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Record W2946341151 · doi:10.3791/59546

A Chronic Immobilization Stress Protocol for Inducing Depression-Like Behavior in Mice

2019· article· en· W2946341151 on OpenAlexaff
Hyeonwi Son, Ju Hwan Yang, Hyun Joon Kim, Dong‐Kun Lee

Bibliographic record

VenueJournal of Visualized Experiments · 2019
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsDepression (economics)Chronic stressCorticosteroneTail suspension testBehavioural despair testPsychologyClinical psychologyMedicineNeuroscienceInternal medicineHormoneAntidepressantHippocampus

Abstract

fetched live from OpenAlex

Depression is not yet fully understood, but various causative factors have been reported. Recently, the prevalence of depression has increased. However, therapeutic treatments for depression or research on depression is scarce. Thus, in the present paper, we propose a mouse model of depression induced by movement restriction. Chronic mild stress (CMS) is a well-known technique to induce depressive-like behavior. However, it necessitates a complex procedure consisting of a combination of various mild stresses. In contrast, chronic immobilization stress (CIS) is a readily accessible chronic stress model, modified from a restraint model that induces depressive behavior by restricting movement using a restrainer for a certain period. To evaluate the depressive-like behaviors, the sucrose preference test (SPT), the tail suspension test (TST), and the ELISA assay to measure stress marker corticosterone levels are combined in the present experiment. The described protocols illustrate the induction of CIS and evaluation of the changes in behavior and physiological factors for the validation of depression.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.061
GPT teacher head0.469
Teacher spread0.408 · 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
GenreMethods

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

Citations47
Published2019
Admission routes1
Has abstractyes

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