Bibliographic record
Abstract
PURPOSE OF REVIEW: COVID-19 remains a major source of concern, particularly as new variants emerge and with recognition that patients may suffer long-term effects. Mechanisms underlying SARS-CoV-2 mediated organ damage and the associated vascular endotheliopathy remain poorly understood, hindering new drug development. Here, we highlight selected key concepts of how the complement system, a major component of innate immunity that is dysregulated in COVID-19, participates in the thromboinflammatory response and drives the vascular endotheliopathy. RECENT FINDINGS: Recent studies have revealed mechanisms by which complement is activated directly by SARS-CoV-2, and how the system interfaces with other innate thromboinflammatory cellular and proteolytic pathways involving platelets, neutrophils, neutrophil extracellular traps and the coagulation and kallikrein-kinin systems. With this new information, multiple potential sites for therapeutic intervention are being uncovered and evaluated in the clinic. SUMMARY: Infections with SARS-CoV-2 cause damage to the lung alveoli and microvascular endothelium via a process referred to as thromboinflammation. Although not alone in being dysregulated, complement is an early player, prominent in promoting the endotheliopathy and consequential organ damage, either directly and/or via the system's complex interplay with other cellular, molecular and biochemical pathways. Delineating these critical interactions is revealing novel and promising strategies for therapeutic intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".