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Record W4213083445 · doi:10.3389/fncel.2022.799753

Modes of Brain Cell Death Following Intracerebral Hemorrhage

2022· review· en· W4213083445 on OpenAlex
Yan Zhang, Suliman Khan, Yang Liu, Ruiyi Zhang, Hongmin Li, Guofeng Wu, Zhouping Tang, Mengzhou Xue, V. Wee Yong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2022
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchInnovation Scientists and Technicians Troop Construction Projects of Henan ProvinceNational Natural Science Foundation of China
KeywordsNecroptosisPyroptosisProgrammed cell deathIntracerebral hemorrhageMedicineAutophagyStroke (engine)NeuroscienceApoptosisCause of deathNecrosisPathologyInternal medicineBiologyDiseaseSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Intracerebral hemorrhage (ICH) is a devastating form of stroke with high rates of mortality and morbidity. It induces cell death that is responsible for neurological deficits postinjury. There are no therapies that effectively mitigate cell death to treat ICH. This review aims to summarize our knowledge of ICH-induced cell death with a focus on apoptosis and necrosis. We also discuss the involvement of ICH in recently described modes of cell death including necroptosis, pyroptosis, ferroptosis, autophagy, and parthanatos. We summarize treatment strategies to mitigate brain injury based on particular cell death pathways after 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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.058
GPT teacher head0.326
Teacher spread0.268 · 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