In Vivo Dynamic Real-Time Monitoring and Quantification of Platelet-Thrombus Formation
Bibliographic record
Abstract
Current methods for monitoring thrombosis and thrombus growth are invasive and provide only single-time-point data. Animal models rely mainly on flow changes as a surrogate of thrombus formation. Our aim was to validate a unique potentially noninvasive system to detect and quantify dynamic thrombus formation in vivo by using a porcine model of carotid artery injury. Thrombus growth was monitored by deposition of autologous (111)In-labeled platelet activity over the injured artery by use of miniaturized gamma detectors and Doppler blood flow. Counts were recorded at 2-minute intervals for 2 hours. The technique was validated by comparing standard antithrombotic agents against controls. Platelet recruitment was detected before significant change in flow. Thrombus formation, calculated as the area under the curve (platelets x minutes x 10(6)), was greatest for control animals (11.7+/-1.28), followed by animals treated with aspirin (6.13+/-0.91, P<0.05), heparin (2.45+/-0.34, P<0.05), and hirudin (0.2+/-0.01, P<0.01 compared with heparin). The rate of platelet deposition was assessed as the slope of the curve in the first 30 minutes (platelets x 10(6) per minute) for the following treatment groups of animals: control, 3.53+/-0.34; aspirin, 1.67+/-0. 34 (P<0.01); heparin, 1.55+/-0.3 (P<0.01); and hirudin, 0.25+/-0.03 (P<0.001). There was no statistical difference between heparin and aspirin treatments. Change in flow was assessed as reduction from baseline: control, >99+/-0.34%; aspirin, 39+/-9.1%; heparin, 36+/-12. 5%; and hirudin, 17+/-5.4%. There was no statistical difference between the aspirin- and heparin-treated groups. Morphometric analysis revealed >99+/-0.63% occlusion of the luminal area with thrombus for the control group, 43+/-14.3% for the aspirin-treated group, 30+/-5.6% for the heparin-treated group, and <10+/-1.8% for the hirudin-treated group. Assessment of platelet-thrombus formation with this technique was more sensitive than change in flow in determining antithrombotic efficacy, and thrombus formation was detected earlier. This study validates a new quantitative, sensitive, potentially noninvasive, portable, in vivo monitoring of dynamic thrombus growth, which appears applicable to phase II studies in humans.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".