Treatment with the vascular endothelial growth factor-A antibody, bevacizumab, has sex-specific effects in a rat model of mild traumatic brain injury
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) involves damage to the cerebrovascular system. Vascular endothelial growth factor-A (VEGF-A) is an important modulator of vascular health and VEGF-A promotes the brain's ability to recover after more severe forms of brain injury; however, the role of VEGF-A in mTBI remains poorly understood. Bevacizumab (BEV) is a monoclonal antibody that binds to VEGF-A and neutralises its actions. To better understand the role of VEGF-A in mTBI recovery, this study examined how BEV treatment affected outcomes in rats given a mTBI. Adult Sprague-Dawley rats were assigned to sham-injury + vehicle treatment (VEH), sham-injury + BEV treatment, mTBI + VEH treatment, mTBI + BEV treatment groups. Treatment was administered intracerebroventricularly via a cannula beginning at the time of injury and continuing until the end of the study. Rats underwent behavioral testing after injury and were euthanized on day 11. In both females and males, BEV had a negative impact on cognitive function. mTBI and BEV treatment increased the expression of inflammatory markers in females. In males, BEV treatment altered markers related to hypoxia and vascular health. These novel findings of sex-specific responses to BEV and mTBI provide important insights into the role of VEGF-A in mTBI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".