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Record W2757938599 · doi:10.3389/fnmol.2017.00305

Neuronal Culture Microenvironments Determine Preferences in Bioenergetic Pathway Use

2017· article· en· W2757938599 on OpenAlexaff
Juliane Sünwoldt, Bert Bosche, Andreas Meisel, Philipp Mergenthaler

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersFP7 People: Marie-Curie ActionsDeutsche ForschungsgemeinschaftDeutscher Akademischer Austauschdienst
KeywordsBioenergeticsNeuroscienceBiologyCell biologyMitochondrion

Abstract

fetched live from OpenAlex

In the brain, metabolic supply and demand is directly coupled to neuronal activation. Methods for culturing primary rodent brain cells have come of age and are geared toward sophisticated modeling of human brain physiology and pathology. However, the impact of the culture microenvironment on neuronal function is rarely considered. Therefore, we investigated the role of different neuronal culture supplements for neuronal survival and metabolic activity in a model of metabolic deprivation of neurons using oxygen deprivation, glucose deprivation, as well as live cell metabolic flux analysis. We demonstrate the impact of neuronal culture conditions on metabolic function and neuronal survival under conditions of metabolic stress. In particular, we find that the common neuronal cell culture supplement B27 protects neurons from cell death under hypoxic conditions and inhibits glycolysis. Furthermore, we present data that B27 as well as the alternative neuronal culture supplement N2 restrict neuronal glucose metabolism. On the contrary, we find that the more modern supplement GS21 promotes neuronal energy metabolism. Our data support the notion that careful control of the metabolic environment is an essential component in modeling brain function and the cellular and molecular pathophysiology of brain disease in culture.

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 score0.241
Threshold uncertainty score0.720

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 science0.0010.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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