The therapeutic relevance of the Kallikrein-Kinin axis in SARS-cov-2-induced vascular pathology
Bibliographic record
Abstract
attachment to the angiotensin-converting enzyme 2 (ACE2) receptor. ACE2 is widely expressed in a multitude of tissues, including the lung (alveolar cells), heart, intestine, kidney, testis, gallbladder, vasculature (endothelial cells), and immune cells. Interference in ACE2 signaling could drive the aforementioned systemic pathologies, such as endothelial dysfunction, microthromboses, and systemic inflammation, that are typically seen in patients with severe COVID-19. ACE2 is a component of the renin-angiotensin system (RAS) and is intimately associated with the plasma kallikrein-kinin system (KKS). As many papers are published on the role of ACE and ACE2 in COVID-19, we will review the role of bradykinin, and more broadly the KSS, in SARS-CoV-2-induced vascular dysfunction. Furthermore, we will discuss the possible therapeutic interventions that are approved and in development for the following targets: coagulation factor XII (FXII), tissue kallikrein (KLK1), plasma kallikrein (KLKB1), bradykinin (BK), plasminogen activator inhibitor (PAI-1), bradykinin B1 receptor (BKB1R), bradykinin B2 receptor (BKB2R), ACE, furin, and the NLRP3 inflammasome. Understanding these targets may prove of value in the treatment of COVID-19 as well as in other virus-induced coagulopathies in the future.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".