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Record W2309000138 · doi:10.1002/jnr.23733

Autophagy inhibition in endogenous and nutrient‐deprived conditions reduces dorsal root ganglia neuron survival and neurite growth in vitro

2016· article· en· W2309000138 on OpenAlexafffund
Joseph-Patrick W. E. Clarke, Karen M. Mearow

Bibliographic record

VenueJournal of Neuroscience Research · 2016
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutophagyNeuriteCell biologyDorsal root ganglionBiologyNeuronProgrammed cell deathNeuroscienceIn vitroBiochemistryApoptosisSpinal cord

Abstract

fetched live from OpenAlex

Peripheral neuropathies can result in cytoskeletal changes in axons, ultimately leading to Wallerian degeneration and cell death. Recently, autophagy has been studied as a potential target for improving axonal survival and growth during peripheral nerve damage. This study investigates the influence of autophagy on adult dorsal root ganglia (DRG) neuron survival and axonal growth under control and nutrient deprivation conditions. Constitutive autophagy was modulated with pharmacological activators (rapamycin; Rapa) and inhibitors (3-methyladenine, bafilomycin A1) in conjunction with either a nutrient-stable environment (standard culture medium) or a nutrient-deprived environment (Hank's balanced salt solution + Ca(2+) /Mg(2+) ). The results demonstrated that autophagy inhibition decreased cell viability and reduced neurite growth and branching complexity. Although autophagy was upregulated with nutrient deprivation compared with the control, it was not further activated by rapamycin, suggesting a threshold level of autophagy. Overall, both cellular and biochemical approaches combined to show the influence of autophagy on adult DRG neuron survival and growth. © 2016 Wiley Periodicals, Inc.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.079
GPT teacher head0.366
Teacher spread0.288 · 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

Citations28
Published2016
Admission routes2
Has abstractyes

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