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Record W4232791285 · doi:10.22215/etd/2015-10820

An Assessment of the Antidepressant-like Properties of Erythropoietin in an Animal Model of Depression

2015· dissertation· en· W4232791285 on OpenAlexaff
Kyla Vanderzwet

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsCarleton University
Fundersnot available
KeywordsErythropoietinAnhedoniaStressorAntidepressantAmygdalaNeurotrophic factorsPrefrontal cortexNeuroplasticityBrain-derived neurotrophic factorInternal medicinePsychologySocial defeatAnimal models of depressionEndocrinologyDepression (economics)MedicineNeuroscienceHippocampusReceptorCognition

Abstract

fetched live from OpenAlex

Impaired neuroplasticity and altered connectivity between different brain regions may be key features of depression.As the cytokine erythropoietin (EPO) increases brain derived neurotrophic factor (BDNF) levels, EPO may enhance neuron survival and growth, leading to antidepressant effects.Here, EPO was tested for its ability to reverse depressive-like symptoms in rats following three weeks of chronic mild stressors.Neither stressor exposure nor EPO affected sucrose preference, which has been used to model the anhedonia characteristic of depression.Additionally, EPO did not reverse stressor-induced social avoidance.BDNF mRNA expression was reduced by stressor and EPO treatments in the prefrontal cortex, and elevated in the amygdala with EPO treatment.Additionally, FGF-2 expression was reduced in the amygdala with stressor exposure, but normalized following EPO treatment.Many of the results are inconsistent with a priori hypotheses, possibly owing to factors such as the timing of tissue collection and a lack of environmental enrichment.

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 score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.065
GPT teacher head0.368
Teacher spread0.303 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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