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Record W2998409789 · doi:10.1080/09537104.2019.1709632

Carbonic Anhydrase Inhibitors suppress platelet procoagulant responses and in vivo thrombosis

2020· article· en· W2998409789 on OpenAlexaff
Ejaife O. Agbani, Xiaojuan Zhao, Christopher M. Williams, Riyaad Aungraheeta, Ingeborg Hers, Erik R. Swenson, Alastair W. Poole

Bibliographic record

VenuePlatelets · 2020
Typearticle
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersBritish Heart FoundationWellcome TrustWellcome
KeywordsAcetazolamidePlateletPharmacologyIn vivoChemistryCarbonic anhydrase inhibitorPlatelet activationCarbonic anhydraseThrombosisSecretionBiochemistryMedicineEnzymeInternal medicineBiology

Abstract

fetched live from OpenAlex

Carbonic anhydrase (CA) inhibitors have a long history of safe clinical use as mild diuretics, in the treatment of glaucoma and for altitude sickness prevention. In this study, we aimed to determine if CA inhibition may be an alternative approach to control thrombosis. We utilized a high-resolution dynamic imaging approach to provide mechanistic evidence that CA inhibitors may be potent anti-procoagulant agents in vitro and effective anti-thrombotics in vivo. Acetazolamide and methazolamide, while sparing platelet secretion, attenuated intracellular chloride ion entry and suppressed the procoagulant response of activated platelets in vitro and thrombosis in vivo. The chemically similar N-methyl acetazolamide, which lacks CA inhibitory activity, did not affect platelet procoagulant response in vitro. Outputs from rotational thromboelastometry did not reflect changes in procoagulant activity and reveal the need for a suitable clinical test for procoagulant activity. Drugs specifically targeting procoagulant remodeling of activated platelets, by blockade of carbonic anhydrases, may provide a new way to control platelet-driven thrombosis without blocking essential platelet secretion responses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.274
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2020
Admission routes1
Has abstractyes

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