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Record W4312201710 · doi:10.3390/biom12121872

Cerebral Ischemic Preconditioning Aggravates Death of Oligodendrocytes

2022· article· en· W4312201710 on OpenAlexafffund
Teng Guan, Ying Guo, Chengren Li, Ting Zhou, Qiang Yu, Chaoxian Yang, Guohui Zhang, Jiming Kong

Bibliographic record

VenueBiomolecules · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsUniversity of Manitoba
FundersFondation Brain CanadaALS Society of Canada
KeywordsIschemic preconditioningIschemiaNeurodegenerationNeurosciencePharmacologyOligodendrocyteMedicineChemistryCell biologyBiologyMyelinCardiologyCentral nervous systemInternal medicine

Abstract

fetched live from OpenAlex

Neurodegeneration can benefit from ischemic preconditioning, a natural adaptive reaction to sublethal noxious stimuli. Although there is growing interest in advancing preconditioning to preserve brain function, preconditioning is not yet considered readily achievable in clinical settings. One of the most challenging issues is that there is no fine line between preconditioning stimuli and lethal stimuli. Here, we show deleterious effect of preconditioning on oligodendrocyte precursor cells (OPCs). We identified Bcl-2/adenovirus E1B 19-kDa interacting protein 3 (BNIP3), a mitochondrial BH3-only protein specifically involved in OPCs loss after preconditioning. Repeated ischemia stabilized BNIP3 and increased the vulnerability of OPCs to subsequent ischemic events. BNIP3 became mitochondrial-bound and was concurrent with the dysfunction of monocarboxylate transporter 1 (MCT1). Inhibition of BNIP3 by RNAi or necrostatin-1 (Nec-1) and knocking out of BNIP3 almost completely prevented OPCs loss and preserved white matter integrity. Together, our results suggest that the unfavorable effect of BNIP3 on OPCs should be noted for safe development of ischemic tolerance. BNIP3 inhibition appears to be a complementary approach to improve the efficacy of preconditioning for ischemic stroke.

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 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 score0.015
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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