Thromboelastography testing in mice following blood collection from facial vein and cardiac puncture
Bibliographic record
Abstract
: Blood collection is critical for mouse research studies particularly in hemostatic testing. Cardiac puncture; a standard effective method requires anesthesia and is a terminal procedure while facial vein technique allows multiple collections. Thromboelastography (TEG) is a global hemostasis test, provides a dynamic real-time picture of coagulation. However, TEG experiments in mice require large number of animals and may not allow pre/postinterventions assessment. In this study, we aimed to investigate the feasibility of facial vein sampling for TEG analysis as an alternative to cardiac puncture and examined the impact on coagulation results. Blood samples were obtained from a total of 10 C57BL/6 and CD-1 mice via cardiac puncture and a total of another eight mice of similar strains via facial vein sampling. We compared TEG parameters in both methods using descriptive statistics and the Student t test. Results show no significant difference in any of the TEG parameters between cardiac and facial vein blood indicating the two methods are comparable. Facial vein sampling provides a less costly alternative to cardiac puncture. It is a suitable blood collection method for pre/postinterventions or follow-up studies and it better addresses reduction and refinement goals in mouse studies. A larger study to evaluate the sex or strain and genetic background differences will be valuable.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".