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Record W3036296928 · doi:10.1101/2020.06.18.159889

A synthetic peptide rescues rat cortical neurons from anesthetic-induced cell death and modulation of growth and synaptic assembly

2020· preprint· en· W3036296928 on OpenAlexafffund

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCortical neuronsNeuriteProgrammed cell deathSynapseCerebral cortexPeptideCortex (anatomy)Neurodegeneration

Abstract

fetched live from OpenAlex

Abstract Anesthetics, although necessary for many modern procedures, exert neurotoxic effects in various experimental models; the underlying mechanisms, however, remain unknown. In the absence of this information, any in vivo mitigation strategy would be challenging. To help address these gaps, we sought to determine whether preserving mitochondrial network integrity with a non-toxic, short-life synthetic peptide, P110, could protect cortical neurons against both inhalational and intravenous anesthetic-induced neurotoxicity. This study provides the first direct comparative account of three key general anesthetics (desflurane, propofol, and ketamine) under identical conditions and demonstrates their impact on cellular viability, neurite outgrowth, and synaptic network assembly on neonatal rat cortical neurons. Further, we discovered that the inhibition of Fis1 receptor-mediated fission reverses anesthetic-induced aberrations in an agent-specific manner. Our data thus underscore the importance of a reductionist approach, offering unique opportunities to explore non-toxic, mitochondria-based therapeutic tools for mitigating anesthetic-induced harm to the developing brain.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.251
Teacher spread0.211 · 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.

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

Citations0
Published2020
Admission routes2
Has abstractyes

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