, plasminogen, kininogen) are presumably mediated by increased BK formation. Acquired forms of BK-mediated angioedema, such as that associated with angiotensin-I converting enzyme (ACE) inhibition, are also known. Antibody-based analytical techniques are briefly reviewed, and support that kinins are extremely short-lived, prominently cleared by ACE. Despite evidence of continuous activation of the kallikrein–kinin system in HAE, patients are not symptomatic most of the time and their blood or plasma obtained during remission does not generate excessive immunoreactive BK (iBK), suggesting effective homeostatic mechanisms. HAE-C1-INH and HAE-FXII plasmas are both hyperresponsive to fibrinolysis activation. On another hand, we suggested a role for the alternate tissue kallikrein–kinin system in patients with a plasminogen mutation. The role of the BK B1 receptor is still uncertain in angioedema states. iBK profiles under in vitro stimulation provide fresh insight into the physiopathology of angioedema.
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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) | 0.000 | 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 | 0.001 |
| 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".