Treatment with an anti-CD11d integrin antibody reduces neuroinflammation and improves outcome in a rat model of repeated concussion
Bibliographic record
Abstract
BACKGROUND: Concussions account for the majority of traumatic brain injuries (TBI) and can result in cumulative damage, neurodegeneration, and chronic neurological abnormalities. The underlying mechanisms of these detrimental effects remain poorly understood and there are presently no specific treatments for concussions. Neuroinflammation is a major contributor to secondary damage following more severe TBI, and recent findings from our laboratory suggest it may be involved in the cumulative properties of repeated concussion. We previously found that an anti-CD11d monoclonal antibody that blocks the CD11d/CD18 integrin and adhesion molecule interaction following severe experimental TBI reduces neuroinflammation, oxidative activity, and tissue damage, and improves functional recovery. As similar processes may be involved in repeated concussion, here we studied the effects of the anti-CD11d treatment in a rat model of repeated concussion. METHODS: Rats were treated 2 h and 24 h after each of three repeated mild lateral fluid percussion injuries with either the CD11d antibody or an isotype-matched control antibody, 1B7. Injuries were separated by a five-day inter-injury interval. After the final treatment and either an acute (24 to 72 h post-injury) or chronic (8 weeks post-injury) recovery period had elapsed, behavioral and pathological outcomes were examined. RESULTS: The anti-CD11d treatment reduced neutrophil and macrophage levels in the injured brain with concomitant reductions in lipid peroxidation, astrocyte activation, amyloid precursor protein accumulation, and neuronal loss. The anti-CD11d treatment also improved outcome on tasks of cognition, sensorimotor ability, and anxiety. CONCLUSIONS: These findings demonstrate that reducing inflammation after repeated mild brain injury in rats leads to improved behavioral outcomes and that the anti-CD11d treatment may be a viable therapy to improve post-concussion outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".