Persistent dysmorphology and dysfunction of the brain microvasculature following repeated trauma
Bibliographic record
Abstract
Mild traumatic brain injury typically produces no abnormalities on neuroimaging yet elicits symptoms that, in an increasing fraction of survivors, linger for years, particularly with recurring injuries. To elucidate the underlying biological substrates, we leveraged two-photon fluorescence microscopy resonant scanning and our recently developed deep-learning based pipeline to evaluate the cerebrovascular network in the subacute stage following three intact-skull cortical impacts in adult mice under low isoflurane. Microvascular volume density increased by 19.1 ± 18.6% after moderate impacts, while mean capillary diameters increased by 5.6 ± 3.2% for mild and by 1.1 ± 2.6% for moderate impact series; consistent with network remodeling and tone dysregulation, which limits cerebrovascular reserve. Mild impacts cohort showed a paradoxical hypercapnia response with decreases in red blood cell velocities (vRBC) (-20 ± 13%) in the majority (62%) of cortical penetrating arteries and a net ipsicontusional hypercapnia-induced arteriolar vRBC decrease, in stark contrast to the physiologically normal increase seen in sham mice (+27 ± 15%). Downstream, the mild impacts resulted in attenuation of hypercapnia-induced cortical capillaries' flow increase while the moderate impacts cohort showed no ipsicontusional capillary flow response. Sustained aberrations in brain microvasculature post-trauma may underlie the persistence of symptoms and mediate susceptibility to further injury.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".