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Record W4220998348 · doi:10.3389/fimmu.2022.847246

Oxidative Stress Following Intracerebral Hemorrhage: From Molecular Mechanisms to Therapeutic Targets

2022· review· en· W4220998348 on OpenAlexaff
Yan Zhang, Suliman Khan, Yang Liu, Guofeng Wu, V. Wee Yong, Mengzhou Xue

Bibliographic record

VenueFrontiers in Immunology · 2022
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsOxidative stressIntracerebral hemorrhageMedicineMicrogliaInflammationReactive oxygen speciesGlutamate receptorBrain damageBlood–brain barrierCentral nervous systemPharmacologyImmunologyAnesthesiaInternal medicineBiologyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Intracerebral hemorrhage (ICH) is a highly fatal disease with mortality rate of approximately 50%. Oxidative stress (OS) is a prominent cause of brain injury in ICH. Important sources of reactive oxygen species after hemorrhage are mitochondria dysfunction, degradated products of erythrocytes, excitotoxic glutamate, activated microglia and infiltrated neutrophils. OS harms the central nervous system after ICH mainly through impacting inflammation, killing brain cells and exacerbating damage of the blood brain barrier. This review discusses the sources and the possible molecular mechanisms of OS in producing brain injury in ICH, and anti-OS strategies to ameliorate the devastation of ICH.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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 designNot applicable
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

Citations120
Published2022
Admission routes1
Has abstractyes

Explore more

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