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Record W4409124417 · doi:10.1101/2025.03.28.646062

Neutrophil extracellular traps block endogenous and intravenous thrombolysis-induced fibrinolysis in large vessel occlusion acute ischemic stroke

2025· preprint· en· W4409124417 on OpenAlexaff
Jean‐Philippe Désilles, Mialitiana Solo Nomenjanahary, Lucas Di Meglio, Fatima Zemali, Sara Zalghout, Stéphane Loyau, Julien Labreuche, Marie‐Charlotte Bourrienne, Dorothée Faille, François Delvoye, Véronique Ollivier, Sébastien Dupont, Jasmina Rogozarski, Nahida Brikci-Nigassa, Nadine Ajzenberg, Mikaël Mazighi, Benoît Ho‐Tin‐Noé

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsOntario Stroke Network
Fundersnot available
KeywordsThrombolysisFibrinolysisMedicineCardiologyOcclusionInternal medicineAnesthesiaIschemiaStroke (engine)Neutrophil extracellular trapsBlock (permutation group theory)InflammationMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Background Intravenous thrombolysis (IVT) failure in acute ischemic stroke (AIS) due to large vessel occlusion (LVO) is frequent but its causes remain elusive. Several non-exclusive mechanisms have been proposed to explain IVT failure, including failed delivery of tPA and inhibition of its activity. We investigated whether biologically relevant intrathrombus concentrations of t-PA were achieved in failed IVT in patients with LVO AIS, and whether neutrophil extracellular traps (NETs) contributed to IVT failure. Methods In this cohort study, a total of 205 thrombi from AIS patients with LVO were analyzed. 83 of these thrombi were compared for tPA content and 53 for their susceptibility to ex vivo thrombolysis according to IVT status. An additional subset of 69 AIS thrombi was used to decipher if and how NETs interfere with intrathrombus fibrinolysis. Results AIS thrombi from IVT patients contained more tPA than those from no-IVT patients (0.209 vs 0.093 µg/mg of thrombus, p<0.0001). Plasminogen and tPA in AIS thrombi were found in association with fibrin and NETs. The ability of NETs to bind tPA and plasminogen, titrating them away from fibrin, was confirmed in a microfluidic model of thrombosis. While ex vivo addition of plasminogen did not cause lysis of either no-IVT or IVT thrombi, combining plasminogen with DNase 1 helped translate the increased tPA content of IVT thrombi into increased thrombolysis. We further show that DNase 1 enables tPA- and plasmin-mediated thrombolysis by eliminating fibrinolysis inhibitors from AIS thrombi. Conclusions These results indicate that intrathrombus tPA concentrations reached in failed IVT bear a therapeutic potential that is however impaired by NETs, which favor intrathrombus retention of fibrinolysis inhibitors and compete with fibrin for tPA and plasminogen binding. Our results stress the interest of DNase 1 to enhance the efficacy of current IVT tPA regimens. Clinical Perspective What is new? Intravenous thrombolysis increases thrombus tPA content even when it fails to cause arterial recanalization in acute ischemic stroke The fibrinolytic activity of intravenously-administered tPA is blocked by neutrophil extracellular traps in acute ischemic stroke thrombi Neutrophil extracellular traps participate in thrombolysis resistance by retaining fibrinolysis inhibitors and titrating tPA and plasminogen away from fibrin in acute ischemic stroke thrombi DNase 1 can convert increased tPA content into increased fibrinolysis by eliminating NETs-associated fibrinolysis inhibitors in acute ischemic stroke thrombi 2) What are the clinical implications? Despite therapeutic failure, biologically significant intrathrombus tPA concentrations are achieved following intravenous thrombolysis at current tPA regimens Sequential administration of DNase 1 prior to intravenous thrombolysis could clear the way for tPA and potentiate its fibrinolytic activity for improved arterial recanalization efficacy

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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