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Record W2897909741 · doi:10.1002/ana.25356

N‐acetylcysteine targets 5 lipoxygenase‐derived, toxic lipids and can synergize with prostaglandin E <sub>2</sub> to inhibit ferroptosis and improve outcomes following hemorrhagic stroke in mice

2018· article· en· W2897909741 on OpenAlexafffund
Saravanan S. Karuppagounder, Lauren Alin, Yingxin Chen, David Brand, Megan W. Bourassa, Kristen Dietrich, Cassandra M. Wilkinson, Colby A. Nadeau, Amit Kumar, Steve Perry, John T. Pinto, Victor Darley‐Usmar, Stephanie Sanchez, Ginger L. Milne, Domenico Praticò, Theodore R. Holman, S. Thomas Carmichael, Giovanni Coppola, Frederick Colbourne, Rajiv R. Ratan

Bibliographic record

VenueAnnals of Neurology · 2018
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of AlbertaWomen and Children’s Health Research Institute
FundersNational Institute on AgingBurke FoundationNational Institutes of HealthVanderbilt UniversityDr. Miriam and Sheldon G. Adelson Medical Research FoundationHeart and Stroke Foundation of Canada
KeywordsAcetylcysteineLipoxygenaseStroke (engine)PharmacologyEicosanoidProstaglandinChemistryProstaglandin E2MedicineEnzymeArachidonic acidInternal medicineBiochemistryAntioxidant

Abstract

fetched live from OpenAlex

Objectives N‐acetylcysteine (NAC) is a clinically approved thiol‐containing redox modulatory compound currently in trials for many neurological and psychiatric disorders. Although generically labeled as an “antioxidant,” poor understanding of its site(s) of action is a barrier to its use in neurological practice. Here, we examined the efficacy and mechanism of action of NAC in rodent models of hemorrhagic stroke. Methods Hemin was used to model ferroptosis and hemorrhagic stroke in cultured neurons. Striatal infusion of collagenase was used to model intracerebral hemorrhage (ICH) in mice and rats. Chemical biology, targeted lipidomics, arachidonate 5‐lipoxygenase (ALOX5) knockout mice, and viral‐gene transfer were used to gain insight into the pharmacological targets and mechanism of action of NAC. Results NAC prevented hemin‐induced ferroptosis by neutralizing toxic lipids generated by arachidonate‐dependent ALOX5 activity. NAC efficacy required increases in glutathione and is correlated with suppression of reactive lipids by glutathione‐dependent enzymes such as glutathione S ‐transferase. Accordingly, its protective effects were mimicked by chemical or molecular lipid peroxidation inhibitors. NAC delivered postinjury reduced neuronal death and improved functional recovery at least 7 days following ICH in mice and can synergize with clinically approved prostaglandin E 2 (PGE 2 ). Interpretation NAC is a promising, protective therapy for ICH, which acted to inhibit toxic arachidonic acid products of nuclear ALOX5 that synergized with exogenously delivered protective PGE 2 in vitro and in vivo. The findings provide novel insight into a target for NAC, beyond the generic characterization as an antioxidant, resulting in neuroprotection and offer a feasible combinatorial strategy to optimize efficacy and safety in dosing of NAC for treatment of neurological disorders involving ferroptosis such as ICH. Ann Neurol 2018;84:854–872

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.269
Teacher spread0.250 · 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 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

Citations294
Published2018
Admission routes2
Has abstractyes

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