Activation of Complement in the Central Nervous System
Bibliographic record
Abstract
The complement system is an essential effector of the humoral and cellular immunity involved in cytolysis and immune/inflammatory responses. Complement participates in host defense against pathogens by triggering the formation of the membrane attack complex. Complement opsonins (C1q, C3b, and iC3b) interact with surface complement receptors to promote phagocytosis, whereas complement anaphylatoxins C3a and C5a initiate local inflammatory responses that ultimately contribute to the protection and healing of the host. However, activation of complement to an inappropriate extent has been proposed to promote tissue injury. There is now compelling evidence that complement activation in the brain is a double-edged sword in that it can exert beneficial or detrimental effects depending on the pathophysiological context. This review focuses on the roles of the complement system in the pathogenesis of acute brain injury (cerebral ischemia and trauma) and chronic neurodegeneration (Alzheimer's disease). Because many effects of the complement appear to promote neuronal survival and tissue remodeling, directing activation of the complement system in the brain may provide a better therapeutic rationale than inhibiting it.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".