Evidence for vascular microbleeds in brains following repeated mild traumatic brain injury
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) is produced by the rapid movement of the brain within the skull. It is believed that each mTBI leaves the brain in a vulnerable state to subsequent injuries (repeated‐mTBI; r‐mTBI), and can precipitate more serious neurological conditions. However, a mechanism responsible for this vulnerable state is currently lacking, and the mTBI process itself remains poorly understood. The current experiments used an Awake Closed Head Injury (ACHI) model we have recently developed to study r‐mTBI. For each ACHI procedure (n = 8 per animal) a neurological assessment was performed to assess basic levels of consciousness, balance, and sensory perception. Our data demonstrate that administering the ACHI protocol 8 times produces robust, but non‐cumulative neurological deficits. One day following mTBI, rats were perfusion fixed, vibratome sliced (50 μm), and processed for immunohistochemistry. Confocal and light microscopic imaging revealed that r‐mTBI is accompanied by diffuse microbleeds in cortical and subcortical regions that ranged in size from 20 to 100 μm. These microbleeds were characterized by the presence of red blood cells and the blood protein, fibrinogen, in the surrounding perivascular space. The parenchyma around these microbleeds was positive for reactive and phagocytic microglia. In addition, the brains of animals following r‐mTBI were unique in that they also contained ‘rod’ type microglia, similar to those normally observed in Alzeheimer‐like dementias. These observations indicate that repeated concussions produce ruptures in small blood vessels in the brain and these likely contribute to both immediate and long‐term cognitive deficits. Support or Funding Information CIHR, NSERC
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".