Impairment of cerebrovascular reactivity in response to hypercapnic challenge in a mouse model of repetitive mild traumatic brain injury
Bibliographic record
Abstract
Incidences of repetitive mild TBI (r-mTBI), like those sustained by contact sports athletes and military personnel, are thought to be a risk factor for development of neurodegenerative disorders. Those suffering from chronic TBI-related illness demonstrate deficits in cerebrovascular reactivity (CVR), the ability of the cerebral vasculature to respond to a vasoactive stimulus. CVR is thus an important measure of traumatic cerebral vascular injury (TCVI), and a possible in vivo endophenotype of TBI-related neuropathogenesis. We combined laser speckle imaging of CVR in response to hypercapnic challenge with neurobehavioral assessment of learning and memory, to investigate if decreased cerebrovascular responsiveness underlies impaired cognitive function in our mouse model of chronic r-mTBI. We demonstrate a profile of blunted hypercapnia-evoked CVR in the cortices of r-mTBI mice like that of human TBI, alongside sustained memory and learning impairment, without biochemical or immunohistopathological signs of cerebral vessel laminar or endothelium constituent loss. Transient decreased expression of alpha smooth muscle actin and platelet-derived growth factor receptor β, indicative of TCVI, is obvious only at the time of the most pronounced CVR deficit. These findings implicate CVR as a valid preclinical measure of TCVI, perhaps useful for developing therapies targeting TCVI after recurrent mild head trauma.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".