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Record W2343765607 · doi:10.1161/circresaha.115.306823

Animal Models of Thrombosis From Zebrafish to Nonhuman Primates

2016· review· en· W2343765607 on OpenAlexaff
Pudur Jagadeeswaran, Brian C. Cooley, Peter L. Gross, Nigel Mackman

Bibliographic record

VenueCirculation Research · 2016
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThrombosisHemostasisZebrafishAntithromboticMedicineNeutrophil extracellular trapsBiologyImmunologyInternal medicineInflammation

Abstract

fetched live from OpenAlex

Thrombosis is a leading cause of morbidity and mortality worldwide. Animal models are used to understand the pathological pathways involved in thrombosis and to test the efficacy and safety of new antithrombotic drugs. In this review, we will first describe the central role a variety of animal models of thrombosis and hemostasis has played in the development of new antiplatelet and anticoagulant drugs. These include the widely used P2Y12 antagonists and the recently developed orally available anticoagulants that directly target factor Xa or thrombin. Next, we will describe the new players, such as polyphosphate, neutrophil extracellular traps, and microparticles, which have been shown to contribute to thrombosis in mouse models, particularly venous thrombosis models. Other mouse studies have demonstrated roles for the factor XIIa and factor XIa in thrombosis. This has spurred the development of strategies to reduce their levels or activities as a new approach for preventing thrombosis. Finally, we will discuss the emergence of zebrafish as a model to study thrombosis and its potential use in the discovery of novel factors involved in thrombosis and hemostasis. Animal models of thrombosis from zebrafish to nonhuman primates are vital in identifying pathological pathways of thrombosis that can be safely targeted with a minimal effect on hemostasis. Future studies should focus on understanding the different triggers of thrombosis and the best drugs to prevent each type of thrombotic event.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.337
GPT teacher head0.476
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations106
Published2016
Admission routes1
Has abstractyes

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