Does Thrombin Play a Role in the Pathogenesis of Brain Damage After Periventricular Hemorrhage?
Bibliographic record
Abstract
Neonatal periventricular hemorrhage (PVH) is a devastating complication of prematurity in the human infant. Based upon observations made primarily in adult rodents and the fact that the immature brain uses proteolytic systems for cell migration and growth, we hypothesized that thrombin and plasmin enzyme activities contribute to the brain damage after PVH. The viability of mixed brain cells derived from newborn rat periventricular region was suppressed by whole blood and thrombin, but not plasmin. Following injection of autologous blood into the periventricular region of newborn rat brain, proteolytic activity was detected in a halo around the hematoma using membrane overlays impregnated with thrombin and plasmin fluorogenic substrates. Two-day old rats received periventricular injection of blood, thrombin, and plasminogen. After 2 days, thrombin and blood were associated with significantly greater damage than saline or plasminogen. Two-day old mice received intracerebral injections of blood in combination with saline or the proteolytic inhibitors hirudin, alpha2macroglobulin, or plasminogen activator inhibitor-1. After 2 days, hirudin significantly reduced brain cell death and inflammation. Two-day-old mice then received low and high doses of hirudin mixed with blood after which behavioral testing was conducted repeatedly. At 10 weeks there was no statistically significant evidence for behavioral or structural brain protection. These results indicate that thrombin likely plays a role in neonatal periventricular brain damage following PVH. However, additional factors are likely important in the recovery from this result.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".