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Record W4283835448 · doi:10.1212/wnl.0000000000200909

Association of Thrombin Generation With Leukocyte Inflammatory Profile in Patients With Acute Ischemic Stroke

2022· article· en· W4283835448 on OpenAlexafffund
Sarina Falcione, Danielle Munsterman, Twinkle Joy, Joseph Kamtchum‐Tatuene, Gina Sykes, Glen C. Jickling

Bibliographic record

VenueNeurology · 2022
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsWomen and Children’s Health Research Institute
FundersCanadian Institutes of Health Research
KeywordsThrombinMedicineStroke (engine)Tissue factorDiscovery and development of direct thrombin inhibitorsInternal medicineImmunologyPlateletCoagulation

Abstract

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Background and Objectives Thrombosis is central to the pathogenesis of acute ischemic stroke, with higher thrombin generation being associated with increased stroke risk. The immune system may contribute to thrombin generation in stroke and thus may offer novel strategies for stroke prevention. This study addresses the research question regarding the relationship of thrombin generation to leukocyte gene expression in patients with acute ischemic stroke. Methods We isolated RNA from whole blood and examined the relationship to thrombin generation capacity in patients with acute ischemic stroke. Due to its effects on thrombin generation, patients on anticoagulants were excluded from the study. The relationship of gene expression with peak thrombin was evaluated by analysis of covariance across peak thrombin quartiles adjusted for sex and age. Results In 97 patients with acute ischemic stroke, peak thrombin was variable, ranging from 252.0 to 752.4 nM. Increased peak thrombin was associated with differences in thromboinflammatory leukocyte gene expression, including a decrease in ADAM metallopeptidase with thrombospondin type 1 motif 13 and an increase in nuclear factor κB (NF-κB)–activating protein, protein disulfide isomerase family A member 5, and tissue factor pathway inhibitor 2. Pathways associated with peak thrombin included interleukin 6 signaling, thrombin signaling, and NF-κB signaling. A linear discriminant analysis model summarizing the immune activation associated with peak thrombin in a first cohort of stroke could distinguish patients with low peak thrombin from high peak thrombin in a second cohort of 112 patients with acute ischemic stroke. Discussion The identified genes and pathways support a role of the immune system contributing to thrombus formation in patients with stroke. These may have relevance to antithrombotic strategies for stroke prevention. ADAMTS13 = : ADAM metallopeptidase with thrombospondin type 1 motif 13; FVIIa= : coagulation factor VIIa; FX= : coagulation factor X; IL= : interleukin; LDA= : linear discriminant analysis; NF-κB= : nuclear factor κB; NKAP = : NF-κB–activating protein; PDI= : protein disulfide isomerase; PDIA5 = : protein disulfide isomerase family A member 5; TFPI2 = : tissue factor pathway inhibitor 2

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2022
Admission routes2
Has abstractyes

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