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Record W2092805551 · doi:10.1177/2167702612455743

Targeted Rejection Triggers Differential Pro- and Anti-Inflammatory Gene Expression in Adolescents as a Function of Social Status

2012· article· en· W2092805551 on OpenAlexaff
Michael Murphy, George M. Slavich, Nicolas Rohleder, Gregory E. Miller

Bibliographic record

VenueClinical Psychological Science · 2012
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsInflammationGene expressionPsychologyDepression (economics)Messenger RNASignal transductionSocial defeatImmunologyGeneMedicineBiologyGeneticsNeuroscience

Abstract

fetched live from OpenAlex

Social difficulties during adolescence influence life-span health. To elucidate underlying mechanisms, we examined whether a noxious social event, targeted rejection (TR), influences the signaling pathways that regulate inflammation, which is implicated in a number of health problems. For this study, 147 adolescent women at risk for developing a first episode of major depression were interviewed every 6 months for 2.5 years to assess recent TR exposure, and blood was drawn to quantify leukocyte messenger RNA (mRNA) for nuclear factor-κB (NF-κB) and inhibitor of κB (I-κB) and the inflammatory biomarkers C-reactive protein and interleukin-6. Participants had more NF-κB and I-κB mRNA at visits when TR had occurred. These shifts in inflammatory signaling were most pronounced for adolescents high in perceived social status. These findings demonstrate that social rejection upregulates inflammatory gene expression in youth at risk for depression, particularly for those high in status. If sustained, this heightened inflammatory signaling could have implications for life-span health.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.076
GPT teacher head0.392
Teacher spread0.316 · 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 designObservational
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

Citations90
Published2012
Admission routes1
Has abstractyes

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