Bleeding is increased in amyloid precursor protein knockout mouse
Bibliographic record
Abstract
BACKGROUND: Amyloid precursor protein (APP) is highly expressed in platelets. APP is the precursor to amyloid beta (Aβ) peptides that accumulate in cerebral amyloid angiopathy and plaques in Alzheimer disease. APP and its metabolites interact with many components of the coagulation system, and have both anticoagulant and procoagulant properties, but it is unclear if APP contributes to hemostasis in vivo. OBJECTIVES: To determine whether APP contributes to hemostasis in mice, including when inhibitors of coagulation are administered. METHODS: Blood loss in APP knockout (KO) mice was measured in liver laceration and tail transection models of hemorrhage. Blood loss was also measured following tail transection in mice given an inhibitor of coagulation factor Xa (apixaban), platelet inhibitors (aspirin + clopidogrel), tissue-type plasminogen activator (t-PA), or the antifibrinolytic tranexamic acid (TXA). RESULTS AND DISCUSSION: Blood loss from liver lacerations was similar between APP KO mice and wild-type (WT) mice, but APP KO mice bled more from tail transections. When mice were challenged with aspirin + clopidogrel, the difference in bleeding between APP KO and WT mice was abrogated. In contrast, a difference in bleeding between the strains persisted when mice were treated with apixaban, t-PA, or TXA. Blood collected from APP KO mice and analyzed with thromboelastography had longer clotting times, and the clots were less stiff and more susceptible to fibrinolysis compared to blood from WT mice. CONCLUSIONS: The absence of APP measurably increases bleeding in mice, which is consistent with a role for platelet-derived APP and Aβ peptides in hemostasis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".