Inhibition of the Membrane Attack Complex of the Complement System Reduces Secondary Neuroaxonal Loss and Promotes Neurologic Recovery after Traumatic Brain Injury in Mice
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the leading cause of disability and death in young adults. The secondary neuroinflammation and neuronal damage that follows the primary mechanical injury is an important cause of disability in affected people. The membrane attack complex (MAC) of the complement system is detected in the traumatized brain early after TBI; however, its role in the pathology and neurologic outcome of TBI has not yet been investigated. We generated a C6 antisense oligonucleotide that blocks MAC formation by inhibiting C6, and we compared its therapeutic effect to that of Ornithodoros moubata complement inhibitor (OmCI), a known inhibitor of C5 activation that blocks generation of the anaphylatoxin C5a and C5b, an essential component of MAC. Severe closed head injury in mice induced abundant MAC deposition in the brain. Treatment with C6 antisense reduced C6 synthesis (85%) and serum levels (90%), and inhibited MAC deposition in the injured brain (91-96%). Treatment also reduced accumulation of microglia/macrophages (50-88%), neuronal apoptosis, axonal loss and weight loss (54-93%), and enhanced neurologic performance (84-92%) compared with placebo-treated controls after injury. These data provide the first evidence, to our knowledge, that inhibition of MAC formation in otherwise complement-sufficient animals reduces neuropathology and promotes neurologic recovery after TBI. Given the importance of maintaining a functional complement opsonization system to fight infections, a critical complication in TBI patients, inhibition of the MAC should be considered to reduce posttraumatic neurologic damage. This work identifies a novel therapeutic target for TBI and will guide the development of new therapy for patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".