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Record W2281177932 · doi:10.4103/1673-5374.165519

Cerebrospinal fluid from rats given hypoxic preconditioning protects neurons from oxygen-glucose deprivation-induced injury

2015· article· en· W2281177932 on OpenAlexaff
Yanbo Zhang, Zhengdong Guo, Meiyi Li, Sijie Li, Mingfeng Yang, Xunming Ji, LV Guo-wei

Bibliographic record

VenueNeural Regeneration Research · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsCerebrospinal fluidHypoxia (environmental)EndogenyDownregulation and upregulationMedicineApoptosisAnesthesiaEndocrinologyInternal medicineOxygenChemistryBiochemistryGene

Abstract

fetched live from OpenAlex

Hypoxic preconditioning activates endogenous mechanisms that protect against cerebral ischemic and hypoxic injury. To better understand these protective mechanisms, adult rats were housed in a hypoxic environment (8% O2/92% N2) for 3 hours, and then in a normal oxygen environment for 12 hours. Their cerebrospinal fluid was obtained to culture cortical neurons from newborn rats for 1 day, and then the neurons were exposed to oxygen-glucose deprivation for 1.5 hours. The cerebrospinal fluid from rats subjected to hypoxic preconditioning reduced oxygen-glucose deprivation-induced injury, increased survival rate, upregulated Bcl-2 expression and downregulated Bax expression in the cultured cortical neurons, compared with control. These results indicate that cerebrospinal fluid from rats given hypoxic preconditioning protects against oxygen-glucose deprivation-induced injury by affecting apoptosis-related protein expression in neurons from newborn rats.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.194
GPT teacher head0.364
Teacher spread0.170 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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