Acute Tissue Damage After Injections of Thrombin and Plasmin into Rat Striatum
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Extravasation of blood is associated with intracerebral hemorrhage and head trauma. The mechanism of brain cell injury associated with hemorrhage differs from that due to pure ischemia. The purpose of this study was to investigate the acute changes after intracerebral injections of proteins that are involved in blood clotting and clot lysis. METHODS: Sixty-eight adult rats were subjected to stereotaxic intrastriatal injections of normal saline (5 microL), low- (2.5 U/5 microL) and high-dose (25 U/5 microL) thrombin, low- (0.1 microgram/5 microL) and high-dose (1 microgram/5 microL) tissue plasminogen activator, low- (0.05 U/5 microL) and high-dose (0.5 U/5 microL) plasminogen, and low- (0.335 U/5 microL) and high-dose (3.35 U/5 microL) plasmin. Forty-eight hours later rats were perfusion fixed. Brain damage area, eosinophilic neurons, terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-biotin nick end labeling (TUNEL)-positive cells, infiltrating neutrophils, CD8a immunoreactive leukocytes, and reactive microglia were quantified. RESULTS: Damage area in striatum, dying cells, inflammatory cells, and microglial reaction were significantly greater after the high-dose plasminogen, plasmin, and thrombin injections. Tissue plasminogen activator injections were associated with mild inflammation. CONCLUSIONS: These results suggested that thrombin and plasmin are harmful to brain cells in vivo. Although the doses required to cause damage are relatively great in consideration of the plasma content of these proteins, their pathological effect might be enhanced through synergism with other mechanisms.
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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 | 0.000 |
| 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".